Advanced Engine Diagnostics PDF

This book describes the discusses advanced fuels and combustion, emission control techniques, after-treatment systems, simulations and fault diagnostics, including discussions on different engine diagnostic techniques such as particle image velocimetry (PIV), phase Doppler interferometry (PDI), laser ignition. This volume bridges the gap between basic concepts and advanced research in internal combustion engine diagnostics, making it a useful reference for both students and researchers whose work focuses on achieving higher fuel efficiency and lowering emissions.

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Energy, Environment, and Sustainability Series Editors: Avinash Kumar Agarwal · Ashok Pandey

Avinash Kumar Agarwal Jai Gopal Gupta Nikhil Sharma Akhilendra Pratap Singh Editors

Advanced Engine Diagnostics

Energy, Environment, and Sustainability Series editors Avinash Kumar Agarwal, Department of Mechanical Engineering, Indian Institute of Technology Kanpur, Kanpur, Uttar Pradesh, India Ashok Pandey, Distinguished Scientist, CSIR-Indian Institute of Toxicology Research, Lucknow, Uttar Pradesh, India

This books series publishes cutting edge monographs and professional books focused on all aspects of energy and environmental sustainability, especially as it relates to energy concerns. The Series is published in partnership with the International Society for Energy, Environment, and Sustainability. The books in these series are editor or authored by top researchers and professional across the globe. The series aims at publishing state-of-the-art research and development in areas including, but not limited to: • • • • • • • • • •

Renewable Energy Alternative Fuels Engines and Locomotives Combustion and Propulsion Fossil Fuels Carbon Capture Control and Automation for Energy Environmental Pollution Waste Management Transportation Sustainability

More information about this series at http://www.springer.com/series/15901

Avinash Kumar Agarwal Jai Gopal Gupta Nikhil Sharma Akhilendra Pratap Singh •

Editors

Advanced Engine Diagnostics

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Editors Avinash Kumar Agarwal Department of Mechanical Engineering Indian Institute of Technology Kanpur Kanpur, Uttar Pradesh, India

Nikhil Sharma Department of Mechanical Engineering Indian Institute of Technology Kanpur Kanpur, Uttar Pradesh, India

Jai Gopal Gupta Department of Mechanical Engineering Government Women Engineering College Ajmer, Rajasthan, India

Akhilendra Pratap Singh Department of Mechanical Engineering University of Wisconsin-Madison Madison, WI, USA

ISSN 2522-8366 ISSN 2522-8374 (electronic) Energy, Environment, and Sustainability ISBN 978-981-13-3274-6 ISBN 978-981-13-3275-3 (eBook) https://doi.org/10.1007/978-981-13-3275-3 Library of Congress Control Number: 2018962990 © Springer Nature Singapore Pte Ltd. 2019 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, speciﬁcally the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microﬁlms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a speciﬁc statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional afﬁliations. This Springer imprint is published by the registered company Springer Nature Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore

Preface

Energy demand has been rising remarkably due to increasing population and urbanization. Global economy and society are signiﬁcantly dependent on the energy availability because it touches every facet of human life and its activities. Transportation and power generation are two major examples. Without the transportation by millions of personalized and mass transport vehicles and availability of 24 7 power, human civilization would not have reached contemporary living standards. The International Society for Energy, Environment and Sustainability (ISEES) was founded at Indian Institute of Technology Kanpur (IIT Kanpur), India, in January 2014 with the aim of spreading knowledge/awareness and catalyzing research activities in the ﬁelds of energy, environment, sustainability, and combustion. The society’s goal is to contribute to the development of clean, affordable, and secure energy resources and a sustainable environment for the society and to spread knowledge in the above-mentioned areas and create awareness about the environmental challenges, which the world is facing today. The unique way adopted by the society was to break the conventional silos of specializations (engineering, science, environment, agriculture, biotechnology, materials, fuels, etc.) to tackle the problems related to energy, environment, and sustainability in a holistic manner. This is quite evident by the participation of experts from all ﬁelds to resolve these issues. ISEES is involved in various activities such as conducting workshops, seminars, and conferences in the domains of its interest. The society also recognizes the outstanding works done by the young scientists and engineers for their contributions in these ﬁelds by conferring them awards under various categories. The second international conference on “Sustainable Energy and Environmental Challenges” (SEEC-2018) was organized under the auspices of ISEES from December 31, 2017, to January 3, 2018, at J N Tata Auditorium, Indian Institute of Science, Bangalore. This conference provided a platform for discussions between eminent scientists and engineers from various countries including India, USA, South Korea, Norway, Finland, Malaysia, Austria, Saudi Arabia, and Australia. In this conference, eminent speakers from all over the world presented their views v

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related to different aspects of energy, combustion, emissions, and alternative energy resources for sustainable development and a cleaner environment. The conference presented ﬁve high-voltage plenary talks from globally renowned experts on topical themes, namely “Is It Really the End of Combustion Engines and Petroleum?” by Prof. Gautam kalghatgi, Saudi Aramco; “Energy Sustainability in India: Challenges and Opportunities” by Prof. Baldev Raj, NIAS Bangalore; “Methanol Economy: An Option for Sustainable Energy and Environmental Challenges” by Dr. Vijay Kumar Saraswat, Hon. Member (S&T), NITI Aayog, Government of India; “Supercritical Carbon Dioxide Brayton Cycle for Power Generation” by Prof. Pradip Dutta, IISc Bangalore; and “Role of Nuclear Fusion for Environmental Sustainability of Energy in Future” by Prof. J. S. Rao, Altair Engineering. The conference included 27 technical sessions on topics related to energy and environmental sustainability including 5 plenary talks, 40 keynote talks, and 18 invited talks from prominent scientists, in addition to 142 contributed talks, and 74 poster presentations by students and researchers. The technical sessions in the conference included Advances in IC Engines: SI Engines, Solar Energy: Storage, Fundamentals of Combustion, Environmental Protection and Sustainability, Environmental Biotechnology, Coal and Biomass Combustion/Gasiﬁcation, Air Pollution and Control, Biomass to Fuels/Chemicals: Clean Fuels, Advances in IC Engines: CI Engines, Solar Energy: Performance, Biomass to Fuels/Chemicals: Production, Advances in IC Engines: Fuels, Energy Sustainability, Environmental Biotechnology, Atomization and Sprays, Combustion/Gas Turbines/Fluid Flow/ Sprays, Biomass to Fuels/Chemicals, Advances in IC Engines: New Concepts, Energy Sustainability, Waste to Wealth, Conventional and Alternate Fuels, Solar Energy, Wastewater Remediation, and Air Pollution. One of the highlights of the conference was the rapid-ﬁre poster sessions in (i) Energy Engineering, (ii) Environment and Sustainability, and (iii) Biotechnology, where more than 75 students participated with great enthusiasm and won many prizes in a ﬁercely competitive environment. More than 200 participants and speakers attended this four-day conference, which also hosted Dr. Vijay Kumar Saraswat, Hon. Member (S&T), NITI Aayog, Government of India, as the chief guest for the book release ceremony, where 16 ISEES books published by Springer under a special dedicated series “Energy, Environment, and Sustainability” were released. This is the ﬁrst time that such signiﬁcant and high-quality outcome has been achieved by any society in India. The conference concluded with a panel discussion on “Challenges, Opportunities & Directions for Future Transportation Systems”, where the panelists were Prof. Gautam Kalghatgi, Saudi Aramco; Dr. Ravi Prashanth, Caterpillar Inc.; Dr. Shankar Venugopal, Mahindra and Mahindra; Dr. Bharat Bhargava, DG, ONGC Energy Center, and Dr. Umamaheshwar, GE Transportation, Bangalore. The panel discussion was moderated by Prof. Ashok Pandey, Chairman, ISEES. This conference laid out the road map for technology development, opportunities, and challenges in energy, environment, and sustainability domains. All these topics are very relevant for the country and the world in the present context. We acknowledge the support received from various funding agencies and organizations for successfully conducting the second ISEES conference SEEC-2018, where these

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books germinated. We would therefore like to acknowledge SERB, Government of India (special thanks to Dr. Rajeev Sharma, Secretary); ONGC Energy Center (special thanks to Dr. Bharat Bhargava), TAFE (special thanks to Sh. Anadrao Patil); Caterpillar (special thanks to Dr. Ravi Prashanth); Progress Rail, TSI, India (special thanks to Dr. Deepak Sharma); Tesscorn, India (special thanks to Sh. Satyanarayana); GAIL, VOLVO; and our publishing partner Springer (special thanks to Swati Meherishi). The editors would like to express their sincere gratitude to a large number of authors from all over the world for submitting their high-quality work in a timely manner and revising it appropriately at short notice. We would like to express our special thanks to Dr. Atul Dhar and Dr. Pravesh Chandra Shukla, who reviewed various chapters of this monograph and provided their valuable suggestions to improve the manuscripts. Currently, IC engines are facing many challenges related to fuel supply, energy efﬁciency, and emissions, which require serious research efforts. This monograph is based on such advanced engine diagnostics such as advanced combustion strategies, combustion simulations, engine fault diagnostics techniques, tribological investigations, emission control systems, and the use of new alternative fuels in IC engines. These topics are the main areas of research in the ﬁeld of IC engine. Therefore, this monograph is intended for practitioners working in the automotive sector and provides them new directions for future research. We hope that the book would be of great interest to the professionals and postgraduate students involved in fuel, IC engine, engine instrumentation, and environmental research. Kanpur, India Ajmer, India Kanpur, India Madison, USA

Avinash Kumar Agarwal Jai Gopal Gupta Nikhil Sharma Akhilendra Pratap Singh

Contents

Part I 1

Introduction to Advanced Engine Diagnostics . . . . . . . . . . . . . . . . . Avinash Kumar Agarwal, Jai Gopal Gupta, Nikhil Sharma and Akhilendra Pratap Singh

Part II 2

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Advanced Fuels and Combustion Techniques

Reactivity-Controlled Compression Ignition Combustion Using Alcohols . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Akhilendra Pratap Singh, Nikhil Sharma, Dev Prakash Satsangi, Vikram Kumar and Avinash Kumar Agarwal

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Effect of Hydrogen and Producer Gas Addition on the Performance and Emissions on a Dual-Fuel Diesel Engine . . . . . . . Abhishek Priyam, Prabha Chand and D. B. Lata

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Characteristics of Particulates Emitted by IC Engines Using Advanced Combustion Strategies . . . . . . . . . . . . . . . . . . . . . . . . . . Akhilendra Pratap Singh and Avinash Kumar Agarwal

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Part III 5

General

Emission Control Techniques and After-Treatment Systems

Modelling and Experimental Studies of NOx and Soot Emissions in Common Rail Direct Injection Diesel Engines . . . . . . . . . . . . . . J. Thangaraja and S. Rajkumar

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On-Board Post-Combustion Emission Control Strategies for Diesel Engine in India to Meet Bharat Stage VI Norms . . . . . . 105 Rabinder Singh Bharj, Rajan Kumar and Gurkamal Nain Singh

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Non-Noble Metal-Based Catalysts for the Application of Soot Oxidation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127 Pravesh Chandra Shukla

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Ceria-based Mixed Oxide Nanoparticles for Diesel Engine Emission Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143 P. K. Shihabudeen, Ajin C. Sajeevan, N. Sandhyarani and V. Sajith

Part IV 9

Simulations and Fault Diagnostics

Model-Based Fault Detection on Modern Automotive Engines . . . . 167 Deepak Agarwal and Chandan Kumar Singh

10 Study of Instability Nature of Circular Liquid Jet at Critical Chamber Conditions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 205 Dhanesh Ayyappan, Aravind Vaidyanathan, C. K. Muthukumaran and K. Nandakumar 11 Transient Reacting Flow Simulations of Chemical-Looping Combustion Reactors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 219 Guanglei Ma, Subhodeep Banerjee and Ramesh K. Agarwal 12 Tribological Studies of an Internal Combustion Engine . . . . . . . . . 237 Vikram Kumar, Sujeet Kumar Sinha and Avinash Kumar Agarwal

Editors and Contributors

About the Editors Avinash Kumar Agarwal is a professor in the Department of Mechanical Engineering in Indian Institute of Technology Kanpur. His areas of interest are IC engines, combustion, alternative fuels, conventional fuels, optical diagnostics, laser ignition, HCCI, emission and particulate control, and large bore engines. He has published 24 books and more than 230 international journal and conference papers. He is a fellow of SAE (2012), ASME (2013), ISEES (2015), and INAE (2015). He received several awards such as prestigious Shanti Swarup Bhatnagar Award-2016 in engineering sciences, Rajib Goyal Prize-2015, NASI-Reliance Industries Platinum Jubilee Award-2012; INAE Silver Jubilee Young Engineer Award-2012; SAE International’s Ralph R. Teetor Educational Award-2008; INSA Young Scientist Award-2007; UICT Young Scientist Award2007; INAE Young Engineer Award-2005.

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Jai Gopal Gupta is a faculty member in the Government Women Engineering College, Ajmer, India. He has done his Ph.D. in the Department of Mechanical Engineering from IIT Kanpur, and his research interests include performance, emission, and combustion analysis in internal combustion engines, alternative fuels, and renewable energy resources. He has worked with the Combustion Engine and Energy Conversion (CEnEC) Laboratory, Hanyang University, South Korea, under the Indo-Korean Research Internship (IKRI) program. He has edited a book and authored 2 chapters and 13 research articles. Nikhil Sharma is a scientist in the Engine Research Laboratory at IIT Kanpur, India. He received his M.Tech. in mechanical engineering from NIT Hamirpur, India, in 2012, and his Ph.D. from IIT Kanpur, in 2017. He was an assistant professor in the Department of Mechanical and Automation Engineering at the Amity University, Noida. His areas of research include alternative fuels for IC engines (biodiesel, alcohols), emission control, and particulate characterization.

Akhilendra Pratap Singh is a Postdoctoral Fellow at University of Wisconsin-Madison, USA. He received his master’s and Ph.D. in mechanical engineering from Indian Institute of Technology Kanpur, India, in 2010 and 2016, respectively. His areas of research include advanced low-temperature combustion; optical diagnostics with special reference to engine endoscopy and PIV; combustion diagnostics; engine emission measurement; particulate characterization and their control; and alternative fuels. He has edited 5 books and authored 17 chapters and 34 research articles in journals and conferences. He is a member of numerous professional societies, including SAE, ASME, and ISEES. He is a member of the editorial board of the Journal of Energy, Environment and Sustainability.

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Contributors Avinash Kumar Agarwal Department of Mechanical Engineering, Indian Institute of Technology Kanpur, Kanpur, Uttar Pradesh, India Deepak Agarwal International Center for Automotive Technology, Gurugram, Manesar, India; EbyT Technology Private Limited, New Delhi, Delhi, India Ramesh K. Agarwal Washington University in St. Louis, St. Louis, MO, USA Dhanesh Ayyappan Indian Institute of Space Science and Technology, Trivandrum, India Subhodeep Banerjee Washington University in St. Louis, St. Louis, MO, USA Rabinder Singh Bharj Department of Mechanical Engineering, Dr. B. R. Ambedkar National Institute of Technology Jalandhar, Jalandhar, Punjab, India Prabha Chand N.I.T. Jamshedpur, Jamshedpur, Jharkhand, India Guanglei Ma Washington University in St. Louis, St. Louis, MO, USA Jai Gopal Gupta Government Women Engineering College, Ajmer, India Rajan Kumar Department of Mechanical Engineering, Dr. B. R. Ambedkar National Institute of Technology Jalandhar, Jalandhar, Punjab, India Vikram Kumar Department of Mechanical Engineering, Indian Institute of Technology Kanpur, Kanpur, Uttar Pradesh, India D. B. Lata Central University of Jharkhand, Ranchi, Jharkhand, India C. K. Muthukumaran Liquid Propulsion Systems Center, ISRO, Trivandrum, India K. Nandakumar Liquid Propulsion Systems Center, ISRO, Trivandrum, India Abhishek Priyam MPSTME, NMIMS University, Mumbai, India S. Rajkumar Department of Mechanical Engineering, SSN College of Engineering, Chennai, India Ajin C. Sajeevan School of Nano Science and Technology, National Institute of Technology Calicut, Kozhikode, Kerala, India V. Sajith School of Nano Science and Technology, National Institute of Technology Calicut, Kozhikode, Kerala, India N. Sandhyarani School of Nano Science and Technology, National Institute of Technology Calicut, Kozhikode, Kerala, India Dev Prakash Satsangi Department of Mechanical Engineering, Indian Institute of Technology Kanpur, Kanpur, Uttar Pradesh, India

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Nikhil Sharma Department of Mechanical Engineering, Indian Institute of Technology Kanpur, Kanpur, Uttar Pradesh, India P. K. Shihabudeen School of Nano Science and Technology, National Institute of Technology Calicut, Kozhikode, Kerala, India Pravesh Chandra Shukla Department of Mechanical Engineering, Indian Institute of Technology Bhilai, Sejbahar, Raipur, Chhattisgarh, India Akhilendra Pratap Singh Department of Mechanical Engineering, University of Wisconsin-Madison, Madison, WI, USA Chandan Kumar Singh AVL, Gothenburg, Sweden Gurkamal Nain Singh Department of Mechanical Engineering, Dr. B. R. Ambedkar National Institute of Technology Jalandhar, Jalandhar, Punjab, India Sujeet Kumar Sinha Department of Mechanical Engineering, Indian Institute of Technology, Delhi, India J. Thangaraja Department of Mechanical Engineering, Vellore Institute of Technology, Vellore, India Aravind Vaidyanathan Indian Institute of Space Science and Technology, Trivandrum, India

Part I

General

Chapter 1

Introduction to Advanced Engine Diagnostics Avinash Kumar Agarwal, Jai Gopal Gupta, Nikhil Sharma and Akhilendra Pratap Singh

Abstract In last two decades, advancements in automotive engines and after-treatment technologies have resulted in better engine performance, lower fuel consumption, and lower emissions; however, system complexity and higher number of control parameters have led to optimization issues. Implementation of advanced combustion diagnostic techniques/strategies in internal combustion (IC) engines further reduced the tail-pipe emissions, especially oxides of nitrogen (NOx) and particulates; however, these combustion strategies have generated a new set of control parameters, which need to be optimized for superior engine performance and emission characteristics. To resolve these complex issues, researchers have combined different techniques such as advanced combustion strategies with after-treatment systems, and experimental research supported by simulations using computational fluid dynamics (CFD) tools. This monograph covers all these topics including advanced fuels and combustion, emission control techniques, after-treatment systems, simulations, and fault diagnostics.

Keywords Advanced combustion techniques After-treatment systems Emission control Simulations Fault diagnostics

Internal combustion (IC) engines transform the heat produced from combustion of fuel into mechanical work. Compression ignition (CI) and spark ignition A. K. Agarwal (&) N. Sharma Indian Institute of Technology Kanpur, Kanpur 208016, Uttar Pradesh, India e-mail: [email protected] N. Sharma e-mail: [email protected] J. G. Gupta Government Women Engineering College, Ajmer, India e-mail: [email protected] A. P. Singh University of Wisconsin–Madison, Madison 53715, WI, USA e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2019 A. K. Agarwal et al. (eds.), Advanced Engine Diagnostics, Energy, Environment, and Sustainability, https://doi.org/10.1007/978-981-13-3275-3_1

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(SI) engines have been widely adopted as power plants for passenger and commercial automotive applications, electricity generation, and other industrial applications. Excessive use of petroleum origin fuels has led to sustainability issues and harmful pollutant emissions. Both these issues are quite serious for mineral diesel-fuelled CI engines, which are preferred over gasoline-fuelled SI engines due to their higher power density and greater thermal efﬁciency. CI engines emit relatively higher oxides of nitrogen (NOx) and particulates compared to SI engine. NOx and particulates emitted by CI engines are harmful for both human health and the environment. To resolve the emission issues, there are two approaches, namely (i) pollutant formation reduction using advanced combustion techniques and (ii) pollutant reduction using after-treatment systems. This monograph covers some of these topics, which are divided into four sections: (i) general, (ii) advanced fuels and combustion techniques, (iii) emission control techniques and after-treatment systems, and (iv) simulation and fault diagnostics. This chapter is part of the ﬁrst section, and it introduces the monograph. The second section of this monograph covers application of alternative fuels and advanced combustion techniques for achieving higher efﬁciency and lower emissions. In this section, a novel combustion strategy, namely reactivity-controlled compression ignition (RCCI), is included. RCCI combustion is a dual-fuel technique in which low-reactivity fuels such as gasoline, alcohols are introduced upstream of the intake valve, to ensure a high level of fuel-air homogeneity in the combustion chamber, and high-reactivity fuels such as mineral diesel, biodiesel are injected directly in the combustion chamber to initiate the combustion (Kokjohn and Reitz 2013). RCCI combustion offers are more complete combustion based on reactivity of both fuels; therefore, it offers a superior control over the combustion. RCCI combustion can reduce harmful emissions of NOx and particulates simultaneously. Utilization of alcohols in RCCI combustion is another interesting feature, which can potentially reduce the excessive consumption of mineral diesel. In this section, ﬁrst chapter describes the detailed mechanism of RCCI combustion and its characteristics while using different alcohols as low-reactivity fuel. Another chapter in this section describes the potential of low temperature combustion (LTC) techniques, namely homogeneous charge compression ignition (HCCI), partially premixed charge compression ignition (PCCI), and RCCI combustion. This chapter shows that HCCI and PCCI combustion techniques are capable of reducing particulate and NOx emissions from diesel-fuelled engines (Agarwal et al. 2013; Jain et al. 2017). Detailed description about the soot formation process and factors affecting soot formation are few other important topics covered in this chapter. For utilization of alternative fuels, a dual-fuel diesel engine fuelled with hydrogen and producer gas is included in this section. This chapter discusses the performance and emission characteristics of dual-fuel engine operated up to full engine load. Results showed that use of hydrogen and producer gas improves the engine efﬁciency. Third section of the monograph covers the application of advanced engine technologies and after-treatment systems for pollutant emission reduction from diesel engines. This section starts with the conventional techniques for diesel

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engines to reduce harmful emissions. A review on effectiveness of common rail direct injection (CRDI) system is presented in the ﬁrst chapter of this section. This chapter shows that CRDI systems can reduce particulate emissions; however due to the introduction of stricter emission norms (as Euro VI), only CRDI systems are not able to comply with such stringent emission norms. Therefore, it becomes necessary to use after-treatment systems along with these advanced engine techniques. Therefore, different after-treatment systems such as diesel particulate ﬁlter (DPF), selective catalytic reduction (SCR), lean NOx trap (LNT) are discussed in the next chapter. This chapter shows that these systems can reduce NOx and particulate emissions; however, these systems use precious and expensive rare earth metals, which increase the cost of the vehicle signiﬁcantly. Therefore, the use of low-cost non-noble metal-based catalysts has been discussed in the next chapter. These non-noble metals can reduce the particulate emissions by increasing the soot oxidation. A chapter about the use of ceria-based mixed oxide nanoparticles for emission control from diesel engines has also been included in this section. This chapter describes different aspects of this technique such as properties of ceria-based nanoparticles, its preparation, and characterization using different techniques such as scanning electron microscopy (SEM), dynamic light scattering. Last section of the monograph is based on the simulations and fault diagnostics. In this section, different simulation techniques have been discussed, which directly or indirectly affect the IC engines. This section starts with a chapter based on model-based fault detection system, which becomes essential for modern engines equipped with sophisticated digital control systems and complex electronic hardware such as input–output sensors, actuators, and processing units. This chapter describes different techniques and fault diagnostic methodologies, which can be used to diagnose any number of signal faults. Next chapter is based on the carbon emission reduction using carbon capture and storage (CCS) technologies . Chemical looping combustion (CLC) is a promising oxy-fuel carbon capture technology consisting of two interconnected fluidized beds with a metal oxide as a bed material/oxygen carrier. The particles, which circulate between both reactors, ﬁrstly absorb oxygen in the air reactor (AR) and secondly provide the oxygen for the combustion in the fuel reactor (FR). This chapter shows that superior results from this technique can be achieved by scale-up investigations and sensitivity analysis. Last chapter of this monograph is based on the tribological studies of an IC engine. Reliable and safe operation of an engine requires effective lubrication of moving parts to allow them to slide smoothly over each other. This chapter describes the optimal conditions for the oil ﬁlm formation in all friction-coupled interfaces such as a piston cylinder, piston rings’ cylinder, main bearings. Proper lubrication in an engine directly affects the life of different moving components by reducing wear. It also affects the engine efﬁciency and fuel consumption because frictional losses affect the power supplied to the engine shaft. Therefore, it is very relevant topic of research for advanced IC engine. This monograph presents different technologies, which can be used for increasing thermal efﬁciency and lowering the emissions. Speciﬁc topics covered in this monograph include:

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• Introduction to Advanced Engine Diagnostics • Reactivity-Controlled Compression Ignition Combustion Using Alcohols • Effect of Hydrogen and Producer Gas Addition on the Performance and Emissions on a Dual-Fuel Diesel Engine • Characteristics of Particulate Emitted by IC Engines Using Advanced Combustion Strategies • Non-Noble Metal-Based Catalysts for the Application of Soot Oxidation • Ceria-Based Mixed Oxide Nanoparticles for Diesel Engine Emission Control • On-Board Post-Combustion Emission Control Strategies for Diesel Engine in India to meet Bharat Stage VI Norms • Modelling and Experimental Studies of NOx and Soot Emissions in Common Rail Direct Injection Diesel Engines • Model-Based Fault Detection on Modern Automotive Engines • Study of Instability Nature of Circular Liquid Jet at Critical Chamber Conditions • Transient Reacting Flow Simulations of Chemical Looping Combustion Reactors • Tribological Studies of an Internal Combustion Engine.

References Agarwal AK, Singh AP, Lukose J, Gupta T (2013) Characterization of exhaust particulates from diesel fuelled homogenous charge compression ignition combustion engine. J Aerosol Sci 58:71–85 Jain A, Singh AP, Agarwal AK (2017) Effect of fuel injection parameters on combustion stability and emissions of a mineral diesel fueled partially premixed charge compression ignition (PCCI) engine. Appl Energy 190:658–669 Kokjohn SL, Reitz RD (2013) Reactivity controlled compression ignition and conventional diesel combustion: a comparison of methods to meet light-duty NOx and fuel economy targets. Int J Engine Res 14(5):452–468

Part II

Advanced Fuels and Combustion Techniques

Chapter 2

Reactivity-Controlled Compression Ignition Combustion Using Alcohols Akhilendra Pratap Singh, Nikhil Sharma, Dev Prakash Satsangi, Vikram Kumar and Avinash Kumar Agarwal

Abstract Rapidly increasing fossil fuel consumption along with increasing fuel cost and serious concerns about carbon dioxide (CO2) emission reduction from the transportation sector motivated the automotive researchers to explore new internal combustion (IC) engine technologies, which can deliver higher engine efﬁciency with a lower impact on the environment and human health. These issues can be resolved by using advanced combustion strategies, which are also capable of utilizing alternative fuels. In last few years, reactivity-controlled compression ignition (RCCI) combustion has attracted signiﬁcant attention due to its capability of ultra-low oxides of nitrogen (NOx) and particulate emissions without any soot-NOx trade-off and superior engine efﬁciency compared to compression ignition (CI) and spark ignition (SI) combustion. RCCI combustion is a combination of dual-fuel and partially premixed combustion (PPC) techniques, in which a low-reactivity fuel such as gasoline, compressed natural gas (CNG), alcohols are injected into the intake port and a high-reactivity fuel such as mineral diesel, biodiesel is directly injected into the combustion chamber. Blending of these two fuels in the combustion chamber controls the heat release rate (HRR) and combustion phasing. Premixed ratio and spatial stratiﬁcation between these two fuels control the combustion phasing and combustion duration. RCCI combustion and emission characteristics are also dependent on fuel injection strategies such as fuel injection pressure (FIP), number of injections, start of injection (SOI) timings, exhaust gas recirculation (EGR) rate, and intake charge temperature. This chapter reviews all these factors and presents important features of RCCI combustion for application in future automotive engines. A separate section for use of alcohols in RCCI

A. P. Singh (&) Department of Mechanical Engineering, University of Wisconsin-Madison, Madison, WI 53715, USA e-mail: [email protected] N. Sharma D. P. Satsangi V. Kumar A. K. Agarwal Department of Mechanical Engineering, Indian Institute of Technology Kanpur, Kanpur 208016, Uttar Pradesh, India © Springer Nature Singapore Pte Ltd. 2019 A. K. Agarwal et al. (eds.), Advanced Engine Diagnostics, Energy, Environment, and Sustainability, https://doi.org/10.1007/978-981-13-3275-3_2

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combustion is also included in this chapter, which shows various pathways for alternative fuel utilization in this advanced combustion technique. Roadmap for future research directions for RCCI combustion is also discussed in this chapter. Keywords Reactivity-controlled compression ignition (RCCI) Fuel injection strategy Alcohols

2.1

Fuel reactivity

Introduction

Demand of fuel-efﬁcient engines is an active research area worldwide due to current issues related to rapidly depleting petroleum reserves, rising environmental concerns and the adverse effect of pollutants on human health. From the beginning of internal combustion (IC) engines, mineral diesel-fueled compression ignition (CI) engines are preferred over the spark ignition (SI) engines due to their higher thermal efﬁciency and superior fuel economy. High compression ratios as well as less throttling losses are the main factors leading to their higher efﬁciency. These engines are used in various applications such as power generation, agriculture, and transport sector. However, these engines are associated with higher emissions of oxides of nitrogen (NOx) and particulates, which limit their application in mega-cities. These toxic emissions have an adverse effect on human health as well as on the environment. To resolve these issues, exhaust gas after-treatment systems such as diesel particulate ﬁlters (DPF), lean NOx trap (LNT), and selective catalytic reduction (SCR) have been installed in modern diesel engines, with a view to reduce toxic emissions. These after-treatment systems have successfully reduced the tailpipe emissions; however, higher cost of these systems and issues related to their durability limit their application in vehicles (Dec 2009). In recent years, various advanced combustion strategies have been developed, which reduce the formation of pollutants in the combustion chamber. These strategies include partially premixed charge compression ignition (PCCI), homogeneous charge compression ignition (HCCI), and reactivity-controlled compression ignition (RCCI). Figure 2.1 shows the basic features of advanced combustion strategies and their evolution over time. These techniques were developed for simultaneous reduction of particulates and NOx emissions, while keeping thermal efﬁciency of the engine equivalent to conventional CI engines. In HCCI technique, premixing of fuel and air occurs outside the cylinder before the charge induction into the engine cylinder. Thereafter, this combustible charge is ignited by compression, similar to CI engines. However, in PCCI combustion, a fraction of fuel is injected during the early compression stroke, which mixes with air and forms partially premixed charge before the start of ignition. This premixed charge helps in the combustion of fuel injected during the main injection. It has been reported that PCCI and HCCI methods help reduce particulate and NOx emissions; however, they increase carbon monoxide (CO) and unburnt hydrocarbon (UHC) emissions (Agarwal et al. 2013; Saravanan et al. 2015). Lack of an adequate combustion

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Fig. 2.1 Different combustion strategies

phasing control and heat release rate (HRR) are few other challenges faced by these combustion techniques, which limit their application at higher engine loads. Due to these issues, several other combustion techniques have been developed, in which RCCI combustion is the most important one.

2.2

Reactivity-Controlled Compression Ignition (RCCI)

RCCI combustion technique was ﬁrst demonstrated at Engine Research Center of University of Wisconsin–Madison. RCCI combustion is a dual-fuel technique, in which two fuels of different reactivity are used. The concept of dual-fuel in diesel engines has been used since 1955, which allows the in-cylinder blending of two different fuels (Karim 1980). In RCCI combustion technique, a low-reactivity fuel such as gasoline/alcohols/compressed natural gas (CNG) is used to create a homogeneous fuel–air mixture either using port fuel injection (PFI) or by using early direct injection. A high-reactivity fuel such as mineral diesel/biodiesel is injected in the compression stroke, early enough to mix below particulate and NOx formation equivalence ratios, similar to PPC. Combustion starts in high-reactivity regions and then proceeds to low-reactivity regions. The combustion duration is controlled by the spatial stratiﬁcation of fuel reactivities, which decreases with increasing mixing duration. RCCI combustion gives excellent results in terms of

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performance and emissions over the entire operating range of the engine. The dual-fuel strategy allows easy control of combustion phasing, which is controlled by the local concentration of high-reactivity fuel and start of injection (SOI) timing of high-reactivity fuel. Since the combustion duration is controlled by the reactivity gradient, it can be effectively used to reduce pressure rise rates (PRRs) and combustion noise. For higher thermal efﬁciency and lower NOx and particulate emissions, RCCI combustion also uses multiple injection strategy in one cycle, along with EGR. These parameters affect the fuel–air chemical kinetics (in-cylinder reactivity), which can be used to optimize the combustion phasing and combustion duration (Reitz and Duraisamy 2015). Due to these advantages, RCCI engine has attracted global attention and motivating researchers to further develop RCCI combustion concept for commercial applications. In a slightly different way, RCCI combustion can be deﬁned as combustion achieved by charge stratiﬁcation in the combustion chamber using two fuels (with low and high cetane numbers). Low cetane number fuel with lower reactivity is injected in the port, which is premixed with air outside the combustion chamber, and thereafter, it enters the combustion chamber. At the end of the compression stroke, high cetane number fuel with higher reactivity is directly injected into the combustion chamber, which is already ﬁlled with lower reactivity fuel–air mixture (Fig. 2.2). The direct injection of fuel creates layers of fuels; hence for more layer generation, multiple injections strategy is highly desirable. During multiple injections, the ﬁrst injection of diesel targets the squish region, whereas the relatively late injected diesel acts as an ignition source (Eichmeier et al. 2014). In RCCI combustion, ratio of both fuels quantities is very important because this is critical for combustion control. This leads to superior combustion control in case of RCCI combustion compared to HCCI or PCCI combustion (Reitz and Duraisamy 2015). This results in lower NOx and particulate emissions, reduced heat transfer loss, and increased fuel efﬁciency, thus eliminating the need for exhaust gas after-treatment systems.

Fig. 2.2 RCCI combustion strategy for different test fuels

Direct Injection of HighReactivity Fuel such as Mineral Diesel, Biodiesel, etc.

Port Injection of LowReactivity Fuel such as Gasoline, Alcohols, CNG, etc.

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Reitz (2010) presented a comparison of energy budgets for different combustion techniques achieved in a single-cylinder oil test engine at 9 bar indicated mean effective pressure (IMEP) (Fig. 2.3). This ﬁgure clearly indicates that RCCI combustion is capable of generating signiﬁcantly higher gross IMEP (*59%) compared to other advanced combustion techniques. The RCCI combustion process converts 59% of the recovered heat loss to work, while the high exhaust gas recirculation (EGR) diesel engine converted only 44% of the recovered heat loss to work. In RCCI combustion, improved combustion control is the main reason for more efﬁcient work extraction, by achieving shorter combustion duration. Relatively lower heat transfer loss (HT MEP) makes this combustion technique more attractive compared to conventional diesel combustion (CDC). Table 2.1 shows the comparison of different advanced combustion techniques and conventional combustion strategies. Li et al. (2014) introduced two parameters, namely global reactivity and reactivity stratiﬁcation, which affect the RCCI combustion control. Global reactivity depends on quantity of fuel directly injected in the combustion chamber, and reactivity stratiﬁcation is related to fuel spray penetration and the entrainment of direct-injected higher reactivity fuel with the mixture of lower reactivity fuel and air. The direction of flame propagation depends on the engine type and fuel injection parameters such as SOI timing, injector spray angle. For an effective RCCI combustion and load limit extension, the optimization of fuel properties plays an important role. Fuel property optimization increases the reactivity gradient between directly injected higher reactivity fuel and premixed lower reactivity fuel (Wang et al. 2018). Therefore, researchers explored different fuel pairs (low/high-reactivity) for RCCI combustion, in which gasoline/diesel, CNG/diesel, methanol/diesel, n-butanol/diesel, gasoline/biodiesel, methanol/biodiesel, etc., are most used fuel pairs (Dahodwala et al. 2014; Doosje et al. 2014; Dempsey et al. 2013; Zhang et al. 2013; Wang et al. 2015). In these studies, a common fact has been reported that

[CL: Combustion loss; HT: Heat loss; Ex: Exhaust loss]

Fig. 2.3 Caterpillar single-cylinder test engine energy budgets (9 bar IMEP). Adapted from Staples et al. (2009), Hardy and Reitz (2006), Hanson et al. (2009, 2010), Reitz (2010)

Lean air–fuel ratio

Near stoichiometric air–fuel ratio Flame propagation speed Cleaner with three-way catalyst Higher CO2

Other emission characteristics

Combustion control mechanism Particulate and NOx emissions

Fuel-flow control

Air-flow control

Power output control Fuel–air mixture condition

Higher HC and CO and lower CO2

Lower NOx and particulates

Higher particulate and NOx (without after-treatment)

Lower CO2

Chemical kinetics

Lean air–fuel ratio or charge dilution

Compression ignited Blend of every liquid or gaseous fuel Fuel-flow control

HCCI

Time for fuel vaporization and mixing

High cetane

High octane

Fuel type

Compression ignited

CI

Spark ignited

Ignition type

SI

Higher HC and CO and lower CO2

Lower NOx and particulates

Chemical kinetics and injection timing

Lean air–fuel ratio or high charge dilution

Fuel-flow control

Blend of every liquid or gaseous fuel

Compression ignited

PCCI

Table 2.1 Comparison of SI, CI, HCCI, PCCI, and RCCI combustion strategies (Paykani et al. 2016)

Very high HC and CO (without after-treatment) and lower CO2

Ultra-low NOx and particulates

Chemical kinetics and fuel reactivity

Air–fuel ratio stratiﬁcation, typically without charge dilution

Fuel reactivity stratiﬁcation

PFI of high octane and DI of high cetane fuel

Compression ignited

RCCI

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higher octane fuels such as alcohols through port injection are beneﬁcial for increasing engine efﬁciency and engine load limit extension. However, these fuels also showed higher cyclic variations and higher CO and HC emissions. In most studies, mineral diesel has been used as a high-reactivity fuel for RCCI combustion. Few researchers also used biodiesel as a high-reactivity fuel for RCCI due to its higher cetane number compared to mineral diesel (Li et al. 2015; Mohsin et al. 2014). The presence of oxygen in biodiesel is another reason for its application in RCCI combustion, which promotes soot oxidation, leading to further lower particulate emissions. In recent studies on RCCI combustion, it is reported that the properties of directly injected fuel played a dominant role over the combustion. Therefore, a small amount of cetane number improvers such as 2-ethylhexyl nitrate (2-EHN) and di-tert butyl peroxide (DTBP) in high-reactivity fuels can be used to improve the RCCI combustion characteristics (Reitz and Duraisamy 2015). Higgens et al. (1998) reported that the addition of small quantity of these cetane number improvers shortened the ignition delay; however, the combustion characteristics of base-fuel (mineral diesel) were not affected signiﬁcantly. Splitter et al. (2010) used cetane improver (DTPB) in gasoline to increase its cetane number. They used gasoline as high-reactivity fuel and found similar performance characteristics as that of RCCI combustion with ultra-low-sulfur-diesel (ULSD) as highreactivity fuel. Kokjohn and Reitz (2013) presented a comparison between combustion characteristics of RCCI combustion and CDC. They showed that the average temperatures predicted by the simulation of these two combustion modes were very similar. The main difference between these two combustion modes was in their peak in-cylinder temperatures since CDC resulted in peak combustion temperature of *2800 K and RCCI combustion resulted in signiﬁcantly lower peak combustion temperature of *1700 K. The peak temperature location in RCCI combustion was more homogeneous compared to CDC, and the location of peak temperature region was away from the piston and cylinder walls. Figure 2.4 shows the in-cylinder temperature distribution inside the combustion chamber at different SOI timings and methanol fraction. 0% methanol showed the CDC; however, 40 and 60% methanol mass showed the RCCI combustion. Results showed that advancing SOI timing resulted in lower in-cylinder temperature; however, increasing methanol fraction led to higher in-cylinder temperature. Relatively more homogeneous fuel distribution with higher mass of methanol was the main reason for this behavior, which reduced the localized fuel-rich regions. Li et al. (2013) also reported that increasing methanol resulted in lower CO emission due to the presence of higher temperature zones near cylinder walls (Fig. 2.4). In another study by Reitz (2010), it was reported that RCCI combustion resulted in *7% higher thermal efﬁciency compared to conventional CDC. He suggested that reduced heat transfer loss from RCCI combustion compared to early injection CDC was one factor leading to relatively higher thermal efﬁciency of RCCI combustion. RCCI combustion also results in superior thermal efﬁciency compared to retarded injection CDC due to improved combustion phasing and combustion duration (Fig. 2.5).

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Fig. 2.4 Cut-planes colored by in-cylinder temperature of different methanol mass fraction at 6, 12, and 20° CA ATDC (Li et al. 2013)

Fig. 2.5 Effectiveness of RCCI combustion compared to CDC (Reitz 2010)

Prikhodko et al. (2011) carried out experiments to compare the particulate characteristics emitted from different combustion modes, namely CDC, PCCI, and RCCI combustion. They reported that dual-fuel RCCI combustion emitted 40% lesser particulate mass compared to CDC mode; however, it was twice higher than diesel PCCI. The presence of signiﬁcantly higher volatile HC, accumulated on the ﬁlter paper loaded from dual-fuel RCCI combustion, was the main reason for relatively higher particulate mass emission compared to PCCI combustion. The physical appearance of ﬁlters loaded with particulates from the three combustion

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Fig. 2.6 Filters loaded with particulate from RCCI, PCCI, and CDC modes (Prikhodko et al. 2011)

modes showed that CDC mode emitted signiﬁcantly higher black carbon (BC) and RCCI combustion emitted the lowest BC in the exhaust gas (Fig. 2.6). In another study by Prikhodko et al. (2011), it was reported that RCCI combustion emitted signiﬁcantly higher unregulated emissions such as aldehydes, ketones, and other gasoline-speciﬁc species including benzene and toluene compared to CDC and diesel PCCI combustion modes. These species are the results of low-temperature combustion chemistry in RCCI combustion.

2.3

RCCI Combustion Using Alcohols

After the great success of biofuels utilization in conventional combustion modes, the next immediate requirement was to use these biofuels in advanced combustion techniques such as RCCI. Researchers used a wide range of fuels including several alternative fuels, namely alcohols, biofuels, etc., in RCCI combustion to achieve a reactivity gradient between primary and secondary fuels. Initially, RCCI combustion was introduced with high cetane (mineral diesel) and high octane fuel (gasoline) combination; however with development of RCCI combustion, several other fuels have also appeared. Use of renewable fuels such as alcohols enhances the potential of RCCI combustion to be adapted in modern engines and expands the operating range of RCCI mode. Use of renewable fuels (especially alcohols as lowreactivity fuels) in RCCI combustion also helps in decreasing the petroleum consumption rate through a combination of direct petroleum displacement and enhanced efﬁciency, as well as reduction in life cycle emissions. This section describes various alcohols, which can be used in RCCI combustion. In alcohol family, methanol is the ﬁrst member, which can be utilized in the engines. Methanol can be produced from natural gas and coal, which are uniformly distributed in sufﬁcient quantity throughout the world. Methanol has excellent fuel properties, and its higher latent heat of vaporization (LHV) helps reduce NOx emissions because it lowers the combustion temperature. The absence of carbon–

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carbon bonds in methanol reduces particulate formation. Inherent oxygen present in methanol promotes the oxidation of HC, CO, and particulates. However, signiﬁcantly lower cetane number of methanol (3–5) compared to mineral diesel hampers the engine performance. In last few decades, methanol has been used in CI and SI engines; however, it has several issues related to its toxicity and incompatibility with modern engine designs and components. But researchers have found the excellent behavior of methanol in RCCI combustion. Li et al. (2013) optimized the RCCI combustion using numerical techniques for combustion and emission characteristics of a methanol/diesel-fueled RCCI engine. They reported that methanol addition via PFI signiﬁcantly affected fuel reactivity and equivalence ratio distribution in the combustion chamber. They varied the methanol fraction at the same engine load and found a drastic reduction in peak HRR and ringing intensity (RI). They suggested that lower cetane number of methanol retarded the combustion phasing (due to longer ignition delay), which reduced the PRR during initial stage of combustion. Dempsey et al. (2013) reported that high octane number and more charge cooling effect of port fuel-injected methanol affected the RCCI combustion efﬁciency. The results showed a signiﬁcant increase in the amount of high-reactivity fuel (mineral diesel) for maintaining the same combustion phasing. Ethanol is the second member of the alcohol family. Ethanol is the most investigated alcohol for IC engine applications. Ethanol can be produced from agricultural feedstocks such as corn and sugarcane. Due to higher heat of vaporization of ethanol, it results in higher charge cooling effect, which is beneﬁcial for RCCI combustion. Higher resistance to auto-ignition is another important feature of ethanol, which results in retarded combustion phasing. Retarded combustion phasing leads to higher peak load limit of RCCI combustion. Researchers proposed that advanced combustion techniques can utilize the ethanol–biodiesel fuel pair instead of gasoline–diesel pair (Sayin and Uslu 2008). Isik and Aydin (2016) showed that ethanol–biodiesel fuel pair resulted in slightly higher brake thermal efﬁciency (BTE) compared to CDC and ethanol–diesel pair. They suggested that ethanol–biodiesel-fueled RCCI combustion eliminated high-temperature regions and hence reduced the heat losses. This resulted in higher expansion work, leading to higher BTE compared to the ethanol–diesel-fueled RCCI combustion (Fig. 2.7).

Fig. 2.7 Comparison of ethanol–biodiesel-fueled RCCI combustion with ethanol–diesel-fueled RCCI combustion (Isik and Aydin 2016)

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Researchers also investigated the use of hydrated ethanol in RCCI combustion. Dempsey et al. (2013) successfully used hydrated ethanol in dual-fuel RCCI combustion via GT-Power, CHEMKIN, and KIVA. Hanson et al. (2010) used E20 (20% ethanol blended with gasoline) and biodiesel to achieve RCCI combustion and compared the results with gasoline–mineral diesel-fueled RCCI combustion. They reported that E20 resulted in superior RCCI combustion (MPRR less than 10 bar/deg) compared to gasoline. Use of E20 extended the engine load limit, and E20 showed higher BTE compared to gasoline, especially at higher engine loads. Fang et al. (2015) also investigated the hydrous ethanol–diesel-fueled RCCI combustion. They reported higher BTE and lower emissions of NOx and particulates from hydrous ethanol–diesel RCCI combustion by substituting 80% of fuel energy by hydrous ethanol. Park et al. (2014) compared the emission characteristics of bioethanol–biodiesel RCCI combustion with gasoline–biodiesel RCCI combustion. They reported that bioethanol–biodiesel RCCI combustion emitted signiﬁcantly lower NOx and particulates compared to gasoline–biodiesel RCCI combustion; however, HC emissions were higher in case of bioethanol–biodiesel RCCI combustion. Butanol is the next alcohol in the alcohol family, which can be used in RCCI combustion. As an oxygenated biofuel, n-butanol can be a promising alternative fuel for IC engines. N-butanol has higher carbon content and caloriﬁc value than other alcohols, which makes it more suitable for use in engines. Butanol has higher miscibility, lower vapor pressure, lower boiling point temperature, and better lubricity compared to methanol and ethanol. Due to these properties of butanol, Mohebbi et al. (2018) suggested that butanol can be mixed with mineral diesel to enhance its atomization characteristics. Figure 2.8 shows the comparison of RCCI

(a)

(b)

[rp= premixed ratio; B0= Mineral diesel as DI fuel; B20= 20% butanol blended with mineral diesel as DI fuel] Fig. 2.8 In-cylinder pressure and heat release rate at various premixed ratios for two different fuels (Mohebbi et al. 2018)

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combustion characteristics fueled with B0 and B20 (20% butanol by volume, balance diesel) as directly injected fuel and gasoline as port-injected fuel. The combustion characteristics of B20-fueled RCCI combustion were almost similar to B0; however, B20-fueled RCCI combustion exhibited slightly lower premixed ratios compared to B0. This was due to the presence of butanol in mineral diesel, which hampered its reactivity and resulted in lesser reactivity gradient compared to B0. Due to lower cetane number, butanol has also been used as low-reactivity fuel. Figure 2.9 shows a comparison of exhaust emissions from RCCI combustion fueled by biodiesel (DI fuel) and different port-injected fuels (ethanol, butanol, and 2,5-dimethylfuran (DMF)). The experimental results showed that use of butanol in RCCI combustion caused slightly higher particulate emissions compared to ethanol. However, HC and CO emissions were slightly lower for butanol–biodiesel-fueled RCCI combustion. Zheng et al. (2018a, b) attributed the particulate emissions with ignition delay that affects the fuel–air mixing process. Relatively longer ignition delay of ethanol compared to butanol was reported as a possible reason for lesser particulate emissions from ethanol–biodiesel-fueled RCCI combustion. In another study, Zheng et al. (2018a, b) compared the butanol utilization in RCCI combustion as low-reactivity fuel and butanol–mineral diesel blend in CI combustion mode. They reported that butanol utilization in RCCI combustion reduced the particulate emissions; however, it increased HC and CO emissions. They suggested that butanol is suitable for RCCI combustion because it can use higher fraction of butanol compared to blending technique, which extends the upper limit of engine load, it can cater to.

Fig. 2.9 Comparison of NOx and particulate emissions from different fuels at different EGR rates (Zheng et al. 2018a, b)

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Parameters Affecting RCCI Combustion

RCCI combustion is completely based on in-cylinder blending of two fuels having different fuel reactivities. In-cylinder chemical kinetics of these fuels plays an important role in deciding the effectiveness of RCCI combustion. Therefore, RCCI combustion is affected by all those parameters, which directly or indirectly affect the in-cylinder charge formation and the fuel–air chemical kinetics. Many researchers used modeling tools to investigate the effect of different parameters on RCCI combustion, in order to develop a relationship between the control parameters. Wickman et al. (2001) utilized Genetic Algorithm (GA) and engine modeling techniques to optimize the engine combustion chamber geometry and air/fuel system-related operating parameters for two different size engines. With this method, they obtained remarkable reduction in both engine-out emissions and fuel consumption without manufacturing a large number of designs. Nieman et al. (2012) used GA coupled with KIVA-3V CFD codes and CHEMKIN chemical kinetics tool to improve the performance and emissions from a diesel–methane RCCI engine under varying speed and load conditions. By optimizing variables such as fraction of methane, diesel SOI timing, and quantity, they found very low NOx and particulate emissions at loads up to 13.5 bar IMEP, without the use of EGR.

2.4.1

Effect of Fuel Injection Strategy

Fuel injection timing of direct injection plays a crucial role in deciding the fuel–air mixing; hence, it controls the start of combustion (SoC). Many researchers carried out experiments to investigate the effect of SOI on RCCI combustion, and they reported a retarded combustion phasing with advanced SOI timings. This led to lower NOx and peak pressure rise rates (PPRR) with an increase in CO emission. With advancing SOI timing, HC emissions slightly decreased. In the case of two direct injections (one pilot and one main), injection timings of the second injection also affected the RCCI combustion. The combustion phasing advanced at retarded SOI timing, which resulted in increased peak in-cylinder pressure and led to higher in-cylinder temperature. Relatively lesser time availability for fuel–air mixing was the main reason for this behavior at retarded SOI timings. This also led to higher NOx and particulate emissions; however, HC and CO emissions remained almost constant. Fuel injection quantities injected during ﬁrst and second direct injections are another important parameter. Hanson et al. (2010) carried out detailed experiments to investigate the effect of fuel quantities injected during ﬁrst and second injections and reported that CO and HC emissions remained constant for different fuel quantities injected during the ﬁrst injection; however upon increasing the fuel injection quantity during the second injection, signiﬁcantly higher NOx and particulate emissions were observed. They observed a distinct two-stage High

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Temperature Heat Release (HTHR) with a lower percentage of fuel in the ﬁrst injection with advanced SOI timing. With increasing fuel quantity during the ﬁrst injection, a retarded combustion phasing with the retarded two-stage HTHR was observed. Fang et al. (2012) performed RCCI combustion experiments using pilot injection strategy. They reported that increasing the pilot injection fuel quantity decreased the NO emission signiﬁcantly. Hanson et al. (2010) carried out engine experiments using dual-fuel LTC strategy using port fuel injection of gasoline and early direct injection of mineral diesel. They compared the measured and simulated in-cylinder pressure and heat release rate curves for different SOI timings (from −29 to −48° CA ATDC) of direct injection. They found that retarding the SOI timing after a critical limit resulted in signiﬁcantly higher NOx and particulate emissions due to heterogeneous fuel–air mixture formation. However, advancing SOI timings caused lower PRR due to lower mixture stratiﬁcation, which promoted fuel–air mixing and reduced the ignition delay. Kokjohn et al. (2012) performed experiments to compare the RCCI combustion characteristics achieved at different SOI timings. They used high-speed combustion luminosity imaging technique to capture the combustion images. These images were distinct due to differences in the mixing times in the three cases. The SOI timing sweeps showed higher peak HRR for very early or late injections of n-heptane. For the minimum peak HRR, an intermediate SOI timing range (*−50° CA ATDC) should be used. At advanced SOI timing, a rapid heat release took place due to under-stratiﬁed charge; however at retarded SOI timing, a rapid heat release took place due to over-stratiﬁed charge. At advanced SOI timings, the chemiluminescence images showed HCCI-like combustion, where ignition locations were distributed randomly throughout the combustion chamber. The fuel-tracer imaging showed a weak spatial gradient in the fuel reactivity in case of early SOI timings.

2.4.2

Effect of Intake Air Temperature and Pressure

Intake charge temperature signiﬁcantly affects the fuel–air chemical kinetics; therefore, it also contributes in controlling the RCCI combustion. Intake charge temperature refers to the temperature of fuel (injected in the port)–air mixture, which is measured in the intake manifold. Slight modiﬁcations in the global fuel reactivity can easily recover the baseline combustion phasing for both hotter and colder intake temperatures. Local reactivity is important for RCCI combustion propagation, which proceeds gradually from high-reactivity to low-reactivity regions. This directly affects the HC and CO emissions by reducing the incomplete combustion. Similar to intake charge temperature, intake pressure also affects RCCI combustion. In comparison to temperature, intake pressure is more important in modern turbocharged engines because they fluctuate by a greater degree compared to the intake charge temperature. Splitter et al. (2014) performed RCCI experiments at different intake pressures and temperatures. They reported that at higher intake air

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Fig. 2.10 Effect of intake temperature on RCCI combustion (Zhang et al. 2017)

temperatures, premixed equivalence ratio approached global equivalence ratio, where RCCI combustion behaved like HCCI combustion. They reported that increasing intake temperatures resulted in higher combustion efﬁciency. Zhang et al. (2017) carried out experiments to investigate the effects of increasing intake air temperature on different combustion modes, namely RCCI and pipe injection (PI) only. Figure 2.10 shows that direct injection of n-heptane extended the lower limit of IMEP at lower intake air temperatures. This was mainly due to improved combustion of lean fuel–air mixtures, especially at lower intake air temperatures. However at high intake air temperatures, overall operating range of RCCI combustion extended due to signiﬁcant contribution of n-heptane, which improved both the lower as well as the upper limit of IMEP. This study indicated that relative fuel quantities could be adjusted according to the intake air temperature. Therefore at lower intake air temperature, quantity of n-heptane can be increased to compensate for the quantity of n-butanol. Desantes et al. (2014) explored the effect of the intake temperature on RCCI combustion efﬁciency at light engine loads. They reported that combustion efﬁciency could be improved by *1.5% using suitable intake air temperatures.

2.4.3

Effect of Fuel Reactivity

The fuel reactivity is another important parameter to control RCCI combustion. To investigate the effect of fuel reactivity on RCCI combustion, a long range of lowreactivity fuels have been investigated. Among these fuels, alcohols showed signiﬁcant potential for efﬁcient RCCI combustion. Splitter et al. (2011) and Curran et al. (2012) carried out RCCI experiments using E85 on heavy-duty and light-duty diesel engines. Due to lower reactivity of E85 compared to gasoline, it resulted in a wider reactivity gradient for RCCI combustion (Fig. 2.11).

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Fig. 2.11 Effect of alcohol on RCCI combustion (Splitter et al. 2011; Curran et al. 2012)

Effect of fuel reactivity on RCCI combustion was also investigated by varying the premixed ratios. Kokjohn et al. (2010) carried out the simulation and experimental study to investigate the effect of the mass fraction of gasoline on RCCI Fig. 2.12 Effect of premixed mass fraction and intake valve closing (TIVC) temperature on RCCI combustion (Li et al. 2016)

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combustion phasing. They reported that increasing mass fraction of gasoline retarded the combustion phasing and reduced the PRR and HRR. In another research carried out by Li et al. (2016), it has been reported that the combustion phasing can be retarded with reducing intake valve closing (TIVC) temperature and/ or increasing gasoline fraction (Fig. 2.12a). Figure 2.12b shows the differences in the behaviors of the HRRs at different rp and TIVC. At higher TIVC and rp, the LTHR advanced due to dominating effect of rp; however at lower rp and TIVC, the weaker LTHR was found mainly due to less injected diesel into the cylinder with more addition of premixed gasoline.

2.5

Conclusions and Recommendations

This chapter shows the effectiveness of RCCI combustion and its control strategies. RCCI combustion is a newly developed LTC strategy that can be used in CI engines. This strategy has shown to yield higher thermal efﬁciency and simultaneous reduction in particulate and NOx emissions. RCCI combustion has better control over combustion events compared to other LTC strategies, which makes this technique quite attractive. The capability of utilization of alternative fuels in RCCI combustion is another important feature. In RCCI combustion, a long range of alternative fuels having low cetane number and high cetane number can be used without any signiﬁcant effect on its performance and emission characteristics. There are few challenges though in RCCI combustion, which need to be resolved before its commercial application in modern diesel engines. Heal losses from cylinder walls pose the main challenge, which requires employing advanced simulation and computation methods. These techniques can be used to study the temperature distribution inside the combustion chamber and optimizing the distance between high-temperature regions and heat transfer surfaces (cylinder walls), so that heat transfer loss can be reduced. Improvement in combustion efﬁciency and higher HC and CO emissions of high-speed RCCI engines are few more obstacles faced in RCCI combustion. RCCI combustion efﬁciency is also affected by engine design, piston material, compression ratio, and fuel injection strategy; therefore, these parameters need to be investigated in greater details. Serious research efforts are required to reduce the tailpipe HC emissions. This can be achieved by using a combination of oxidation catalysts and superior thermal management of the engine. Optimization of fuel injection strategy for the best NOx and HC trade-off is required to make RCCI combustion more effective for overall emission reduction. There are several novel areas of research, including the effect of increased squish area for combustion control, development of low-cost after-treatment systems for RCCI combustion engines, etc., which would be some of the interesting areas for future RCCI combustion studies. Development of fuel reactivity enhancers may be another interesting area of future research for RCCI combustion.

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References Agarwal AK, Singh AP, Lukose J, Gupta T (2013) Characterization of exhaust particulates from diesel fueled homogenous charge compression ignition combustion engine. J Aerosol Sci 58:71–85 Curran S, Hanson R, Wagner R (2012) Effect of E85 on RCCI performance and emissions on a multi-cylinder light-duty diesel engine. SAE Technical Paper 2012-01-0376 Dahodwala M, Joshi S, Koehler EW, Franke M (2014) Investigation of diesel and CNG combustion in a dual fuel regime and as an enabler to achieve RCCI combustion. SAE Technical Paper 2014; 2014-01-1308 Dec JE (2009) Advanced compression-ignition engines-understanding the in-cylinder processes. Proc Combust Inst 32(2):2727–2742 Dempsey AB, Walker NR, Reitz R (2013) Effect of cetane improvers on gasoline, ethanol, and methanol reactivity and the implications for RCCI combustion. SAE Int J Fuels Lubr 6(1):170– 187 Desantes JM, Benajes J, García A, Monsalve-Serrano J (2014) The role of the in-cylinder gas temperature and oxygen concentration over low load reactivity controlled compression ignition combustion efﬁciency. Energy 78:854–868 Doosje E, Willems F, Baert R (2014) Experimental demonstration of RCCI in heavy-duty engines using diesel and natural gas. SAE Technical Paper 2014; 2014-01-1318 Eichmeier J, Reitz R, Rutland C (2014) A zero-dimensional phenomenological model for RCCI combustion using reaction kinetics. SAE Int J Eng 7(1):106–119 Fang Q, Fang JH, Zhang J, Huang Z (2012) Influences of pilot injection and exhaust gas recirculation (EGR) on combustion and emissions in a HCCI-DI combustion engine. Appl Therm Eng 48:97–104 Fang W, Kittelson DB, Northrop WF (2015) Optimization of reactivity-controlled compression ignition combustion fueled with diesel and hydrous ethanol using response surface methodology. Fuel 160:446–457 Hanson R, Splitter D, Reitz RD (2009) Operating a heavy-duty direct-injection compression-ignition engine with gasoline for low emissions. SAE Technical Paper 2009; 2009-01-1442 Hanson RM, Kokjohn SL, Splitter DA, Reitz RD (2010) An experimental investigation of fuel reactivity controlled PCCI combustion in a heavy-duty engine. SAE Int J Engines 3(1):700– 716. SAE Technical Paper 2010; 2010-01-0864 Hardy WL, Reitz RD (2006) A study of the effects of high EGR, high equivalence ratio, and mixing time on emissions levels in a heavy-duty diesel engine for PCCI combustion. SAE Technical Paper 2006; 2006-01-0026 Higgens B, Siebers D, Mueller C, Aradil A (1998) Effects of an ignition-enhancing, diesel-fuel additive on diesel-spray evaporation, mixing, ignition, and combustion. In: 27th international symposium on combustion, The Combustion Institute, pp 1873–1880 Isik MZ, Aydin H (2016) Analysis of ethanol RCCI application with safflower biodiesel blends in a high load diesel power generator. Fuel 184:248–260 Karim GA (1980) A review of combustion processes in the dual fuel engine—the gas diesel engine. Prog Energy Combust Sci 6(3):277–285 Kokjohn SL, Reitz RD (2013) Reactivity controlled compression ignition and conventional diesel combustion: a comparison of methods to meet light-duty NOx and fuel economy targets. Int J Engine Res 14(5):452–468 Kokjohn S, Hanson R, Splitter D, Reitz R (2010) Experiments and modeling of dual-fuel HCCI and PCCI combustion using in-cylinder fuel blending. SAE Int J Engines 2(2):24–39 Kokjohn S, Reitz RD, Splitter D, Musculus M (2012) Investigation of fuel reactivity stratiﬁcation for controlling PCI heat-release rates using high-speed chemiluminescenc imaging and fuel tracer fluorescence. SAE Int J Eng 5(2):248–269

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Li Y, Jia M, Chang Y, Liu Y, Xie M, Wang T et al (2014) Parametric study and optimization of a RCCI (reactivity controlled compression ignition) engine fueled with methanol and diesel. Energy 65(11):319–332 Li J, Yang WM, An H, Zhao D (2015) Effects of fuel ratio and injection timing on gasoline/ biodiesel fueled RCCI engine: a modeling study. Appl Energy 155:59–67 Li Y, Jia M, Chang Y, Xie M, Reitz RD (2016) Towards a comprehensive understanding of the influence of fuel properties on the combustion characteristics of a RCCI (reactivity controlled compression ignition) engine. Energy 99:69–82 Li Y, Jia M, Liu Y, Xie M (2013) Numerical study on the combustion and emission characteristics of a methanol/ diesel reactivity controlled compression ignition (RCCI) engine. Appl Energ 106:184–197 Mohebbi M, Reyhanian M, Hosseini V, Said MFM, Aziz AA (2018) Performance and emissions of a reactivity controlled light-duty diesel engine fueled with n-butanol-diesel and gasoline. Appl Therm Eng 134:214–228 Mohsin R, Majid ZA, Shihnan AH, Nasri NS, Sharer Z (2014) Effect of biodiesel blends on engine performance and exhaust emission for diesel dual fuel engine. Energy Convers Manag 88:821– 828 Nieman DE, Dempsey AB, Reitz RD (2012) Heavy-duty RCCI operation using natural gas and diesel. SAE Technical Paper 2012; 2012-01-0379 Park SH, Yoon SH, Lee CS (2014) Bioethanol and gasoline premixing effect on combustion and emission characteristics in biodiesel dual-fuel combustion engine. Appl Energy 135:286–298 Paykani A, Kakaee A, Rahnama P, Reitz RD (2016) Progress and recent trends in reactivity-controlled compression ignition engines. Int J Engine Res 17(5):481–524 Prikhodko VY, Curran SJ, Barone TL, Lewis SA, Storey JM, Cho K, Wagner RM, Parks JE (2011) Diesel oxidation catalyst control of hydrocarbon aerosols from reactivity controlled compression ignition combustion. In: ASME 2011 international mechanical engineering congress and exposition, pp 273–278. IMECE 2011; 2011-64147 Reitz RD (2010) The reactivity controlled compression ignition engine: simulating performance in a changing fuel environment. In: 3rd MACCCR fuels research meeting, Princeton, NJ Reitz RD, Duraisamy G (2015) Review of high efﬁciency and clean reactivity controlled compression ignition (RCCI) combustion in internal combustion engines. Prog Energy Combust Sci 46:12–71 Saravanan S, Pitchandi K, Suresh G (2015) An experimental study on premixed charge compression ignition-direct ignition engine fueled with ethanol and gasohol. Alex Eng J 54 (4):897–904 Sayın C, Uslu K (2008) Influence of advanced injection timing on the performance and emissions of CI engine fueled with ethanol-blended diesel fuel. Int J Energy Res 32(11):1006–1015 Splitter D, Reitz RD, Hanson R (2010) High efﬁciency, low emissions RCCI combustion by use of a fuel additive. SAE Int J Fuels Lubr 3(2):742–756 Splitter DA, Hanson RM, Kokjohn SL, Reitz RD (2011) Reactivity controlled compression ignition (RCCI) heavy-duty engine operation at mid-and high-loads with conventional and alternative fuels. SAE Technical Paper 2011; 2011-01-0363 Splitter D, Wissink M, Delvescovo D, Reitz RD (2014) Improving the understanding of intake and charge effects for increasing RCCI engine efﬁciency. SAE Technical Paper 2014; 2014-01-1325 Staples L, Reitz R, Hergart C (2009) An experimental investigation into diesel engine size-scaling parameters. SAE Int J Engines 2(1):1068–1084 Wang H, DelVescovo D, Yao M, Reitz RD (2015) Numerical study of RCCI and HCCI combustion processes using gasoline, diesel, iso-butanol and DTBP cetane improver. SAE Int J Engines 8(2):831–845 Wang H, Zhao X, Tong L, Yao M (2018) The effects of DI fuel properties on the combustion and emissions characteristics of RCCI combustion. Fuel 227:457–468 Wickman DD, Senecal PK, Reitz RD (2001) Optimization using genetic algorithms and multi-dimensional spray and combustion modeling. SAE Technical Paper 2001; 2001-01-0547

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Zhang C, Zhang C, Xue L, Li Y (2017) Combustion characteristics and operation range of a RCCI combustion engine fueled with direct injection n-heptane and pipe injection n-butanol. Energy 125:439–448 Zhang Y, Sagalovich I, De Ojeda W, Ickes A, Wallner T, Wickman DD (2013) Development of dual-fuel low temperature combustion strategy in a multi-cylinder heavy-duty compression ignition engine using conventional and alternative fuels. SAE Int J Engines 6(3):1481–1489 Zheng Z, Xia M, Liu H, Shang R, Ma G, Yao M (2018a) Experimental study on combustion and emissions of n-butanol/biodiesel under both blended fuel mode and dual fuel RCCI mode. Fuel 226:240–251 Zheng Z, Xia M, Liu H, Wang X, Yao M (2018b) Experimental study on combustion and emissions of dual fuel RCCI mode fueled with biodiesel/n-butanol, biodiesel/2,5-dimethylfuran and biodiesel/ethanol. Energy 148:824–838

Chapter 3

Effect of Hydrogen and Producer Gas Addition on the Performance and Emissions on a Dual-Fuel Diesel Engine Abhishek Priyam, Prabha Chand and D. B. Lata

Abstract There is a global interest in the use of alternative fuels due to environmental concerns such as greenhouse emission, ozone depletion, air pollution. Also, the limited petroleum reserves invite the alternate solution for diesel engines. Several researchers have proposed various types of solutions. One among them is the use of different gaseous fuels with pilot diesel fuel. An experimental work has been done to ﬁnd the performance of high-capacity diesel engine which uses diesel fuel with the variation of hydrogen and rice-husk-derived producer gas. The results of engine test with producer gas and hydrogen on brake thermal efﬁciency and emissions such as unburnt hydrocarbon, carbon monoxide, and NOx are presented. Beyond 30% load, the brake thermal efﬁciency of dual-fuel operation is improved. Maximum efﬁciency of 38–43% is achieved with mixture of 10% PG and varying hydrogen from 5 to 25% and similarly for mixture of 40% PG and varying hydrogen gives the maximum efﬁciency of 43–48% at 60% load condition. It is found that speciﬁc energy consumption increases with the increase in PG and hydrogen flow through inlet of engine. The maximum fuel substitution has been found at 80% load with 10% PG and 25% hydrogen mixture. At higher loads, volumetric efﬁciency has been better as the oxygen or air intake would be more, but at mixture of 40% PG and 25% hydrogen, the volumetric efﬁciency reaches a level of 27% as there is sufﬁcient amount of PG and hydrogen, but minimum intake of air took place. The higher CO and HC emission levels were recorded for increased producer gas content due to the CO content. Nox emissions were maximum at higher loads due to the presence of nitrogen in air as well as fuel. Overall smooth running of engine is found in all cases. One major ﬁnding of

A. Priyam MPSTME, NMIMS University, Mumbai, India P. Chand (&) N.I.T. Jamshedpur, Jamshedpur, Jharkhand, India e-mail: [email protected] D. B. Lata Central University of Jharkhand, Ranchi, Jharkhand, India © Springer Nature Singapore Pte Ltd. 2019 A. K. Agarwal et al. (eds.), Advanced Engine Diagnostics, Energy, Environment, and Sustainability, https://doi.org/10.1007/978-981-13-3275-3_3

29

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the experiment is that the mixture of PG and hydrogen is an alternative fuel with good efﬁciency.

Keywords Dual-fuel diesel engine Producer gas Emissions Brake thermal efﬁciency

3.1

Hydrogen

Introduction

The economic development of any country requires the energy as the major input. Currently, twin crisis of fossil fuel depletion and environmental degradation is confronted by the world. Modern agriculture is an extremely energy-intensive process. High agricultural productivities and the growth of green revolution have been made possible only by large amount of energy inputs, especially those from fossil fuels. Scarcity of fossil fuels and skyrocketing of fuel prices have been a trend toward the use of alternative energy sources like solar, wind, geothermal. However, these alternatives have not been able to provide an economically feasible solution for agricultural applications (Yogi Goswami 1986). For the developing country like India, a missionary approach is needed to explore the possible use of biofuel/their blends with pilot fuel (diesel) as an alternative fuel that reduces the environmental pollution and to enhance the energy security of the country.

3.1.1

Background

The increasing concern about environmental safety and scarcity of crude oil has become the focus of much attention to use alternative fuels. Two alternative potential fuels are producer gas and hydrogen. Hydrogen engines have various attractive characteristics, but especially at high load, they lead to suffer from premature ignition. This is because of lower ignition energy, wider flammability range, and shorter quenching distance of hydrogen. But, producer gas engines have less oppressive in this enigma although producer gas constitutes 10–20% of hydrogen. The complete replacement of crude oil for the transportation and agricultural sectors is the biggest and toughest challenge for India. In the view of this, biomass-based power generation is being promoted and being encouraged in the country. Producer gas can act as a promising alternative fuel, especially for diesel engines by substituting considerable amount of diesel oil. However, diesel engines cannot be operated on producer gas without injection of a small amount of diesel oil because the producer gas will not ignite under the prevailing conditions of temperature and pressure. A diesel engine needs to be dual-fueled. Use of biomass gasiﬁers to drive engines in single-fuel mode of operation has the advantage of having complete independence from petroleum fuels. This feature is very

3 Effect of Hydrogen and Producer Gas Addition on the Performance …

31

convenient for electricity generation in more remote areas or areas inaccessible for long periods over the year. Further, compared to gasoline engines, exhaust emissions like NOx, CO, and HC are lesser for producer-gas-operated gas engines. The major problem with producer gas operated gas engines is power derating. A power derating from 40 to 70% can be expected. Because of very high derating, enormous applications of existing SI engines for producer gas operation are less attractive. Modiﬁcation of existing diesel engine for dual-fuel operation with producer gas is very simple, and power derating is limited to 20–30% (Banapurmath et al. 2008). Biomass is available in various forms such as forest residues (stems, branches, and leaves), crop residues (husk, straw), plantation residues, animal excrete (cow dung, poultry droppings), industrial waste (wood chips, sawdust, and bagasse), and municipal solid waste. This biomass may be converted into various types of fuels by means of physical, chemical, and biological degradation processes. India, an agro-based country, requires major attention for the requirement of energy demands of a farmer. Irrigation is the main activity of the country and this must be developed on a larger side, but at the same time, use of diesel fuel must be minimized because of its price and scarcity because diesel is used in agriculture and transportation sectors. Finding an alternative fuel for diesel fuel is critically important for our nation’s economy and security. This study focused on engines fueled with blend of producer gases and hydrogen with pilot fuel diesel.

3.1.2

Engine Technologies

3.1.2.1

Dual-Fuel Engine Concept

Dual-fuel engine consists of both types of combustion that exist together; i.e., a carbureted mixture of air and gaseous fuel is compressed (Sahoo et al. 2009). Due to high auto-ignition temperature of the compressed mixture of air and gaseous fuel, it does not auto-ignite. Hence, a small liquid fuel injection is ﬁred which ignites spontaneously at the end of compression phase. It uses the difference of flammability of two used fuels which make its advantages so that in case of lack of gaseous fuel, this engine runs prior to the diesel cycle by switching from dual-fuel mode. The necessity to have liquid diesel fuel makes its disadvantage available for the dual-fuel engine operation.

3.1.2.2

Biomass Gasiﬁcation

Gasiﬁcation stands for incomplete combustion which results in the production of combustible gases consisting of carbon monoxide, hydrogen, and an amount of methane, and the mixture is well known as producer gas (Sahoo et al. 2009), which can be used for various applications such as to run internal combustion engines (both compression and spark ignition), substitute for furnace oil in heat

32

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applications, and can be used to produce methanol which is an extremely attractive chemical, useful as fuel for engines as well as chemical feedstock in industries. Any biomass material can undergo gasiﬁcation which makes it more attractive than ethanol production or biogas in which only selected biomass materials can be used to produce them (Uma et al. 2004).

3.1.2.3

Modiﬁcation in IC Engines

A spark ignition-gas engine modiﬁcation is easy as it is already designed to operate on air–fuel mixture with spark ignition. A provision of a gas–air mixer instead of the carburetor is added in this type of engine, and the engine control is performed by the mixture supply variation, i.e., throttle valve position. In dual-fuel gas diesel engines, a mixture of air and gaseous fuel is induced and compressed. The compressed mixture is then ignited by a small amount of the diesel fuel spray known as pilot fuel. A small amount of diesel fuel spray, i.e., 10–20% of the operation on diesel alone needed for this ignition at normal working loads, and this amount varies with the point of engine operating and its design conditions. During part-load engine operation, the fuel gas supply is reduced by means of a gas control valve. However, a continuous reduction of the air supply decreases the air quantity induced. Hence, the compression pressure and the mean effective pressure of the engine decrease. This leads to power drop and efﬁciency. The drastic reduction in the compression conditions might even become too weak for the mixture to effect self-ignition. Therefore, dual-fuel engines should not be controlled on the air side. So, there is a need for optimum variation in the liquid pilot fuel quantity used any time in relation to the gaseous fuel supply. The main goal is to minimize the use of the diesel fuel for both emissions and economic reasons and to maximize its replacement by the cheaper gaseous fuel for entire engine load range. The dual-fuel engine can operate effectively for various gaseous fuels as a conventional diesel engine (Roy et al. 2009).

3.1.3

Fuel Options

The deﬁciency of fossil fuel, skyrocketing cost of the liquid petroleum fuels, and severe environmental damage have developed the interest of using gaseous fuels. Among the several alternatives compressed natural gas (Market Study Series, GNC 2009), liqueﬁed petroleum gas (Bauer et al. 1996), hydrogen, biogas (Ramalingam 2011), producer gas, and liqueﬁed natural gas (LNG) have been explored as an alternative gaseous fuel in various parts of the world depending on their availability and nature of applications in IC engines.

3 Effect of Hydrogen and Producer Gas Addition on the Performance …

3.1.3.1

33

Hydrogen and Producer Gas as an Alternative Fuel

Hydrogen holds the potential to provide clean, reliable, and affordable energy supply that can enhance economy, environment, and security. It is flexible and can be used by all the sectors of economy. It is non-toxic and recyclable. Due to these qualities, it is considered to be an ideal energy carrier in the foreseeable future (Khan 2006). Woody matter such as crop residue, wood chips, bagasse, rice husk and coconut shells can be transformed to producer gas (also known as synthesis gas, syngas, wood gas and water gas or blue gas) by a method known as gasiﬁcation of solid fuel. The compositions of the gas produced depend on the type of biomass and the design of gasiﬁer. This can be used to fuel IC engines (diesel, dual-fuel mode engines) for irrigation pumps, motor vehicles, and small-scale power generation or to produce process heat. Table 3.1 shows the constituents of producer gas, and the various properties of hydrogen is given in Table 3.2.

3.1.4

Literature Reviews

The dual-fuel diesel–producer gas operating modes have been reported in the literature by several investigators using different fuels in dual-fuel mode. Some of the literatures are tabulated in Table 3.3.

Table 3.1 Constituents of producer gas (percentage by volume) Carbon monoxide (%)

Hydrogen (%)

Methane (%)

Nitrogen (%)

Carbon dioxide (%)

20.36

20.6

1.2

47

11.3

Table 3.2 Properties of Hydrogen Property

Range/ value

Property

Range/ value

Limits of flammability in air (vol.%) Minimum energy for ignition (MJ) Quenching gap at NTP air (cm) Auto-ignition temperature (K)

4–75

Burning velocity in NTP air (cm/s) Diffusion coefﬁcient at NTP air (cm2/s) Heat of combustion (MJ/kg)

265–325

0.02 0.064 858

0.61 119.93

34

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Table 3.3 Various conﬁgurations of dual-fuel engines used in the literature S. No.

Author

Type of fuel/engine used

Nature of study

Major ﬁndings

1

Luijten and Kerkhof (2011)

Jatropha oil and biogas (CH4 and CO2)

Experimental

2

Bose and Maji (2009)

Hydrogen as inducted fuel and diesel as injected fuel

Experimental

3

Roy et al. (2009)

Two producer gases having different hydrogen content

Experimental

4

Uma et al. (2004)

Diesel-alone and dual-fuel modes with producer gas

Experimental

5

Krishna and Kumar (1994)

Coffee husk biomass

Experimental

6

Ramadhas and Jayaraj (2006)

Coir-pith and wood derived producer gas

Experimental

7

Sridhar et al. (2011)

Producer gas in SI mode

Experimental

Maximum heat release fraction of methane of about 80% for pure methane and 25–55% for biogas (depending on load and biogas quality). Efﬁciency suffered at the low loads and at high loads. Thermal efﬁciency is hardly affected by biogas addition Supply of 0.15 kg/h of hydrogen increases brake thermal efﬁciency by 12.9%, but at high rate, the combustion becomes uncontrolled, and hence, thermal efﬁciency decreases. They found that the hydrogen–diesel blend has proved to be a viable approach to minimize pollution and improve performance Smooth and smoke-free engine operation for different fuel-air equivalence ratios and pilot injection timing Found that the diesel engine is capable of running with dual fueling with 67–86% diesel replacement rate Achieved the maximum diesel replacement of 31% only because of clinkers’ formation and a low density of biomass Speciﬁc energy consumption in dual-fuel mode was higher at all load conditions. The brake thermal efﬁciency of engine using wood chips is higher than that of coir-pith. CO emission was higher in dual-fuel mode Performance of the engine at higher CR has been found smooth, and suitable modiﬁcation of the chamber was essential for reducing the energy loss to the coolant. The maximum derating in power is observed to be 16% in gas mode when compared to diesel operation at comparable CR (continued)

3 Effect of Hydrogen and Producer Gas Addition on the Performance …

35

Table 3.3 (continued) S. No.

Author

Type of fuel/engine used

Nature of study

Major ﬁndings

8

Azimov et al. (2011)

Different types of syngas

Experimental

9

Singh et al. (2007)

CI engine with rice bran oil and producer gas in mixed fuel mode

Experimental

10

Banapurmath et al. (2009)

Producer gas with three other oils

Experimental

11

Lee et al. (2017)

Dual combustion concept

Experimental

The mass of fuel burned during the second stage affected the rate of maximum pressure rise. Syngas content affected the engine performance and emissions. Increased H2 content led to higher combustion temperatures and efﬁciency, lower CO and HC emissions, but higher NOx emissions. Increased CO2 influenced performance and emissions only when it reached a certain level At 84% engine load having 18.4:1 compression ratio and operating in dual-fuel mode, the concentration of pollutants reduced except HC compared to diesel, whereas with 17:1 CR and operating in dual-fuel mode, the concentration of pollutant reduced except NOx as compared to diesel Poor performance at all the loads with dual-fuel mode when compared with single-fuel mode at all injection timings tested, but brake thermal efﬁciency improved marginally with advanced injection timing. Decreased smoke, NOx emissions, and increased CO emissions were observed for dual-fuel mode for all the fuel combinations compared to single-fuel operation High efﬁciency and low emissions were achieved at low-speed and low-load condition by using single-cylinder diesel engine and lower NOx and PM emission with high gross inducted fuel conversion efﬁciency with excellent combustion stability (continued)

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Table 3.3 (continued) S. No.

Author

Type of fuel/engine used

Nature of study

Major ﬁndings

12

Mustaﬁ et al. (2013)

Dual-fuel engine operated on natural gas and bio gas fuels

Experimental

13

Taku et al. (2017)

Hydrogen in hydrogen/dual-fuel engine

Experimental

14

Dimitriou et al. (2018)

Hydrogen–diesel dual-fuel engine

Experimental

15

Belgiorno et al. (2018)

Methane–diesel dual-fuel engine

Experimental

16

Senthil Kumar et al. (2017)

Mahua oil-based dual fuel

Experimental

Rapid and high energy release rates, increased ignition delay, and shortened combustion duration dual fueling. Also, lowest NOx and increased UBHC emissions were achieved for all dual-fuel operation and substantial reduction in PM emission Higher thermal efﬁciency than conventional at higher load conditions. Preignition with relatively high input energy fraction of hydrogen was reported before diesel fuel injection and ignition. Measured the ﬁre-deck temperature to investigate the casual relationship between ﬁre-deck temperature and occurrence of preignition with charging operating conditions of dual-fuel engine Smooth engine operation with hydrogen energy share ratio of up to 98% with the reduction of all harmful emissions of over 85% at low-load conditions. Controlled the rise of NOx emissions at medium load by using exhaust gas recirculation A signiﬁcant impact of the CR, EGR, and A/F ratio variables on the UBHC reduction up to 40%. Relevant impact on the development of advanced combustion engines running under natural gas–diesel dual-fuel combustion mode and transferable in modern light-duty engines Improved brake thermal efﬁciency, reduced smoke, and NOx emission. Also, an optimal energy share for the best combustion behavior was found

3 Effect of Hydrogen and Producer Gas Addition on the Performance …

3.2

37

Experimental Investigation on Dual-Fuel Engine

3.2.1

Basic Experimental Conﬁguration

A test diesel engine setup was developed in the laboratory to carry out study on dual-fuel engine. The engine system was modiﬁed and ﬁtted with suitable retroﬁts at speciﬁc locations to generate relevant data. The instruments were properly calibrated to minimize the possible errors during experimentation work. The detailed experimental setup is shown in Fig. 3.1.

3.2.2

Test Rig Description

3.2.2.1

Biogasiﬁer

Gasiﬁers are the reactors which produce the producer gas by the gasiﬁcation of solid biomass. The most common gasiﬁer is the downdraft type, and it has been used to produce the producer gas. The various zones of the downdraft gasiﬁer are the combustion zone, pyrolysis zone, and the reduction zone. Rice husk is fed from the top hopper, and the airflows in the downward direction through the different zones. Air movement and the fuel movement are in the same direction. The gasiﬁer is designed in such a way that tars in the pyrolysis zone are drawn through the combustion zone and they are being cracked and reduced to non-condensable gaseous products before leaving the gasiﬁer. Air inlet nozzles are set radially around the throat to distribute air as uniformly as possible. The producer gas is then sent to the cooler to reduce its temperature and then ﬁltered before sending to the gas mixture box. The photograph of biomass gasiﬁer is shown in Fig. 3.2, and the speciﬁcations are shown in Table 3.4.

3.2.2.2

Diesel Engine

The present study was carried out on a four-stroke, compression ignition engine, Ashok Leyland ALU WO 4CT model diesel engine coupled with 62.5 kVA generator. It is equipped with in-line four cylinders, vertical, direct injection, water-cooled, turbocharged with intercooler. The rated power output is of 75.5 HP at 1500 RPM. This engine consists of single rigid crankshaft and single independent cylinder head with single tappet cover. The ﬁring order of the engine is 1-3-4-2. The camshaft is located at right-hand side of the engine and operates on valve rocker arrangement through valve tappet and push rod. The inlet and exhaust

38

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Fig. 3.1 Experimental setup

manifold, fuel injection pump, and engine oil ﬁlter assembly are ﬁtted on the left-hand side of the engine. The drive to the camshaft is provided through idle gear arrangement. The engine lubrication system is of wet sump type, with 7.5 L capacity. A gear-driven pump is used for the circulation of lubrication oil. The lubrication oil temperature and pressure at full load and rated 1500 rpm were 65 °C and 450 kPa. The engine cooling system consists of water-circulating pump, fan, radiator, and intercooler which cool down the temperature of air/charge after turbocharger and before entering into inlet manifold. These components are ﬁtted on the front side of the engine. A thermostat in the cooling system is provided to maintain working temperature of the engine. The thermostat valve opens at 85 °C. The flywheel is mounted on the rear side of the crankshaft housing. TDC marks and fuel injection point (FIP) of the ﬁrst cylinder piston marks are on the outer edge of the flywheel. A mechanical governor is attached to the fuel injection system to control the diesel flow rate. The rotation of the crankshaft is anticlockwise when viewed from flywheel end. The photographic view of the engine is shown in Fig. 3.3, and the relevant information about the engine is presented in Table 3.5.

3.2.2.3

The Generator

The generator used is brushless alternator (4/D 200L) of Kirloskar coupled with the multi-cylinder diesel engine. This generator is self-excited and self-regulated type. The rotor consists of an exciter, armature, main rotor, and rotating assembly. This system eliminates the slip rings and brush gear provision of the conventional alternators. The generator is self-regulated by automatic voltage regulator which

3 Effect of Hydrogen and Producer Gas Addition on the Performance …

39

Fig. 3.2 Photograph of biogasiﬁer

Table 3.4 Speciﬁcations of down draft gasiﬁer S. No.

Type

Speciﬁcation

1

Supplier

2 3 4

Rated capacity Rated gas Flow Average gas caloriﬁc value Rated woody biomass consumption Hopper storage capacity Biomass size Typical conversion efﬁciency Typical gas composition

Datacone Engineers Pvt. Ltd., Sangli, Maharashtra, India 62,000 kJ/h 12 nm3/h 4185 kJ/m3

5 6 7 8 9

5–6 kg/h 50 kg 10–50 mm 70–80% CO = 15–21%, CO2 = 8–13%, N2 = 42–52%, H2 = 16–20%, CH4 = up to 3%

controls the excitation of the exciter ﬁeld to maintain the desired voltage within limits. During starting, the residual magnetism creates a current in the exciter rotor. This current is rectiﬁed by the rotating diode assembly which is supplied into the

40

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main rotor. Then, the main rotor induces a voltage in auxiliary windings. The induced voltage in the auxiliary windings is given to exciter stator through automatic voltage regulator (AVR). The AVR is an advanced electronic voltage regulator with an accuracy of ±1%. It has an excellent dynamic response, and the recovery time is 0.3–0.5 s on the application of full load.

3.2.2.4

Control Panel

The control panel of the generator is ﬁtted on a separate base. It is ﬁtted with ammeter, voltmeter, and frequency meter in addition to different engine and alternator control switches like starting, external load connection, and engine cutoff.

3.2.3

Engine Modiﬁcations

3.2.3.1

Engine Fuel Supply System Modiﬁcation

The original engine was supplied by the manufacturer with fuel tank located below the engine fuel feed pump. The fuel pump was drawing the diesel fuel, and after injection with the help of injection pump, the excess amount of fuel got back into tank through overflow return pipe. In the modiﬁed system, the fuel tank was ﬁtted above the engine level at certain height to ensure fuel to flow under gravity. The 1000 cc plastic burette apparatus with a control valve was placed in between the fuel tank and engine fuel supply system. Similarly, the provision was also made for

Fig. 3.3 Photograph of engine setup

3 Effect of Hydrogen and Producer Gas Addition on the Performance …

41

Table 3.5 Engine speciﬁcations S. No.

Parameter

Speciﬁcation

1

Make and model

2

General details

3 4 5 6 7

No. of cylinder Bore (mm) Stroke (mm) Rated speed (rpm) Swept volume per cylinder (cc) Clearance volume (cc) Compression ratio Injection pressure (bar) Injection timing BTDC (°) Rated power kW at 1500 rpm Inlet pressure (bar) Inlet temperature (K) Nozzle diameter (mm)

Ashok Leyland ALU WO 4CT Turbocharged, intercooler, gen-set Four stroke, compression ignition, constant speed, vertical, water-cooled, direct injection, turbocharger, intercooler, gen-set 4 104 113 1500 959.917

8 9 10 11 12 13 14 15

232.90 17.5:1 260 16 62.5 1.06 313 0.285

attaching the overflow return pipe to the same plastic burette. Separate gas cylinders for hydrogen with pressure regulator were used, and producer gas from the gasiﬁer entered directly to the air–gaseous fuel mixture and operated with a valve ﬁtted for opening of producer gas.

3.2.3.2

Engine Cooling System Modiﬁcation

The closed-loop water cooling system in the supplied engine was replaced by an open-loop arrangement to supply water with a separate pump. A manually controlled mechanical valve was provided to monitor the flow rate of water through engine jacket, and outgoing water temperature was maintained in between 85 and 90 °C.

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Exhaust System Modiﬁcation

An exhaust system was provided with the engine with exhaust-gas-driven turbine of the turbocharger and was open out to the atmosphere at the top of the engine. In this test engine exhaust pipe was properly designed with an extended horizontal pipe attached to the engine. The provision was made to ﬁt thermocouple to measure exhaust temperature and probe for exhaust gas analyzer.

3.2.4

Developments of Measuring Units

The diesel gen-set engine was converted into a complete test rig with appropriate changes in the air intake system, engine cooling system, engine exhaust system, and fuel supply system (both diesel and gaseous fuels). During changes, proper care was taken to put up instruments with accessories to measure basic quantities during experimentation work.

3.2.4.1

Air Measurement System

The method used to measure air consumption is based on “Air Box Method.” It consisted of an airtight chamber (tank) of volume 625 625 1260 mm3 ﬁtted with sharp edge oriﬁce of diameter 54 mm and having known coefﬁcient of discharge. This oriﬁce was connected on the opposite face of the suction pipe. The volume of the tank was based on the displacement volume of the engine (approximately 500 times the engine swept volume). Due to suction of the engine, depression is developed in the chamber which causes the flow through oriﬁce. A rubber diaphragm was provided to further reduce the pressure pulsation. The pressure difference causing the flow through the oriﬁce was measured with the help of U-tube water manometer.

3.2.4.2

Fuel Measurement System

The fuel consumption of an engine is measured by determining the volume flow in a given time interval, or to measure the time required for the consumption of a given volume (or mass) of fuel. The liquid fuel measurement system for the test rig was based on the gravimetric principle. The diesel flow rate to the engine was measured by fuel measuring burette of 1000 cc capacity with a control valve. The time for 100 cc of diesel consumption was measured with the help of a stopwatch. The hydrogen was supplied to the engine from gas cylinders which were

3 Effect of Hydrogen and Producer Gas Addition on the Performance …

43

maintained at a constant temperature, and the producer gas was supplied directly from the gasiﬁer and controlled with the valve. The amount of pilot diesel fuel was automatically controlled with the help of a governor, while the flow of gaseous fuels was controlled manually.

3.2.4.3

Engine Speed Measurements

A speed meter was provided on the dashboard of the engine by the manufacturer. The RPM of the engine was ﬁxed at 1500 rpm.

3.2.4.4

Pressure Measurement System

The intake manifold air pressure was measured by U-tube water manometer, while hydrogen pressures were measured by pressure gauge and producer gas were measured by venturimeter.

3.2.4.5

Emission Measurements

Exhaust gas emissions, namely CO, CO2, NOx, O2, and unburnt hydrocarbons (UHC), were measured by HG-540 automotive emission analyzer. The CO, CO2, and O2 were measured in volume percentage basis with an uncertainty of 5%, whereas NOx and UBHC were measured in ppm units with an uncertainty of 5%. The gas analyzer was calibrated by passing a known amount of gases, and readings were taken with variation in gaseous concentration.

3.2.4.6

High-Speed Data Acquisition System

The purpose of data acquisition is to measure an electrical or physical phenomenon such as voltage, current, temperature, pressure, or sound. PC-based data acquisition uses a combination of modular hardware, application software, and a computer to record measurements. While each data acquisition system is deﬁned by its application requirements, every system shares a common goal of acquiring, analyzing, and presenting information. Data acquisition systems incorporate signals, sensors, actuators, signal conditioning, data acquisition devices, and application software. The data acquisition system was used for storing the pressure time history of the engine cylinder. A 12-bit A/D card was used to convert the pressure signals to digital data. It was then stored on the hard disk of the computer. The A/D card has

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external and internal activating facility with sixteen single-ended or eight differential channels. The internal charge frequency can be adjusted at any desired sampling frequency, while the external activation was done at 1° crank angle using external charge pulse.

3.3 3.3.1

Experimentation on Dual-Fuel Engine with Hydrogen and Producer Gas Experimental Procedure

The four-stroke, compression ignition engine, Ashok Leyland ALU WO 4CT model, turbocharged with intercooler, four-cylinder test rig with a rated power of 75.5 Ps at 1500 rpm coupled with 62.5 kW generator was used for the experimental investigation. The experiments were performed on the test engine under the following ﬁve conditions (Table 3.6). (i) Case I: Engine runs on diesel as pilot fuel and producer gas with 10% and varying hydrogen content from 5 to 25% as secondary fuel. (ii) Case II: Engine runs on diesel as pilot fuel and producer gas with 20% and varying hydrogen content from 5 to 25% as secondary fuel. (iii) Case III: Engine runs on diesel as pilot fuel and producer gas with 30% and varying hydrogen content from 5 to 25% as secondary fuel. (iv) Case IV: Engine runs on diesel as pilot fuel and producer gas with 40% and varying hydrogen content from 5 to 25% as secondary fuel. At the time of conducting experiments, the engine was given sufﬁcient time to stabilize. Then, during each load condition, the following data, viz. engine speed, diesel flow rate, gaseous fuels (PG and hydrogen) flow rate, gaseous fuel intake pressure and temperature, airflow rate, intake air temperature and pressure, cooling water temperature, TDC crank angle signals, cylinder pressure signals, and exhaust emissions such as CO, CO2, NOx, HC, O2, and smoke were recorded. The cooling water temperature was maintained at 90 °C (±5 °C), whereas PG temperature was kept at 35 °C. The lubricating oil temperature was at 65 °C. After 20 min of engine operation on stabilized conditions, pressure data were obtained for an average of 100 cycles. The mass flow rate of hydrogen and PG was measured by mass flow meters in liters per minute. To ensure repeatability, the experiments were carried out for ﬁve times. The engine was coupled with the gen-set with rated capacity of 62.5 kW and power factor of 0.8. As resistive loading system was used for these experimental works with a power factor of unity hence, not to overload the generator, the

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45

Table 3.6 Mass flow rate of fuels used with different compositions Producer gas (%)

Mass flow rate (kg/h)

Hydrogen (%)

Mass flow rate (kg/h)

P1 P2 P3 P4

0.4586 0.824 1.371 1.855

H1 H2 H3 H4 H5

0.02688 0.03949 0.05265 0.0688 0.0790

= = = =

10 20 30 40

= = = = =

5 10 15 20 25

maximum power output of the engine was ﬁxed at 50 kW for the entire range of experimental studies. The loads over the engine were selected as 2, 6, 18, 30, and 42 kW. The full-load condition (50 kW) was avoided because of knocking problem.

3.3.2

Gaseous Fuel Substitution

In the present experiment, the diesel fuel is substituted by the gaseous fuels like producer gas and hydrogen. A small amount of diesel fuel is injected in the compressed air–gaseous fuel mixture. The fuel injection pump was ﬁtted with a mechanical governor to control the diesel fuel supply during variable load conditions. The amount of gaseous fuel is entered with the opening of producer gas valve, and hydrogen is entered in the intake manifold through fuel flow rate. The experiments were performed extensively for all the ﬁve cases, to study the performances and emission characteristics of each individual fuel at different load conditions.

3.3.3

Performance Parameters

Engine performance and exhaust emission tests are very important to observe effects of a fuel on the performance and emission of the engine. These test results indicate an idea whether the fuel is used in an engine efﬁciently and without any problem or not. For that reason, it is necessary to determine performance parameters of an engine. There are several performance parameters, such as brake torque (T), brake power (BP), brake mean effective pressure (Bmep), brake-speciﬁc fuel consumption (Bsfc), and brake thermal efﬁciency. It is necessary to ﬁnd these parameters which can be obtained using measurement values of fuel and air consumptions, heating capacity of the fuel, torque, and speed.

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Results and Discussion

3.4.1

Experimental Results with Producer Gas and Hydrogen Blend as Secondary Fuel

The engine performance characteristics by PG–hydrogen mixture substitution were evaluated on the basis of variation in load conditions and different PG–hydrogen substitutions on brake thermal efﬁciency, brake-speciﬁc energy consumption, and emissions, namely unburnt HC, CO, CO2, and NOx.

3.4.2

Performances

3.4.2.1

Brake Thermal Efﬁciency

The effect of dual-fuel operations on brake thermal efﬁciency at different compositions and different loads is shown in Fig. 3.4. At lower loads, dual-fuel operation with hydrogen and producer gas shows an inferior curve. This is due to lower combustion rate caused by a high CO content in the PG fuels. Again at these loads, pilot fuel leads to poor ignition and combustion of lean air–gas mixture. Therefore, a minor influence of PG and hydrogen fuel is on the thermal efﬁciency at part load. However, beyond 30% load, the brake thermal efﬁciency of dual-fuel operation is improved. An increase in hydrogen content improves the brake thermal efﬁciency effectively. This is due to the faster combustion rate of hydrogen and PG and higher level of premixing. Maximum efﬁciency of 38–43% has been achieved with 10% PG and varying hydrogen from 5 to 25% and similarly at 40% PG and varying hydrogen gives the maximum efﬁciency of 43–48% at 60% load condition. Beyond this load condition, the efﬁciency decreases as the lower heat value including poor combustion rate of small PG and hydrogen mixture fuels.

3.4.2.2

Brake-Speciﬁc Energy Consumption (BSEC)

The BSEC of the engine is higher at part-load conditions. With the 10% PG and varying hydrogen from 5 to 25%, it is found that with the increase in hydrogen content, the BSEC increases. This value of BSEC increases with the 40% of PG. The graph between load and BSEC clearly indicates different values of PG with varying hydrogen content. It can be seen from Fig. 3.5 that the minimum BSEC is achieved at 60% load irrespective of the fuel used in the dual-fuel mode. This is expected at the maximum efﬁciency. Above this load, the increase in BSEC and

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47

Fig. 3.4 Brake thermal efﬁciency versus load for various PG and hydrogen mixtures

lower brake thermal efﬁciency is achieved. It is found that speciﬁc energy consumption increases with the increase in PG and hydrogen flow through inlet of engine. An increase in BSEC leads to the reduction in efﬁciency in dual-fuel engine. This shall be due to the incomplete combustion and lower flame velocity at a relative high temperature and due to lower heat value of PG and poor combustion rate of small hydrogen fuels.

3.4.2.3

Gaseous Fuel Substitution Rate

The fuel substitution in dual fuel can be calculated as: Fuel Substitution Rate ð%Þ ¼

TotalðFCÞDualðFCÞ 100 TotalðFCÞ

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Fig. 3.5 BSEC versus load for various PG and hydrogen mixtures

Figure 3.6 represents the fuel substitution rate in dual-fuel mode operating with PG with varying hydrogen content. It can be seen that the percentage substitution of fuel goes up to 95% with the 10% of PG and 25% hydrogen and substituting up to 82% with the 40% of producer gas and 25% hydrogen. This states that 5–18% of diesel is saved at full load. It can be seen that about 42–52% of fuel is substituted at 5% hydrogen and 40% of PG. The maximum fuel substitution is found at 80% load with 10% of PG and 25% hydrogen. This increase in the fuel substitution is due to sufﬁcient gas flows at high load, but at the lower load, fuel substitution rate decreases as there is insufﬁcient oxygen to complete the combustion.

3.4.2.4

Volumetric Efﬁciency

Volumetric efﬁciency is the breathing capacity of an engine. It can be seen from Fig. 3.7 that the volumetric efﬁciency is found as 47% when 10% PG and 5%

3 Effect of Hydrogen and Producer Gas Addition on the Performance …

49

Fig. 3.6 Gaseous fuel substitution versus load for various PG and hydrogen mixtures

hydrogen has been used at minimum load, but it decreases as the hydrogen content increases. At higher loads, volumetric efﬁciency is better as the oxygen or air intake would be more, but at 40% PG and 25% hydrogen, this value of volumetric efﬁciency reaches a level of 27% as there is sufﬁcient amount of PG and hydrogen, but minimum intake of air took place. If the volumetric efﬁciency is more, the amount of fresh air intake will be more, and if it is less, the air intake will be less.

3.4.3

Exhaust Emissions

3.4.3.1

CO Emissions

The variation of CO emission of the engine with various mixtures of diesel, hydrogen, and PG is shown in Fig. 3.8. An increase in the percentage of gas mixture, increase in the CO emission is observed. It can be seen that at 10% PG

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Fig. 3.7 Volumetric efﬁciency versus load for various PG and hydrogen mixtures

with 5–25% varying hydrogen gives CO as 0.025–0.075% (on volumetric basis). But it has increased at a level of 0.1–0.4% with 40% PG and 5–25% hydrogen at 5% load. Simultaneously, it reaches at 0.275–1.2% at higher loads with higher concentration. This higher concentration of CO emission in the dual-fuel mode gives an indication of incomplete combustion. The mixture of hydrogen, PG, and airflow to the engine reduces the amount of oxygen required for complete combustion. This creates incomplete combustion and increase in the CO emissions.

3.4.3.2

CO2 Emissions

The variation of CO2 emission of the engine with various mixtures of PG, hydrogen, and diesel is shown in Fig. 3.9. It can be seen that increase in the load

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51

Fig. 3.8 CO versus load for various PG and hydrogen mixtures

there is an increase in the CO2 emission. Also, it can be seen from Fig. 3.9 that the hydrogen content less signiﬁes to increase in the CO2 emission. With the PG as fuel, the CO2 emissions increase. It is around 3.2–3.8% (on volumetric basis) at 10% PG and 5% hydrogen to 25% hydrogen at 5% load, but it increases up to 12.5% at 80% load for the same conditions. Similarly, at 40% PG and 5–25% hydrogen, it is about 3.2–3.9% at 5% load and increases up to 12.8% at 80% load for the same conditions. As expected with the increase in load, there is increase in the CO2 emissions in the dual-fuel mode. This may be due to that the producer gas is a mixture of CO and CO2, and the combustion of these gases increases the CO2 emissions.

3.4.3.3

HC Emissions

The unburnt hydrocarbon variation with load for dual-fuel operations is shown in Fig. 3.10. Due to poor combustion of PG with hydrogen and air at lower loads, the

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Fig. 3.9 CO2 versus load for various PG and hydrogen mixtures

HC emissions are higher. Further increase in the load increased the combustion temperature, and this leads to a more complete combustion of PG with lower HC emissions. The lowest HC emission is estimated at 60% load in each operations; it varies from 11 to 14 ppm for 10% PG, and hydrogen varies from 5 to 25%; it is found to be 41–65 ppm. This may be due to the incomplete combustion of pilot fuel. With increase in pilot fuel and intake temperature which reduces it by EGR combined with intake heating, and after that, efﬁciency increases.

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Fig. 3.10 HC versus load for various PG and hydrogen mixtures

3.4.3.4

NOx Emissions

NOx reduction is a consequence of the lower maximum cylinder pressure and combustion temperature (Stone 1992). On the other hand, NOx emission in the lean combustion is very low. From Fig. 3.11, at the high engine loads (beyond 40%), the more pronounced premixed and advanced combustion of PG and hydrogen fuels resulted in increased cylinder pressure and temperature and hence tended to increase the NOx concentration. This is due to the increase in combustion temperature at higher loads. The highest NOx is observed at 80% load for 10% PG and 25% hydrogen as 760 ppm, and at 5% hydrogen, it is 665 ppm. Further, at 40% PG and 5% hydrogen, it reaches a level of 750 ppm, and at 25% hydrogen, it is 790 ppm at 80% load. This increase is due to the NO2 content in the producer gas. A lower engine loads, the NOx concentrations were reduced for all cases of dual-mode operation. This is due to poor combustion of PG and hydrogen at part load which decreases the cylinder pressure and combustion temperature.

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Fig. 3.11 NOx versus load for various PG and hydrogen mixtures

3.5

Conclusions

The objective of this experiment was to ﬁnd the performance of a dual-fuel engine fueled by hydrogen and producer gas as secondary fuel. This study also found some basic information on the environmental aspects of power generation system with producer gas and hydrogen in dual-fuel mode. This also gives the new alternative fuel. The various mixtures of the producer gas and hydrogen were investigated on the engine performance and emission characteristics. The present investigations on producer gas and hydrogen were resulted and discussed. On the basis of result and discussion presented above, the following conclusions are made: (1) The use of hydrogen and PG as a secondary fuel enhances brake thermal efﬁciency at high load but shows adverse effect at low-load conditions. (2) One major ﬁnding of the present investigations is that the mixture of PG and hydrogen is an alternative fuel with good efﬁciency. (3) The study shows that a higher gaseous fuel substitution of 95% is achieved. (4) The volumetric efﬁciency gets reduced at all loads for dual-fuel operation due to displacement of sucked air by PG and hydrogen.

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(5) Smooth and knock-free engine operation resulted from the use of hydrogen and PG fuel. (6) HC and CO emissions are higher under all load conditions irrespective of fuels because of higher fuel consumption with lower caloriﬁc value fuels. (7) NOx emissions increase as the PG and load increase and decreases as the hydrogen content increases because of nitrogen content in PG and air.

References Azimov U, Tomita E, Kawahara N, Harada Y (2011) Effect of syngas composition on combustion and exhaust emission characteristics in a pilot-ignited dual-fuel engine operated in premier combustion mode. Int J Hydrog Energy 36:11985–11996 Banapurmath NR, Tewari PG, Hosmath RS (2008) Experimental investigations of a four stroke single cylinder direct injection diesel engine operated on dual fuel mode with producer gas as inducted fuel and Honge oil and its methyl ester (HOME) as injected fuel. Renew Energy 33 (9):2007–2018 Banapurmath NR, Tewari PG, Yaliwal VS, Kambalimath S, Basavarajappa YH (2009) Combustion characteristics of a 4-stroke CI engine operated on Honge oil, Neem and Rice Bran oils when directly injected and dual fuelled with producer gas induction. Renew Energy 34:1877–1884 Bauer H ed (1996) Automotive handbook, 4th edn. pp 238–239 Belgiorno G, Di Blasio G, Beatrice C (2018) Parametric study and optimization of the main engine calibration parameters and compression ratio of a methane-diesel dual fuel engine. Fuel 222:821–840 Bose PK, Maji D (2009) An experimental investigation on engine performance and emissions of a single cylinder diesel engine using hydrogen as inducted fuel and diesel as injected fuel with EGR. Int J Hydrog Energy 34:4847–4854 Dimitriou P, Kumar M, Tsujimura T, Suzuki Y (2018) Combustion and emission characteristics of a hydrogen-diesel dual-fuel engine. Int J Hydrog Energy (In Press, Corrected Proof) Khan BH (2006) A textbook on ‘non-conventional energy resources’, 2nd edn. TMH Krishna KS, Kumar A (1994) A study for the utilization of coffee husk in diesel engine by gasiﬁcation. In: Proceeding of biomass gasiﬁcation technology, India Lee J, Chu S, Kang J, Min K, Jung H, Kim H, Chi Y (2017) Operating strategy for gasoline/diesel dual-fuel premixed compression ignition in a light-duty diesel engine. Int J Automot Technol 18(6):943–950 Luijten CCM, Kerkhof E (2011) Jatropha oil and biogas in a dual fuel CI engine for rural electriﬁcation. Energy Convers Manag 52:1426–1438 Ramalingam (2011) A textbook of I.C engines. SciTech Publication Market Study Series, GNC (2009) Consulate of the Argentinian Republic, Mumbai, in Spanish. Retrieved 2011-01-03 Mustaﬁ NN, Raine RR, Verhelst S (2013) Combustion and emissions characteristics of a dual fuel engine operated on alternative gaseous fuels. Fuel 109:669–678 Ramadhas AS, Jayaraj S (2006) Power generation using coir-pith & wood derived producer gas in diesel engines. Food Process Technol 87:849–853 Roy MM, Tomita E, Kawahara N, Harada Y, Sakane A (2009) Performance and emission comparison of a supercharged dual-fuel engine fueled by producer gases with varying hydrogen content. Int J Hydrog Energy 34:7811–7822

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Sahoo BB, Sahoo N, Saha NK (2009) Effect of engine parameters and type of gaseous fuel on the performance of dual fuel gas diesel engines—a critical review. Renew Sustain Energy Rev 13 (6–7):1151–1184 Senthil Kumar M, Nataraj G, Selvan AS (2017) A comprehensive assessment on the effect of high octane fuels induction on engine’s combustion behaviour of a Mahua oil based dual fuel engine. Fuel 199:176–184 Singh RN, Singh SP, Pathak BS (2007) Investigations on operation of CI engine using producer gas and rice bran oil in mixed fuel mode. Renew Energy 32:1565–1580 Sridhar G, Sridhar PJ, Mukunda HS (2011) Biomass derived producer gas as a reciprocating engine fuel—an experimental analysis. Biomass Bioenerg 21:61–72 Stone R (1992) Introduction to internal combustion engines. MacMillan, USA Taku T, Sujimura Y, Suzuki A (2017) The utilization of hydrogen in hydrogen/diesel dual fuel engine. Int J Hydrog Energy 42(19):14019–14029 Uma R, Kandpal TC, Kishore VVN (2004) Emission characteristics of an electricity generation system in diesel alone and dual fuel modes. Biomass Bio Energy 27:195–203 Yogi Goswami D (1986) A Text book on ‘alternative energy in agriculture’, vol II. CRC Press

Chapter 4

Characteristics of Particulates Emitted by IC Engines Using Advanced Combustion Strategies Akhilendra Pratap Singh and Avinash Kumar Agarwal

Abstract Particulates emission is a common problem for both conventional compression ignition (CI) and spark ignition (SI) engines, and it creates issues related to environment, human health, and engine efﬁciency. For particulate reduction, the use of after-treatment systems/devices has been debated since last two decades; however, cost and system complexity issues are the main hurdles for adaptation of these systems in the engines. Therefore, advanced combustion technologies have been developed to achieve cleaner combustion, especially lower oxides of nitrogen (NOx) and particulates. Most of these advanced combustion strategies are categorized as low temperature combustion (LTC). LTC is a novel combustion technology, in which simultaneous reduction of NOx and particulates can be achieved without affecting the engine performance. LTC strategies include mainly homogeneous charge compression ignition (HCCI), partially-premixed charge compression ignition (PCCI), and reactivity controlled compression ignition (RCCI) combustion. In LTC strategies, early fuel injection provides sufﬁcient time for fuel–air mixing before combustion, or a homogeneous fuel–air mixture is supplied to the combustion chamber, which results in complete absence of fuel-rich regions, leading to lower particulate formation. This chapter discusses all these advanced combustion technologies and describes the effect of different control parameters on particulate characteristics emitted from these strategies. A section including particulate formation mechanism and its structure has been included in this chapter for better understanding of the effects of different parameters on particulate emissions. This chapter presents the current technology status and the future research directions for these technologies so that these combustion concepts can be adapted for developing new generation vehicles.

A. P. Singh (&) Department of Mechanical Engineering, University of Wisconsin-Madison, Madison, WI 53715, USA e-mail: [email protected] A. K. Agarwal Department of Mechanical Engineering, Indian Institute of Technology Kanpur, Kanpur 208016, Uttar Pradesh, India © Springer Nature Singapore Pte Ltd. 2019 A. K. Agarwal et al. (eds.), Advanced Engine Diagnostics, Energy, Environment, and Sustainability, https://doi.org/10.1007/978-981-13-3275-3_4

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Keywords Low temperature combustion Particulates Partially premixed charge compression ignition (PCCI) Reactivity controlled compression ignition (RCCI)

4.1

Introduction

Automobiles play an important role in the transport sector, and most automobiles are powered by the internal combustion (IC) engines. Demand for fuel-efﬁcient IC engines around the world is a desired research outcome due to current problems including limited petroleum reserves and environmental concerns. Previous studies showed that the compression ignition (CI) engines deliver the highest proven thermal efﬁciency compared to other combustion modes in this size range. However, CI engines are a major source of emissions (*80% of total on-road automotive emissions), which cause a signiﬁcant impact on human health, local air quality, and global climate issues (global warming). Particulates, especially nanoparticles (NP, Dp < 10 nm) and oxides of nitrogen (NOx) emitted by CI engines, have emerged as major hurdles for their continuation in megacities. Particulate emissions from CI engines mainly consist of highly agglomerated solid carbonaceous materials and ash, volatile, and sulfur compounds. These toxic species present in particulates are harmful to human health as well as the environment. A large number of epidemiology studies reported a strong relation between increasing vehicular pollution and its negative impact on human health (McClellan 1987; Greenstone et al. 2015). The primary particulate exposure of the human is via inhalation, which leads to subsequent deposition of particulates in human respiratory system, leading to increased occurrence of cardiovascular events. To resolve these issues, researchers have explored several advanced combustion strategies and advanced after-treatment systems. The advanced combustion strategies deliver higher thermal efﬁciencies and signiﬁcantly lower engine-out emissions. Many after-treatment systems such as diesel particulate ﬁlters (DPF), lean NOx trap (LNT), and selective catalytic reduction (SCR) have been used in diesel engines; however, these systems suffer from several issues, including cost and durability issues, which limit their application in the production grade vehicles (Dec 2009). In India, government has announced the Bharat Stage-VI (BS-VI) emission norms, due to be implemented in 2020. In BS-VI, emission limits for diesel engines are very stringent and require large reduction in particulate and NOx emissions simultaneously compared to previous emission norms (Dev et al. 2017). Previous studies showed that such a drastic reduction in NOx and particulates will not be possible only by using high fuel injection pressure (FIP), boost pressure, exhaust gas recirculation (EGR), etc. This will require a combination of both in-cylinder combustion control (using advanced combustion systems) and the use of exhaust gas after-treatment systems. Therefore, many researchers have explored the combined effect of advanced combustion strategies and low-cost after-treatment systems [diesel oxidation catalysts (DOC)] to achieve higher engine efﬁciency and lower emissions (Fig. 4.1).

4 Characteristics of Particulates Emitted by IC Engines Using … Fig. 4.1 Clean diesel engine with a combination of advanced combustion strategies and exhaust gas after-treatment systems

59

Advanced Combustion Strategies

Improved Fuel Efficiency Lower Exhaust Emissions

After-Treatment Systems

4.2

Particulate Composition and Formation

Particulate formation takes place inside the engine combustion chamber. Main steps involved in particulate formation in a diesel engine are described by Eastwood (2008). The particulate formation takes place in ﬁve steps, namely: pyrolysis, nucleation, surface growth, agglomeration, and oxidation. All these steps are shown in the schematic given in Fig. 4.2. In a diesel engine, combustion occurs after injecting the fuel droplets at a high injection pressure, and they penetrate hot compressed air in the combustion chamber. This results in ﬁner spray atomization and fuel vaporization, which are important processes affecting particulate formation. A better fuel atomization results in superior combustion due to more homogeneous fuel–air mixing that leads to the absence of fuel-rich zones inside the combustion chamber. These fuel-rich zones are the main source of particulate formation, leading to incomplete combustion due to oxygen deﬁciency. Incomplete combustion is responsible for soot formation. The deﬁciency of oxygen promotes the occurrence of pyrolytic reactions, which produces pyrolyzed compounds. One of the main products of these pyrolytic reactions is ethyne. Ethyne is the main precursor for the formation of polycyclic molecules. A number of ethyne molecules can make ring-like structures and form polycyclic compounds. These reactions are mainly responsible for production of very small

Fig. 4.2 Schematic of the processes involved in particulate formation

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primary particles called ‘spherules’ in large numbers, which are the main building blocks of the agglomerated particles. Spherules vary in size, but this variation is rather limited. This helps them in combining together to form bigger particles. The combination maybe in the form of a long-chain, or sometimes the ﬁrst spherule joins the last spherule like a chain and folds inward to form a closed spherical particle. This chain of particle formation is almost same for all forms of combustion, or there may be minor difference in these steps. The particulate composition is another interesting topic for research. Although particulate generation from different combustion strategies maybe same qualitatively, their chemical composition maybe different, which directly affects their toxicity to human health (Agarwal et al. 2018; Jain et al. 2017a, b). A typical particulate composition includes elemental carbon (EC), organic carbon (OC), sulfates, and ash, which also includes trace metals (Fig. 4.3). EC is crystalline in structure and mostly forms central part of particulates. It mainly comprises of ‘carbon.’ OC is the organic fraction of the particulates and is of great concern due to its toxicity for the humans. OC includes mostly hydrocarbons originating from the fuel and lubricating oil, which might remain unburnt during combustion. Due to pyrolysis of unburned fuel in the presence of high temperature and pressure, hydrogen atoms get stripped off from the hydrocarbon molecules. Remaining carbon atoms undergo cyclization and form sheet (graphitic) and nanotube-like structures (spherules). Incomplete combustion of fuel and lubricating oil generates hundreds of organic compounds, which form complex organic species, generally known as volatile organic materials. Some of these organic compounds such as polycyclic aromatic hydrocarbons (PAHs), benzene, toluene, ethylbenzene,

Fig. 4.3 Typical particulate composition and its artistic representation (Maricq 2007)

4 Characteristics of Particulates Emitted by IC Engines Using …

61

and xylene (BTEX) are listed as carcinogens (Agarwal et al. 2018). Due to condensation of these volatile organic materials over the solid soot particles, the particulate growth takes place. The presence of signiﬁcant amount of trace metals (Na, K, Mg, Fe, Cu, Cr, Co, Ni, As, etc.) in particulates emitted from diesel engines is another serious concern, which makes them harmful for human health and the environment. Particulate contains transition metals (Cr, Fe, Ni, Co, Cu, Zn, etc.), which may cause more serious health hazards due to the ability of transition metals to catalyze the formation of toxic reactive oxygen species (ROS) that is capable of damaging the cells by oxidizing lipids, proteins, and nucleic acids (Tsai et al. 2000). These trace metals are generated due to pyrolysis of fuel and lubricants, engine wear generated debris, and damage of sealings and other soft materials. Several studies have shown that concentration of trace metals in particulates emitted from diesel engines is higher compared to particulates emitted from gasoline-fueled engines (Agarwal et al. 2013, 2015).

4.3

Low Temperature Combustion (LTC) Strategies for Particulate Reduction

LTC techniques can be characterized as flameless and staged burning of fuel at relatively lower adiabatic flame temperatures compared to conventional CI and SI combustion. LTC offers thermal efﬁciency similar to conventional diesel combustion (CDC) and results in simultaneous reduction of engine-out NOx and particulate emissions without using any after-treatment systems. The LTC strategy has many variants, such as homogeneous charge compression ignition (HCCI), partially premixed charge compression ignition (PCCI), and reactivity controlled compression ignition (RCCI) combustion. All these LTC strategies are characterized by a common feature of flameless combustion due to fuel–air mixing prior to start of combustion (SOC). In spite of having signiﬁcantly lower NOx and particulate emissions from LTC strategies, application of these combustion strategies in IC engines require the use of after-treatment systems. Due to reduced exhaust gas temperatures, these combustion strategies produce higher engine-out unburned hydrocarbons (HC) and carbon monoxide (CO) emissions, which need to be controlled. Their general working principle along with fundamental difference between the LTC derivatives is shown in Fig. 4.4. In last few years, particulate reduction potential of these combustion strategies has gained signiﬁcant attention of researchers; therefore, these combustion strategies can be effectively used to displace CDC. In CI combustion, the combustion takes place at relatively leaner fuel–air mixture condition; however, the presence of fuel-rich regions promotes the particulate formation. LTC strategies resolve these issues, eliminate fuel-rich zones from the combustion chamber, and reduce the tendency of particulate formation. However in LTC, particulate formation is not removed completely. The fraction of compounds in fuel that skips the combustion

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GDI: Gasoline direct injection, GCI: Gasoline compression ignition, HTC: High temperature combustion, PFS: Partial fuel stratification Fig. 4.4 Comparison of various conventional and advanced combustion strategies (Wagner et al. 2014)

and the compounds formed due to incomplete combustion makes up the organic particulates. The products formed due to partial combustion give a stronger contribution to the overall composition of organic particulates. According to Kittelson and Frankline (2010), the formation of diesel particulates very much depends on dilution conditions, especially dilution rate and dilution temperature. The formation of particulates is favored by low carbon concentration in the engine exhaust. In all derivatives of LTC, particulate emission characteristics are different because all these combustion strategies differ from each other in terms of in-cylinder charge condition, fuel–air mixing, and combustion mechanism. The next section of this chapter discusses the particulate formation and emission characteristics from all these derivatives of LTC.

4.3.1

HCCI Combustion

HCCI combustion is an advanced LTC strategy utilizing the autoignition of a fully premixed charge. Because of fully premixed homogeneous charge, nearly simultaneous heat release is achieved, resulting in shorter combustion duration.

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By realizing shorter combustion duration, a constant-volume approximation of the Otto cycle is approached, improving the thermal efﬁciency, coupled with reduced pollutant emissions. Due to its excellent fuel–air mixture condition, particulate emissions from HCCI combustion engines are signiﬁcantly lower compared to conventional CI and SI combustion; however, they cannot be neglected because HCCI combustion also emits signiﬁcant amount of relatively smaller particles. This was experimentally veriﬁed by Franklin, who conducted a series of engine experiments using ethanol-fueled HCCI combustion strategy. Franklin (2010) reported that exhaust of a fully premixed HCCI combustion engine was free from accumulation mode particles (AMP, 50 nm < Dp < 1000 nm); however, he found a signiﬁcant amount of nucleation mode particles (NMP, 10 nm < Dp < 50 nm) in the exhaust. Peng et al. (2008) also conducted HCCI experiments using diesel-like fuels and reported HCCI combustion as smoke-free combustion compared to convention CI combustion. Particulates from HCCI combustion were further investigated by Price et al., and they suggested that particulate formation from HCCI combustion could be very sensitive to fuel–air mixing and fuel composition. They reported that particulate mass from HCCI combustion was deﬁnitely lower than CI combustion; however, they were comparable to particulate mass emitted during SI combustion. Agarwal et al. (2013) investigated the physical and chemical characteristics of exhaust particulates emitted by a diesel-fueled HCCI engine. They reported that the emission of particulates from a HCCI engine mainly depends on exhaust gas recirculation (EGR) rate and relative air–fuel ratio (k). As the air–fuel mixture becomes leaner, particulate mass emissions from the HCCI engine decrease, while they increase with increasing EGR rate (Fig. 4.5). They further reported that the engine exhaust predominantly consists of ultraﬁne particles in the accumulation mode. This was due to higher benzene soluble organic fraction (BSOF) of particulates, which increased with increasing k and EGR. It was concluded from the trace metal analysis of soot particles that initially, trace metals were comparatively lower in the particles, but with the application of EGR at higher loads, their concentration increased in the particles. Kaiser et al. (2002) reported that particulate emissions from HCCI combustion engine at moderate loads were signiﬁcantly lower than conventional CI engines but almost equal to direct injection SI engines. Zhu et al. (2013) demonstrated late injection strategy for achieving ethanol–biodiesel-fueled LTC. They also reported a signiﬁcant reduction in particulate emissions from HCCI combustion. Many researchers indicated potential of advanced start of injection (SOI) timing for particulate reduction; however, lack of sufﬁcient fuel–air mixture homogeneity was the limiting factor in achieving HCCI combustion (Mancaruso and Vaglieco 2010).

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Fig. 4.5 Particulate mass emitted from mineral diesel-fueled HCCI engine at different operating conditions (Agarwal et al. 2013)

4.3.2

PCCI Combustion

PCCI combustion is another variant of LTC, which offers better control over combustion events compared to HCCI combustion. PCCI combustion is based on early fuel injection (in-between HCCI and CI combustion) technique, which provides sufﬁcient time for fuel–air mixing before the SOC. The fuel–air mixture homogeneity in case of PCCI combustion is relatively inferior compared to HCCI combustion; however, it is superior to CI combustion. Similar to HCCI combustion, PCCI combustion is also a single-stage combustion process, in which most fuel burns in premixed combustion phase. This results in the absence of diffusion combustion phase, leading to locally higher but overall lower combustion temperatures inside the combustion chamber. This is the main reason for signiﬁcantly lower NOx emissions from PCCI combustion mode. However, similar to other LTC derivatives, PCCI combustion also suffers from the issue of higher HC and CO emissions; however, amount of HC and CO emissions are relatively lower compared to HCCI combustion. Overall, PCCI combustion can be considered as intermediate combustion strategy between HCCI and conventional CI combustion, which offers lower NOx and particulate emissions and provides better control over the combustion phasing.

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It has been established that PCCI combustion engines can operate with lean fuel–air mixtures. Since there are no fuel-rich zones in the premixed fuel–air homogeneous mixture, it prevents formation of particulates. PCCI combustion has been justiﬁed by various researchers (Furutani et al. 1993; Aoyama et al. 1996; Iida 1994; Mancaruso and Vaglieco 2010) using optical diagnostic techniques. Many researchers investigated the particulate characteristics of PCCI combustion and reported that PCCI combustion emits signiﬁcantly lower particulates compared to CI combustion. These studies also showed a dominant effect of different control parameters on particulate characteristics of PCCI combustion engine. Diwakar and Singh (2008) carried out experimental as well as computational analysis of PCCI combustion in a medium-load diesel engine in order to understand the effect of EGR on soot formation. Experiments were performed using 105° angle injector, which validated the simulations done using KIVA-3V code. It was observed that soot formation is increased for EGR rates from 40 to 65% due to lower soot oxidation rates. However, at 70% EGR rate, soot emissions showed signiﬁcant reduction due to lower soot formation. Bittle et al. (2010) suggested that lower soot at high EGR rates was mainly because of greater temperature sensitivity of soot oxidation process compared to soot formation. Ganesh et al. (2016) explored the effect of swirl ratio and various injection parameters on late injection strategy to achieve premixed charge numerically. It was observed that higher swirl ratio (up to a certain limit) led to better combustion and thus reduced the soot emissions. However, if the swirl ratios were too large, then soot emissions would increase due to enhanced heat loss. The effect of injection parameters on emissions from LTC diesel engines reflected the potential of PCCI combustion to simultaneously reduce both NOx and particulate emissions. Several researchers also used multiple injections per cycle and retarded the SOI timing for particulates reduction (Zheng et al. 2009; Fang et al. 2008). Veltman et al. (2009) reported that the use of late injection strategy at higher FIP reduced the particulate emissions from premixed LTC. A combined effect of this optimized injection strategy along with high EGR could be used for simultaneous reduction of particulates and NOx. Zheng et al. (2006) added few other parameters such as higher boost pressure and multiple injections per cycle for lowering the particulate emissions. Zheng et al. (2006) emphasized the threshold temperature limits for soot formation and soot oxidation, which can be achieved by varying the EGR rate. Mancaruso and Vaglieco (2010) used rapeseed methyl ester (RME) at higher FIP to achieve the LTC. They reported lower particulate emissions due to superior fuel atomization and higher oxygen content of the test fuel. The combined effect as lower soot formation and enhanced rate of oxidation were main attributes of their research. Price et al. (2007) contradicted the perception of negligible particulate emission from LTC concept. It was reported that although PCCI combustion produced lesser particulates compared to conventional diesel combustion, particulates from HCCI comprised mostly of AMPs; therefore, they could not be considered negligible. Kittelson and Franklin (2010) also demonstrated that LTC signiﬁcantly reduced carbonaceous particulate emissions from diesel engines; however, particulates from PCCI combustion comprised mostly of volatile compounds in the NMPs, as

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opposed to solid carbon-based AMPs originating from conventional diesel combustion. Desantes et al. (2013) investigated the effect of intake air pressure on number-size distribution of particles. They reported a signiﬁcant reduction in CO, HC, particulate mass, and number emissions with increasing intake air pressure. At higher intake air pressure, the presence of more oxygen inside the combustion chamber was the main reason for this, which improved the soot oxidation in late combustion phase. At higher intake air pressure, relatively lesser fuel deposition in the piston bowl was another reason for lesser CO, HC, and particulate emissions. Jain et al. (2017a, b) carried out detailed PCCI experiments to investigate the effect of different parameters on particulate emissions. They conducted the experiments in a single cylinder engine equipped with common rail direct injection (CRDI) system. They reported that particulate emission from PCCI combustion was signiﬁcantly affected by fuel injection parameters, namely SOI timing, FIP, and EGR (Fig. 4.6). Figure 4.6 shows that advancing SOI timing and increasing FIP reduced the particle number concentration. Increasing FIP shifted the concentration of maximum particles toward smaller particle sizes, which lead to reduction in bigger particles (mainly AMP). They also reported that increasing EGR rate decreased particulate emission; however, too high EGR rate led to signiﬁcantly higher particulate emissions. Jain et al. (2017a, b) also investigated the morphological characteristics of particulates and reported that primary particle concentration increased with increasing FIP (Fig. 4.7). Increasing FIP also increased the organic fraction of

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Fig. 4.7 TEM images of PCCI particulates generated at different FIPs (Jain et al. 2017a, b)

primary particles due to lower exhaust gas temperature, which promoted the condensation of volatile species.

4.3.3

RCCI Combustion

In order to address limited control over ignition timing and HRR of advanced single-fuel combustion strategies, advanced dual-fuel RCCI combustion strategy was developed by Kokjohn and Reitz (ERC, University of Wisconsin). RCCI combustion utilized two fuels having different reactivity (e.g., gasoline and diesel) to control the in-cylinder fuel stratiﬁcation, which allowed optimization of combustion phasing and combustion duration. In RCCI combustion, the low-reactivity fuel was fully premixed with the intake charge, and the high-reactivity fuel was directly injected in the cylinder prior to ignition. However, the direct-injected fuel did not completely mix with air and remained stratiﬁed prior to SOC. The combustion was initiated at high-reactivity fuel–air mixture locations, and subsequent reactions progressed through the fuel reactivity range from high to low reactivity, thereby allowing a controlled sequential ignition event. As a result, by modifying the global fuel reactivity in combination with one or more direct injections, complete control over the combustion phasing and rate of heat release was accomplished. By nature, RCCI is inherently a fuel-flexible advanced combustion strategy. RCCI combustion relies heavily on the fuel reactivity gradient to generate clean and efﬁcient combustion; therefore, a wide range of fuels may be used.

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Particulate emissions from RCCI combustion engines have also been routinely investigated. Kokjohn et al. (2009) suggested that particulates from RCCI combustion were signiﬁcantly lower compared to conventional CI combustion. Splitter et al. (2011) also carried out experiments using RCCI combustion and reported that RCCI combustion engines emitted approximately two order of magnitude lower particulates compared to conventional diesel combustion engines. Figure 4.8 shows the number-size distribution of particles emitted by different combustion modes. This ﬁgure revealed that RCCI combustion emitted lesser particles and most particles were in nucleation size range. Figure 4.9 shows the particulate samples collected on ﬁlter papers from CDC, PCCI, and RCCI combustion engines operated at same speed and load. The particulate mass collected from RCCI combustion was higher than the particulate mass collected from the PCCI combustion. In another research, Jiang et al. (2005) reported that RCCI combustion emitted signiﬁcantly lower particulate mass compared to CDC; however, the SOF in particulates of RCCI combustion was slightly higher. With increasing engine load, SOF of RCCI combustion remained the same; however, SOF of CDC reduced signiﬁcantly. Storey et al. (2017) carried out the TEM analysis of particles emitted from 1.9-L engine (GM) at 2000 r/min, 2.0 bar BMEP, indicating the presence of condensed

Fig. 4.8 Comparison of particulate number-size distribution emitted by conventional CI, diesel PCCI, and dual-fuel RCCI combustion (Prikhodko et al. 2010)

Fig. 4.9 Comparison of ﬁlter-collected samples for conventional diesel (CI), diesel PCCI, and dual-fuel RCCI combustion (Prikhodko et al. 2010)

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HC droplets. This analysis also suggested lack of carbon structure and lack of optical density in the particulate structure of RCCI combustion, compared to typical soot from the CDC. Lack of graphitic structure in particulates generated during RCCI combustion also indicates different particulate formation mechanism compared to CDC. They also performed the chemical characterization (OC/total carbon (TC) ratio) of particulates emitted from CDC and RCCI combustion engines fueled with different fuel pairs. The results showed that RCCI combustion was less sensitive to fuel chemistry; therefore, different fuel pairs did not show any signiﬁcant difference in OC/TC ratios. Although many studies reported lower particulates (based on smoke meter measurements, which showed reduction in black carbon) from RCCI combustion, a detailed study of particulate from RCCI combustion including its chemical characterization needs to be performed so that total particulate mass from RCCI combustion engine can be compared with particulate mass from conventional CI combustion engine. There are several areas such as the health effects of particulates emitted from RCCI combustion, emission characteristics of RCCI combustion equipped with emission control devices, which need to be done before the implementation of RCCI combustion in production grade engines (Storey et al. 2017).

4.4

Conclusions

This chapter presents basic introduction of different LTC strategies, namely HCCI, PCCI, and RCCI combustion and evaluated particulate characteristics emitted from these combustion modes. Continuous research efforts for better control of these combustion strategies showed that these modes are signiﬁcantly affected by different control parameters such as fuel–air mixing, fuel injection parameters, intake charge conditions, EGR levels, fuel reactivities. The results from different studies clearly indicated that advanced combustion strategies emitted signiﬁcantly lower particulates compared to conventional CI combustion. Studies showed that at higher FIP, retarded SOI timing, multiple fuel injections per cycle, higher boost pressure, and EGR were several factors, which reduced particulate emissions from the LTC mode engines. However, particulate emissions increased with increasing EGR rate, and advancement or retardation of SOI timings to achieve LTC, unless combustion temperature was below the threshold limit of soot formation. The particulate characteristics of RCCI combustion indicated a fundamentally different particulate formation mechanism compared to CDC. In RCCI combustion, highly premixed fuel–air charge results in complete absence of localized fuel-rich zones, which was the main cause of particulate formation in CDC. Therefore, serious research efforts to understand particulate formation mechanism from these advanced combustion strategies are needed because these mechanisms affect the particulate emissions as well as particulate composition, which directly affect the toxicological characteristics of these particulates.

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References Agarwal AK, Singh AP, Lukose J, Gupta T (2013) Characterization of exhaust particulates from diesel fueled homogenous charge compression ignition combustion engine. J Aerosol Sci 58:71–85 Agarwal AK, Gupta T, Lukose J, Singh AP (2015) Particulate characterization and size distribution in the exhaust of a gasoline homogeneous charge compression ignition engine. Aerosol Air Qual Res 15(2):504–516 Agarwal AK, Ateeq B, Gupta T, Singh AP, Pandey SK, Sharma N, Agarwal RA, Gupta NK, Sharma H, Jain A, Shukla PC (2018) Toxicity and mutagenicity of exhaust from compressed natural gas: Could this be a clean solution for megacities with mixed-trafﬁc conditions?. Environmental Pollution 239:499–511 Aoyama T, Hattori Y, Mizuta JI, Sato Y (1996) An experimental study on premixed-charge compression ignition gasoline engine. SAE Technical Paper 1996; 960081 Bittle JA, Knight BM, Jacobs TJ (2010) Investigation into the use of ignition delay as an indicator of low-temperature diesel combustion attainment. Combust Sci Technol 183:138–153 Dec JE (2009) Advanced compression-ignition engines—understanding the in-cylinder processes. Proc Combust Inst 32(2):2727–2742 Desantes J, Benajes J, García-Oliver JM, Kolodziej CP (2013) Effects of intake pressure on particle size and number emissions from premixed diesel low temperature combustion. Int J Engine Res 2013. 1468087412469514 Dev S, Chaudhari HB, Gothekar S, Juttu S, Walke NH, Marathe NV (2017) Review on advanced low temperature combustion approach for BS VI. SAE Technical Paper 2017; 2017-26-0042 Diwakar R, Singh S (2008) NOx and soot reduction in diesel engine premixed charge compression ignition combustion: a computational investigation. Int J Engine Res 9(3):195–214 Eastwood P (2008) Particulate emissions from vehicles. SAE International Fang T, Lin YC, Tien MF, Lee CF (2008) Reducing NOx emissions from a biodiesel fueled engine by use of low-temperature combustion. Environ Sci Technol 42:8865–8870 Franklin L (2010) Effects of homogeneous charge compression ignition (HCCI) control strategies on particulate emissions of ethanol fuel. Ph.D. dissertation, University of Minnesota. (http:// hdl.handle.net/11299/99777) Furutani M, Ohta Y, Komatsu K (1993) Onset behavior of low-temperature flames caused by piston compression. JSAE Rev 14(2):12–18 Ganesh V, Deshpande S, Sreedhara S (2016) Numerical investigation of late injection strategy to achieve premixed charge compression ignition mode of operation. Int J Engine Res 17(4):469– 478 Greenstone M, Nilekani J, Pande R, Ryan N, Sudarshan A, Sugathan A (2015) Lower pollution, longer lives: life expectancy gains if India reduced particulate matter pollution. Econ Polit Wkly 1(8) Iida N (1994) Combustion analysis of methanol-fueled active thermo-atmosphere combustion (ATAC) engine using a spectroscopic observation. SAE Technical Paper 1994; 940684 Jain A, Singh AP, Agarwal AK (2017a) Effect of fuel injection parameters on combustion stability and emissions of a mineral diesel fueled partially premixed charge compression ignition (PCCI) engine. Appl Energy 190:658–669 Jain A, Singh AP, Agarwal AK (2017b) Effect of split fuel injection and EGR on NOx and PM emission reduction in a low temperature combustion (LTC) mode diesel engine. Energy 122:249–264 Jiang H, Wang J, Shuai S (2005) Visualization and performance analysis of gasoline homogeneous charge induced ignition by diesel. SAE Technical Paper 2005; 2005-01-0136 Kaiser EW, Yang J, Cuip T, Xu N, Maricq MM (2002) Homogeneous charge compression ignition engine-out emissions-does flame propagation occur in homogeneous charge compression ignition? Int J Engine Res 3(4):185–195

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Kittelson DB, Franklin L (2010) Nanoparticle emissions from an ethanol fueled HCCI engine. In: Center for Diesel Research Department of Mechanical Engineering, University of Minnesota, Presented at Cambridge particle meeting Kokjohn S, Splitter D, Hanson R, Reitz RD (2009) Experiments and modeling of dual-fuel HCCI and PCCI combustion using in-cylinder fuel blending. SAE Technical Paper 2009; 2009-01-2647 Mancaruso E, Vaglieco BM (2010) Optical investigation of the combustion behavior inside the engine operating in HCCI mode and using alternative diesel fuel. Exp Thermal Fluid Sci 34 (3):346–351 Maricq MM (2007) Chemical characterization of particulate emissions from diesel engines: a review. J Aerosol Sci 38:1079–1118 McClellan RO (1987) Health effects of exposure to diesel exhaust particles. Annu Rev Pharmacol Toxicol 27(1):279–300 Peng Z, Zhao H, Tom M, Laddomatos N (2008) Characterization of premixed homogeneous charge compression ignition (HCCI) diesel combustion and emissions. Combust Sci Technol 177:11 Price P, Stone R, Misztal J, Xu H, Wyszynski M, Wilson T, Qiao J (2007) Particulate emissions from a gasoline homogeneous charge compression ignition engine. SAE Technical Paper 2007; 2007-01-0209 Prikhodko VY, Curran SJ, Barone TL, Lewis SA, Storey KC, Wagner RM, Parks JE (2010) Emission characteristics of a diesel engine operating with in-cylinder gasoline and diesel fuel blending. SAE Technical Paper 2010; 2010-01-2266 Splitter D, Hanson R, Kokjohn S, Reitz RD (2011) Reactivity controlled compression ignition (RCCI) heavy-duty engine operation at mid-and high-loads with conventional and alternative fuels. SAE Technical Paper 2011; 2011-01-0363 Storey JM, Curran SJ, Lewis SA, Barone TL, Dempsey AB, Moses-DeBusk M, Hanson RM, Prikhodko VY, Northrop WF (2017) Evolution and current understanding of physicochemical characterization of particulate matter from reactivity controlled compression ignition combustion on a multi cylinder light-duty engine. Int J Engine Res 18(5–6):505–519 Tsai FC, Apte MG, Daisey JM (2000) An exploratory analysis of the relationship between mortality and the chemical composition of airborne particulate matter. Inhalation Toxicol 12:121–135 Veltman MK, Karra PK, Kong SC (2009) Effects of biodiesel blends on emissions in low temperature diesel combustion. SAE Technical Paper 2009; 2009-01-0485 Wagner R, Curran S, Dempsey A, Sluder S, Splitter D, Szybist J, West B (2014) ORNL advanced combustion research and future fuel opportunities. In: Saudi Aramco workshop “future of transport fuels”, Mar 2014 Zheng M, Mulenga MC, Reader GT, Wang M, Ting DSK (2006) Influence of biodiesel fuel on diesel engine performance and emissions in low temperature combustion. SAE Technical Paper 2006; 2006-01-3281 Zheng M, Wang M, Reader GT, Mulenga MC, Tjong J (2009) An improvement on low temperature combustion in neat biodiesel engine cycles SAE. Int J Fuels Lubr 1(1):1120–1132 Zhu H, Bohac SV, Nakashima K, Hagen LM, Huang Z, Assanis DN (2013) Effect of biodiesel and ethanol on load limits of high-efﬁciency premixed low temperature combustion in a diesel engine. Fuel 106:773–778

Part III

Emission Control Techniques and After-Treatment Systems

Chapter 5

Modelling and Experimental Studies of NOx and Soot Emissions in Common Rail Direct Injection Diesel Engines J. Thangaraja and S. Rajkumar

Abstract Diesel engines have sustained with stringent emission limits and increased power demands due to the advancement in the fuel-injection systems. The injection process plays a major role in diesel engine combustion. In this regard, the common rail injection system has the potential of providing flexibilities in injection pressure and timing over a wide range of engine operating conditions. Common rail system is one of the modern variants of electronically controlled injection systems and offers flexibility in injection scheduling with sharp start and cut-off in injection process. It is reported that while the pilot injection is capable of reducing the initial rate of heat release and hence the NOx emission, the post injection enhances the rate of air fuel mixing in the later stages of combustion which promotes soot oxidation. Hence, multiple-injection offers simultaneous reduction of NOx and soot emission. However, the reduction in NOx and soot emission depends on the judicious selection of the multiple injection schedules which comprise injection timing, fuel quantities in each pulse and the intervening dwell between the pulses for a given engine at a particular operating condition. This necessitates a great deal of parametric investigations to analyze the performance and emission characteristics. In this regard, modelling of diesel engines serves as a beneﬁcial tool for the ﬁrst order design estimates by avoiding exhaustive experimental works. Hence, this article addresses both the modelling and experimental investigations on CRDI engines. Further, this study highlights the effect of biofuel and their blends on NOx and soot emission of a common rail direct injection diesel engine. The potential of alcohols as oxygenated additives for realizing the emission reduction is also covered in this chapter.

J. Thangaraja (&) Department of Mechanical Engineering, Vellore Institute of Technology, Vellore 632014, India e-mail: [email protected] S. Rajkumar Department of Mechanical Engineering, SSN College of Engineering, Chennai 603110, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2019 A. K. Agarwal et al. (eds.), Advanced Engine Diagnostics, Energy, Environment, and Sustainability, https://doi.org/10.1007/978-981-13-3275-3_5

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Keywords Common-rail diesel engine Electronic diesel control Electronic management system Oxides of nitrogen Biofuels Soot

5.1

Introduction

Energy is the key prime mover of economy of a country involving fossil based resources. The primary regulated emissions such as NOx, CO, HC, PM and the secondary greenhouse gas impacts are the two issues related to the energy-air pollution nexus. Fossil fuels provide the energy (*95%) towards the world’s transportation. Global carbon emissions from fossil fuel combustion have signiﬁcantly increased and the CO2 emissions have risen about 90%. In the European Union (EU), all new passenger cars registered from 2012 must adhere to an average CO2 emission of 130 g/km. Since fossil based fuels result in emission of carbon dioxide, biomass based fuels are proposed as future fuels to mitigate the release of CO2. Further, EU has a 10% target share for the utilization of renewable energy in the transportation fuels by 2020 (Giuntoli 2018). Table 5.1 provides the details about the future target of renewable energy by other countries. The automobile industry plans to increase the production of diesel-powered passenger cars because of their inherent improvement in fuel economy than the gasoline engines. Further unthrottled operation, lean mode of combustion and higher compression ratio with diesel engines make them more efﬁcient compared to SI engines. Though the overall fuel lean combustion of the diesel engine provides lower carbon monoxide (CO) and hydro carbon (HC) emissions, the serious concerns are the emissions of oxides of nitrogen (NOx) and particulate matter (PM). PM or soot emissions from the diesel engines are one of the major environmental concerns these days with increasing vehicle density on roads. These pollutants of diesel engine are controlled by several methods. The two possible approaches used for this purpose are either affecting the engine design changes to control the formation of these emissions or treating them in the exhaust using after treatment techniques. The engine design changes include enhancement of air motion/ in-cylinder turbulence, increasing the injection pressure, turbo charging, exhaust gas recirculation (EGR) and tuning of injection timing etc. Consequent to introducing features like air motion augmentation by port and combustion chamber conﬁguration (Allan and Khin 1970), multi-hole injection (Iwata 1897), turbo charging (Watson 1984) etc., the direct injection diesel engine improved its

Table 5.1 Future target for the usage of renewable energy in road transport fuels (García-Olivares et al. 2018; Giuntoli 2018) Countries

Brazil, Japan, Indonesia

China

United States

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5

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operating speed range and made inroads to automotive applications. There have been several strategies evolved involving fuel injection system modiﬁcations, combustion system development and use of after treatment devices to meet the stringent emission norms enforced from time to time. However, these measures did not alter the NOx-soot trade-off considerably since in these techniques, the reduction of NO emission led to increase in soot and vice versa. Hence, the trade-off between NOx-soot of the diesel engines remained unresolved and conventional diesel engine with mechanical type injection systems reached a point of no returns in terms of emission characteristics improvements. Because, in conventional injection system, the fuel injection rate increases rapidly to a maximum and ends after certain duration depending on the engine operating load. At closure of the injection, the fuel dribble is found to be a common occurrence which contributes to hydrocarbon and soot emissions. The stricter demands on NOx and soot concentrations from diesel engine remained a challenge till the advent of electronics for engine control. The electronically controlled fuel injection system offered the vital flexibility in fuel injection schedule (injection rate and pattern) and with a sharp cut-off (Nishizawa et al. 1987). Besides the accurate injection pressure and timing, this system also made implementation of split/pilot injection comfortable and hence it became satisfactory approach to NOx control (Oblander et al. 1989). In some cases, pilot injection pulse even resulted in soot reduction (Shimada et al. 1989) and hence paved way for flexible injection scheduling (Needham and Bouthenet 1993) to control NOx and particulate emissions simultaneously. During development phase, the electronic injection control evolved in several stages through use of solenoid actuator (Komiyama et al. 1984), electronically controlled mechanical injection system with spill ring (Shinoda et al. 1986) and in-line fuel injection pump (Schwartz 1985). However none of these modiﬁcations met the requirements of an ideal injection process. The idea of electronically controlled common rail fuel injection system proposed by Miyaki et al. (1991) followed by successful demonstration of its flexibility in controlling injection timing, fuel quantity and injection rate at high injection pressures (Needham et al. 1990) brought common rail direct injection (CRDI) technology into fore. The majority of passenger cars, commercial vehicles, agricultural and industrial tractors are equipped with distributor fuel injection pumps. Figure 5.1 shows the comparison between the mechanical (distributor) type and common rail injection system. Apart from the increase in mean effective pressure with the common rail system, it is possible to achieve higher injection pressure even at lower engine speeds along with the flexible injection timing (Stumpp and Ricco 1996). It is one of the modern variants of electronically controlled injection systems and offers flexibility in injection scheduling with sharp start and cut-off in injection process. It is reported that while the pilot injection is capable of reducing the initial rate of heat release and hence the NOx emission, the post injection enhances the rate of air fuel mixing in the later stages of combustion which promotes soot oxidation. Hence, multiple-injection reduces NOx and soot emission simultaneously. However, the reduction in NOx and soot emission depends on the multiple-injection schedules in terms of injection time, fuel quantities and the intervening dwell period

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Fig. 5.1 Injection pressure map for a mechanical and electronic injection system (Stumpp and Ricco 1996)

between the injection pulses. This necessitates a great deal of parametric investigations to analyze the performance and emission characteristics. In this regard, modelling of diesel engines serves as a good tool for the ﬁrst order design estimates by avoiding exhaustive experimental works. Hence, this article addresses both the modelling and experimental investigations on CRDI engines. Further, this study highlights the effect of biofuel and their blends on the NOx and soot emission of a common rail direct injection diesel engine.

5.2

Overview and Features of CRDI

A common rail direct injection system operates at constant pressure throughout the period of injection and the control of injection timing is made independent of engine speed (Hoffmann et al. 1997). From historical perspective, the development efforts on common rail began in mid 1980s at Fiat, Italy and intensiﬁed under a project “UNIJET” between Fiat Auto and Centro Ricerche Fiat. The outcome of their efforts showed feasibility of CRDI concept in 1993 and brought out a production version by Bosch in 1997. As shown in Fig. 5.2, common rail injection system comprises high pressure supply pump, common-rail, fuel injectors, various sensors and an electronic control unit (ECU) to control the various engine components. The electronic control unit stores the engine map and controls the entire engine operation as per the preprogrammed mapping. In multiple-injection the total fuel quantity/cycle is suitably split such that a small quantity of fuel is injected as pilot

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Fig. 5.2 Schematic of common rail injection system

and the remaining fuel is injected in one or more additional pulses with certain amount of dwell between them. Multiple-injection therefore include pilot, main and post injection pulses. A typical multiple-injection schedule is shown in Fig. 5.3.

5.2.1

State-of-the-Art

The role of fuel injection process is known to be central to diesel engine performance, combustion and emission characteristics. The characteristics of single-pulse conventional and multiple-pulse common rail fuel injection processes are observed to be quite distinct and this section presents the state-of-the-art concerning the

Fig. 5.3 Schematic of multiple-injection schedule

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effects of split and multiple-injection on diesel engine performance, combustion and emissions as: • Split/Double pulse (pilot + main) injection • Multiple pulse (pilot + main + post) injection

5.2.1.1

Double Pulse Injection

The double pulse injection comprises of pilot injection followed by main injection with a dwell period in between. Unlike single injection, double pulse injection schedule has slower combustion during the dwell (Benajes et al. 2001) causing different heat release characteristics in order to affect the trade-off and even simultaneous reduction of NOx and PM (Tow et al. 1994; Pierpont et al. 1995). Due to slower combustion during dwell period, temperature drops until the injected fuel during main injection starts burning. This temperature drop does not occur if the injection is continuous (i.e. single injection). Hence, double injection provides lower NOx emission (Benajes et al. 2001). While higher soot concentration in single injection is due to large extent of rich regions (Hasse et al. 1999), the lower soot concentration in split injection is because of enhanced or additional mixing due to the second pulse (Sperl 2011). Enhanced air utilization and higher combustion temperature in the last combustion phase due to retardation of second pulse are the two major reasons for the soot reduction in split injection (Chen 2000). Pilot injection decreases soot emission also by avoiding the fuel-rich regions and the deterioration of the mixture formation of the main injection (Schmid and Leipertz 2002). The advantages of pilot/split injection are (i) the increased time for oxidizing the soot at high temperature, (ii) the rapid combustion of the later pulses avoid any further soot formation and (iii) enhanced mixing between fuel and air and increased temperature helps in soot oxidation (Hampson and Reitz 1998).

5.2.1.2

Multiple Pulse Injection

Multiple-injection is referred to have pilot, main and post fuel pulses with intervening pilot-main and main-post dwell periods. Several investigators opined that an increase in soot emission (if any) in double injection can be controlled with triple injection technique which avoids the soot producing rich zones at the spray tip (Kuleshov 2006; Badami et al. 2003). It is reported that the triple injection is capable of achieving simultaneous reduction of NOx and smoke (Vanegas et al. 2008). Bakenhus and Reitz (1999) stated that the increase in soot emission can be prevented by multiple fuel injections which interrupt the injection process. This avoids the continuous enrichment of the leading portion of the spray and enhances air utilization. Mobasheri and Peng (2012) argued that the injected after main

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injection ignites rapidly which controls the soot formation in high temperature rich regions. The reduction of soot emission in triple injection is attributed to the air utilization in the later stage of the combustion that helps in soot oxidation. Thus, the post injection is understood to provide additional fuel-air mixing and introduces a new combustion phase wherein the presence of oxygen and higher temperatures favor soot oxidation (Pierpont et al. 1995). Experimental investigations with multiple-injection schedules in the absence of EGR, Tow et al. (1994) and Pierpont et al. (1995) demonstrated that the triple injection schedule provides a higher degree of control over the combustion process. They also observed that the triple injection reduces the NOx and particulate emissions compared to the single injection by signiﬁcantly altering the burning characteristics of the premixed phase.

5.2.2

Strategies to Improve the NOx-Soot Tradeoff

The formation of oxides of nitrogen (NOx) and soot in a diesel engine is attributed to the nature of diesel engine combustion (Plee et al. 1981). In conventional diesel engines, the heat release is known to occur in two major phases—premixed and diffusion. Increasing the mixing rate during the diffusion phase reduces the soot concentration and reducing the heat release rate during the premixed phase reduces the NOx emissions. It has been found that the split and multiple-injection are capable of achieving this strategy. Though the split and multiple-injection is capable of achieving simultaneous reduction of NOx and smoke emission, it does not necessarily always guarantee this advantage until the conditions are optimum. Moreover, the multiple-injection schedule alone is not enough to meet the stricter emission norms (Andreadis et al. 2011) and hence the use of EGR is also practiced. It has been opined that the multiple-injection strategies require a high degree of accurate control over fuel delivery and dwell periods (Pierpont et al. 1995). The following section discusses about various parameter that improve the NOx-soot trade-off.

5.2.2.1

Effect of Start of Injection and Pilot Fuel Quantity

Lee et al. (2009) compared NOx and smoke emissions by varying injection timing, pilot fuel quantity and injection pressure and reported that (i) NOx and smoke emissions decrease with the advance in pilot injection timing, (ii) NOx emission increases with increase in pilot fuel quantity (Lee et al. 2009) and (iii) a signiﬁcant reduction (*60%) in NOx when injection pressure changed from 30 to 140 MPa. Some of the above results are seem to contradict with the majority of the literatures. With higher pilot fuel quantity, there is an increase in the rate of heat release during premixed phase (Badami et al. 2001). It is also reported that NOx emission level can

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be lowered by injecting the large pilot fuel quantities with earlier injection timing (Carlucci et al. 2003). An optimum pilot fuel quantity (*0–30%), can achieve simultaneous reduction of NOx and soot with the added beneﬁts of improved engine performance and combustion noise (Herfatmanesh and Zhao 2014).

5.2.2.2

Effect of Dwell Between Pilot and Main Pulse

In double injection, the dwell between ﬁrst and the second pulse is another signiﬁcant parameter vis-à-vis the main fuel quantity (Tow et al. 1994). In their experiments of Tow et al. (1994), at three quarter of the load, they observed that a longer dwell of 10° CA reduced particulate. Similarly, Yamaki et al. (Yamaki et al. 1994) observed that a short dwell between pilot and main resulted in an increase in smoke and attributed it to the overlapping of main spray on pilot flame. It is important to note that a change in dwell accompanies change in injection timing of main pulse. Increase in dwell time increases the ignition delay of the main flame (Cung et al. 2015). As timing of main injection is advanced in short dwell period conditions, an increase in NOx is reported. However, there is a limit for increase in dwell beyond which soot and CO emissions increase signiﬁcantly (Chen 2000).

5.2.2.3

Effect of Post Injection Timing and Post Fuel Quantity

Yun and Reitz (2007) studied the effect of post fuel quantity on emissions and showed that a small quantity of post pulse is sufﬁcient to achieve lowest particulate emission without any change in NO emission. It is also reported that increasing the post injection quantity at high loads reduces the soot emissions at the cost of BSFC. Higher post fuel quantity increases the temperature during the later part of the cycle which helps in soot oxidation. Payri et al. (2010) suggested an optimum post injection pulse as 7–10% of total mass for full load and 15–20% for partial loads while exploring the advantages of injecting the pilot pulse after main injection. However, the timing of multiple injections is important since too much retardation of post injection from main injection results in an abrupt increase in particulate emission (Pierpont et al. 1995). Like split injection, the reduction of NOx in multiple-injection is observed to be a strong function of injection timing (Tow et al. 1994). In case of post injection occurring too late in the cycle, the cylinder pressure and temperature conditions do not allow the post fuel quantity to burn completely. It is also recommended that early post injection timings should be avoided, thus injecting the post pulse at 40° CA aTDC is desirable for increasing the exhaust gas temperature which helps in soot oxidation. They also reported that a triple injection schedule consists of 8–15% pilot, 74–80% main and 11–12% post injection fuel quantities reduces the NOx and soot emissions up to 40% when compared to double injection case. According to Yun and Reitz (2007), the dwell limits are 1000–2000 µs to avoid fuel injection into an existing soot cloud. The post injection pulse close to the main injection reduces soot emission with an increase in engine efﬁciency.

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Effect of Dwell Between Main and Post Pulse

Tow et al. (1994) observed that (i) there is a signiﬁcant reduction in particulates at 10° dwell between main and post injection and (ii) the injection schedules 32-(2)39-(10)-29 and 32-(7)-33-(10)-35 produced same magnitude of particulate reduction. Hence, they concluded that (i) the particulate emission is more sensitive to a change in the length of the dwell between the second and the third injection than between the ﬁrst and the second, and (ii) only a small post injection pulse is required to increase the particulate oxidation rate late in the cycle. It is observed that (Hampson and Reitz 1998) an increase in both the post injection quantity and the dwell between main and post injection increase the soot emission and fuel consumption. According to Beatrice et al. 2002), the dwell between main and post injection in the presence of swirl allows time for the products of combustion to diffuse out of the spray, but too long dwell may transport the burned products into the subsequent spray. An optimum swirl, injection timing and dwell between the injection pulses provide lower emissions. In this regard, Badami et al. (2003) suggested an optimum dwell period of 400–1200 ls between main and post injection to be the better choice. On either side of these limits there is a substantial increase in soot emission and an increase in CO is also observed when the dwell exceeds 1200 ls. Thus, it is clear that the timing of post injection is an important parameter to achieve reduction in soot emission (Hasse et al. 1999; Badami et al. 2003) which also strongly depends on the dwell period. In order to explore more advantage of multiple-injection strategy comprising pilot, main and post fuel injection pulses, there have been investigations with further splitting of pilot and post injection pulses into two or more pulses with varying intervening dwell.

5.2.2.5

Effect of EGR

The parametric investigations on use of EGR in conjunction with multiple-injection carried out by Pierpont et al. (1995) suggest that unlike single injection, increasing EGR rate with retarded injection timing and triple injection allows NOx to be reduced to very low level without adversely affecting soot emission. It is also opined that the undesirable effects (like increased engine wear) due to EGR may be reduced along with the decrease in particulate emission with the use of multiple-injection.

5.3

Modeling of Diesel Engines

The diesel combustion modeling effort can be dated back to early 1960s when Lyn (1960) proposed a simple triangular heat release hypothesis relating diesel burn rate with the injection process. Since then the art of diesel combustion modeling has

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come a long way using simple to complex modeling strategies for enabling heat release and emission predictions.

5.3.1

Diesel Engine Modeling

In general, the models of diesel engine combustion can be broadly classiﬁed as phenomenological and multi-dimensional. These models are based on the essential physics of engine in-cylinder processes which are governed by the widely validated empirical correlations. Based on the approach used and the mixture preparation details involved, engine models are also termed as thermodynamic models where the First Law analysis is central. Such models evolve in single, two or multi-zone conﬁgurations for which innumerous publications can be found in literature. For brevity, the present discussion will be restricted to the multi-zone phenomenological models which remain popular for their less expensive computational cost and facilitate understanding the influence of complex engine processes prior to detailed multi-dimensional computations. In this regard, the details of multi-zone modeling approaches developed for multiple-injection diesel engines are discussed here.

5.3.2

Development and Features of Phenomenological Modeling

In a multi-zone model, the instantaneous spray region forms with onset of fuel injection. The fuel injected in each time step is considered as a parcel. Each parcel is divided into number of radial zones on equal fuel mass basis. Thus, the multi-zone model has growing number of zones during the fuel injection along with a surrounding region. Figure 5.4 shows a schematic of the multi-zone conﬁguration for multiple-injection case. The spray zones are identiﬁed by the indices i and j, where ‘i’ and ‘j’ refer a parcel and the radial zone respectively. Thus the number of parcels depends on both the duration of injection and the computational time

Fig. 5.4 Schematic of spray evolution (Rajkumar and Sudarshan 2014)

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step. The total number of zones in radial direction for a parcel depends on the zoning features. The model comprises detailed empirical correlation for predicting air entrainment and spray penetration, fuel atomization and evaporation, Ignition and combustion, thermodynamic analysis and emission.

5.3.3

Detailed Modeling of Essential Physics of Diesel Engine Processes

The model formulation considering processes which are responsible for spray growth and fuel-air mixture preparation is based on phenomenological description choosing suitable correlations for various processes involved. For the proposed model development, the major modules include (i) (ii) (iii) (iv)

Air entrainment and spray penetration Fuel atomization and evaporation Ignition and combustion Thermodynamic analysis

While the model salient features are briefed below, the detailed formulation of the model is available in the reference (Rajkumar and Sudarshan 2014).

5.3.3.1

Zoning

A major feature of multi-zone model is to provide spatial distributions of fuel-air by evolving suitable zoning pattern in the spray region. Early model of Hiroyasu and Kadota (1976) included multi-zone structure on the basis of spray cone solid angle. Subsequent comprehensive multi-zone model of Hiroyasu et al. (1983) has been at the core of many phenomenological models to follow. In their formulation, the spray zone comprises of 10 radial zones for each axial time step after spray break-up in the presence air swirl. However, some multi-zone models (Shahed et al. 1975; Dent and Mahta 1981) provide radial distributions using velocity and concentration and proﬁle in an axially growing spray.

5.3.3.2

Air Entrainment and Spray Penetration

The rate of air entrainment into the spray zone in the attached period (during fuel injection) and detached period (during spray plume after the end of fuel injection) are estimated based on spray mixing and turbulent mixing concepts respectively. Spray mixing—The initial fuel injection period is dominantly a liquid fuel and persists till the surrounding air is sucked into the jet to form a spray. This period concerning liquid core is identiﬁed as spray break-up period (tbreak) and evaluated in

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the model following Hiroyasu et al. (1983) who proposed a correlation based on several experiments. Turbulent mixing—At the end of injection, when fuel supply ceases and hence the momentum decays fast. The detached spray from the injector oriﬁce is carried by the swirling air as a plume. In a multiple-injection situation, this occurs for each of the fuel pulse. Since, the rate of air entrainment is no longer governed by this momentum exchange; the spray-swirl interaction is due to the in-cylinder turbulence. The turbulence then plays an important role in the fuel-air mixing and hence, it is opt to model the air entrainment into the spray considering the sources of turbulence. In an earlier work, Dent and Mehta (1981) proposed an approach for turbulent mixing considering available mass of the surrounding charge and the turbulent mixing time.

5.3.3.3

Fuel Atomization and Evaporation

Owing to the atomization process at the injector end, the fuel enters into the engine cylinder, as droplets of different sizes (*few microns) and it is surrounded by hot entrained air. In the spray model, the fuel injected in each time step (say 0.1° CA) is treated as a parcel of droplets whose representative mean diameter can be represented as Sauter mean diameter (SMD). For computation, a parcel is assumed to be a group of droplets with identical properties (like diameter, temperature etc.). The instantaneous evaporation calculations required in the model are carried out on the basis of SMD values of the respective parcels. The instantaneous droplet temperature of the spray zone is calculated from the energy balance for the fuel droplet.

5.3.3.4

Ignition and Combustion

Typically, a diesel engine has three important combustion phases—delay period, premixed combustion phase and mixing controlled combustion phase. Ignition delay—The mixing of evaporated fuel mass and the entrained air in the spray zone elapses a time before it attains a state to ignite and subsequently burn to release energy. The time elapse between the start of injection and the onset of combustion is an important phase which causes ignition delay. Premixed combustion phase—The fuel vapor and air mixed during the ignition delay period produces a premixed charge and its ignitable fraction burns rapidly from the onset of combustion till the fuel amount injected during the delay period is consumed. The burning in this phase is dominantly kinetically controlled and hence the premixed burning is modeled using bi-molecular reaction kinetics based on rate equation (Hiroyasu and Nishida 1989). Mixing controlled combustion phase—After the end of premixed combustion phase, the rate fuel burning depends dominantly on the intermixing of oxygen and fuel on a molecular level scale. Thus the eddy dissipation concept suggested by

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Magnussen and Hjertager (1976) includes mixing time which is evaluated from k-e model is used to predict burning rate.

5.3.3.5

Thermodynamic Analysis

In thermodynamic analysis for two zones viz. spray and surroundings, the estimation of instantaneous values of cylinder pressure (p), temperatures of spray (Tsp) and surrounding (Tsur) and spray volume (Vsp) require setting up the expressions for respective zones concerning their: i. equations of state, and ii. equations of mass and energy conservation. The energy equations consider loss of heat from cylinder walls from respective zones. The solution of these equations is required to satisfy the constraint of total instantaneous volume. The simultaneous solution of four ordinary differential equations under the volume constraint condition gives instantaneous values of cylinder pressure, volumes and temperatures of spray zone and surroundings. Heat transfer—The instantaneous total wall heat transfer (Qht) rates required in the energy equation of the spray and surrounding zones are proportioned from the total wall heat transfer

5.3.4

Emission (NOx and Soot) Modeling

One of the essential purposes of multi-zone modeling has been prediction of emissions to a reasonable degree of accuracy with lesser computational time and storage resources compared to CFD package. The predictions for NO and soot emissions in diesel engine have been primary concerns of most modeling attempts reported in literature. For NO prediction, the extended Zeldovich mechanism (Heywood 1988) has remained undisputed choice in all phenomenological models referred here. However, diesel engine soot modeling evolved from a very simple proposal of Khan and co-workers (Khan et al. 1971; 1973) put forward in early 1970s. They (Khan et al. 1971; 1973) proposed an overall rate equation in terms of equivalence ratio (Ø) and thermodynamic conditions. Recently, soot formation models are proposed considering multi-process single step to multi-process multi-step schemes involving considerations of physical processes such as radical precursor formation, particle inception, particle growth, acetylene formation, particle coagulation etc., with their single or multi-step rate kinetics. The other important stage of soot modeling includes the precursor formations and soot oxidation. In a multi-step process soot model proposed by Fusco et al. (1994), all important steps covering the soot formation and oxidation are included. The oxidation model of Nagle and Strickland-Constable (1962) has been popularly utilized

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for estimating the soot oxidation. The eight step model of Fusco et al. has been found effective in multiple-injection study (Liu et al. 2005) as compared to the simple two-step soot model. Tao and co-workers (Tao et al. 2005, 2006) proposed a nine step model by adding an additional step on Fusco’s scheme with the consideration of soot oxidation due to OH radical.

5.4

Model Validation and Parametric Studies

In order to analyze the effect of multiple injection schedules on combustion and emission characteristics of CRDI engine, several modeling works are proposed in the literature. While, the application of phenomenological models on single injection is well demonstrated, their use on multiple-injection is limited.

5.4.1

Literature Review

For split and multiple-injection case, a multi-zone model is used by Li et al. (1996) to study the effects of split injection schemes on NO and soot emissions in a direct injection diesel engine. They validated the model at 50 and 100% load at 1500 rpm. The model considers the formation of spray zone without any radial zone. In their model, a characteristic time scale determines the burn rate. The ignition delay and conservation equation of mass and energy are solved at each axial instant. They predicted NO with extended Zeldovich mechanism and soot emission by two step Hiroyasu et al. (1983) equations. In a two-zone phenomenological model of Barba et al. (2000) for combustion and NO emission, the fuel mass injected during each time step (0.2° CA) is classiﬁed as a package with its own SMD and number of droplets. Though, the evaporation rates are estimated from the respective package, the model calculates a cumulative value of vaporized fuel at any instant to arrive at an instantaneous total mass of evaporated fuel. Thus, the model considers a single value of the vaporized fuel mass that represent the total amount of gaseous unburned fuel mass in the cylinder. While their formulation for premixed burning of the mixture is based on the turbulent flame velocity suggested by Damkoehler, the mixing controlled combustion model is based on mixing frequency concept. The mixing frequency depends on characteristic mixing velocity and a characteristic mixing length which are obtained from a turbulence model. For the multiple-injection, there are few other multi-zone models available in literature (Kuleshov 2006; Rether et al. 2010). Kuleshov (2006) divided the spray into 7 characteristic zones for evaporation and burning rate estimations. The fuel distribution details are related to the injection proﬁle through correlations. The fuel evaporation estimations are made on zonal SMD basis and cumulative value of vaporized fuel mass in each zone is arrived. In this multiple-injection model, combustion of every injected portion of fuel is simulated separately by considering

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the air-fuel ratio of each portion. Rether et al. (2010) proposed a quasi-dimensional model for multiple pilot and post injection strategies based on turbulent flame velocity and characteristic mixing length concept to evaluate the rate of burning during premixed and mixing controlled combustion phases respectively.

5.4.2

Parametric Investigation

Rajkumar and Sudarshan (2014) carried out parametric investigations through their validated multi-zone model to study the effect of pilot fuel quantity and dwell period on combustion and NO and soot emissions. In this analysis, the injection schedule D1 of Nehmer and Reitz (1994) is taken as a base case. The quantity of pilot fuel is varied as 10, 30, 50 and 75% and the dwell period between the two injection pulses is changed as 1, 3, 5, 8 and 10° CA. An increase in NO emission is observed with increase in pilot fuel quantity, while it decreases soot emission (for the pilot fuel from 10 to 25%) at all dwell periods. The increase in dwell between pilot and main injection decreased the soot emission. However, soot emission increases when the dwell period exceeds 8° CA. Thus the shorter dwell (1 and 3°) and longer dwell (10°) produced more soot emission and the soot emission is observed to be the minimum at 8° CA dwell. A similar investigation is carried out on Ford engine by Rajkumar (2013). A double injection schedule of 20 (22) 80 at 2000 rpm and 40% load is taken as the operating condition. The pilot fuel quantity is changed from 16 to 24% and the dwell period between pilot and main injection is varied from 18 to 26°. Figures 5.5 and 5.6 shows the effect of pilot fuel quantity and dwell period on NO and soot emissions respectively. From Fig. 5.5, it is observed that the increase in pilot fuel quantity increased NO emission and decreased soot emission at all the dwell periods tested. Figure 5.6 shows that increase in dwell decrease both NO and soot emission. However, the soot emission is observed to be the lowest at 20° CA for 16 to 22% pilot fuel quantity and at 22° CA for 24% pilot fuel quantity. The requirement of an optimum dwell for the reduction of soot (as observed in the previous case) is widely reported in several experimental works (Tow et al. 1994; Chen 2000; Badami et al. 2003).

5.4.3

Summary

The Multiple-injection strategies in CRDI engines helped resolving NOx-soot trade-off problems in diesel engine through improvement of the combustion process. Simultaneous reduction of NOx and soot and smoother combustion in such engines are observed to depend on the quality (metering, timing and stability) of the pilot injection, additional post injection after the main injection pulse and dwell between these pulses and, the timing of multiple-injection. The post injection is

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Fig. 5.5 Effect of pilot fuel quantity on NO and soot emission

understood to provide additional fuel-air mixing that introduces a new combustion phase to provide higher temperature for soot oxidation. However, the multiple-injection strategies need high degree of control over the injection process. Hence, the multiple-injection scheduling is central to realize the advantage of CRDI engine technology and hence, proposing an injection schedule for a given engine at a particular operating condition needs exhaustive analysis and parametric investigations on the combustion and emission characteristics of these engines. Arriving optimal injection schedules through engine test bed experiments is cumbersome, expensive and time consuming. In present times, this exercise is often complemented by a simple to complex computational models. In this context, the

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Fig. 5.6 Effect of dwell on NO and soot emission

phenomenological engine modeling serves a cost effective purpose for the engine ﬁrst order design estimates and becomes a tool for analyzing the essential insights that affect the combustion and emission processes of multiple-injection common rail direct diesel engines.

5.5

Impact of Biofuels

The choice of biofuel has been the major alternative to fossil diesel and numerous research works with biofuels and diesel blends has been carried out in CRDI systems. This section discusses the impact of biofuel on NOx and soot emission

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characteristics of CRDI engines and is further categorized into three sub sections, wherein the ﬁrst section comprises the effect of change in fuel characteristics on NOx, soot emission followed by the second section bears the impact of engine response and the last section outlines the potential measures to control them.

5.5.1

Effect of Fuel Response

The influence of oxygen content, ratio of carbon to hydrogen atom, changes in fuel properties with composition are identiﬁed as the fuel effects. Described below are the studies which associated with the effects of these biofuel properties upon the change in NOx and soot emissions in CRDI engines. Hwang et al. (2014) experimentally evaluated the effects of the injection pressure (80 and 160 MPa) and the timing (−25 to 0° CA aTDC) on the combustion and emission characteristics in a single-cylinder CRDI diesel engine fuelled with waste cooking oil biodiesel and fossil diesel. The results showed that the biodiesel had beneﬁts in reduction of smoke, CO, HC emissions, with a penalty in the NOx emissions under all experimental conditions. The authors opined the presence of fuel bound oxygen in biodiesel to be the primary factor for the reduction of smoke and the increase in NOx emissions. However, it has to be noted that these experimental results were obtained with the single injection strategy. Similarly, Lee et al. (2005) investigated the effect of biodiesel blends on the emission characteristics of a CRDI engine for a constant fuel quantity of 8 mg, rail pressure at 100 MPa and at a constant speed of 1000 rpm. Biodiesel derived from unpolished rice and soybean is blended with fossil diesel with the mixing ratios of 10, 20, and 40% by vol. The authors observed that the peak injection rate becomes lower as the mixing ratio of the biodiesel increases and they attributed it to the higher viscosity of the biodiesel fuels. Hence they recommended to increase the injection pressure, while operating biodiesel blended fuels. Further, the NOx emission increased with increase in biodiesel blends and the authors ascribed it to the presence of fuel bound oxygen. Recently, Alptekin (2017) conducted an experiment in a CRDI engine to analyze the injection, combustion and emission of diesel, biomix of 50% of canola and 50% safflower biodiesel with oxygenates (solketal and ethanol). The test engine used in this study has a closed ECU control and operates with split injection strategy, viz. pilot and main injection. The authors reported a change in the injection characteristics (SOI, EOI and duration) for the test fuels based on fuel type/blends due to the change in response with the engine management system (EMS). Interestingly, both pilot and main injection duration of neat biodiesel and oxygenated blends were lower than those of fossil diesel. However, higher oxygen content and excess air factor caused an increase in NOx emissions (>24%) with biodiesel and oxygenated blends. Tziourtzioumis and Stamatelos (2012) demonstrated the response of engine management system to the difference in fuel properties, viz. B70 biodiesel blend in both steady state and transient test conditions. Fatty acid methyl ester produced from a mixture of 40% rapeseed oil, 30% soybean oil and 30% recycled cooking

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oils is employed in this study. The test results revealed a signiﬁcant difference in the fuel system dynamics with the B70 blend at high load conditions. An increase in the common rail pressure (*100 bar) is reported with the operation of B70 blend and the authors attributed it to the lower heating value of biodiesel, which caused a higher fuel delivery to be injected into the engine cylinder. The authors proposed to enhance the fuel delivery by 10% for higher biodiesel blends in the ECU map to compensate the loss in engine performance. Regarding the injection system dynamics, the higher viscosity of B70 is described to cause an increased degree of damping. To improve the transient performance of CRDI engines operating with higher concentration of biodiesel blends, Tziourtzioumis and Stamatelos proposed certain modiﬁcations to the ECU maps and control parameters. Yadav and Ramesh (2018) experimentally evaluated the injection strategies to reduce the smoke emission of a butanol diesel dual fuel common rail automotive engine. The authors highlighted that the main plus post strategy is effective in improving the energy efﬁciency, reducing smoke emission in a dual fuel engine. Because of the charge cooling effect, the NO emission reduced drastically with the introduction of butanol due to the higher latent heat of vaporization of butanol. Further, the NO level with post injection was lower than the single pulse injection (SPI) of diesel which was attributed to the lower peak temperatures as compared to SPI of diesel. Among the various injection strategies, the authors recommended the main plus post injection strategy to be the most beneﬁcial strategy in terms of engine performance and emissions for dual fuel operation with butanol and diesel fuel (refer Fig. 5.7). Duda et al. (2018) compared the performance and emission characteristics of a CRDI diesel engine fuelled with biodiesel of animal origin, viz. swine lard, turkey lard and rapeseed oil. The engine experiments were conducted with single and two pulse (pilot + main) injections for blends of 75% of biodiesel and 25% of diesel by

Fig. 5.7 NO versus Smoke tradeoff for different injection strategies and butanol to diesel energy share (BDES) (Yadav and Ramesh 2018)

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vol. All the tested blends (B75) caused an increase in NOx emission (*7%) and decreased the smoke opacity, which was accounted to the elevated combustion temperature and higher oxygen content in the fuel. In the case of split injection, the NOx emission increased for all the biodiesel blends at all load conditions. However, the single injection yielded marginally lower NOx emission at part load conditions. Further, the authors encouraged B75 as a desirable blend for CRDI engines with a marginal penalty in the engine performance parameters. Choi and Reitz (1999) carried out an experimental study to determine the effects of oxygenated (ethers, esters) fuel blends and multiple injection strategies on a single cylinder heavy duty diesel engine. Blends of 20 and 40% by volume of methyl soyate with diesel were used as the biodiesel blends and mixtures of 7.8 and 15.6% by volume of tri-propylene glycol mono methyl ether with diesel were used as the ether blends. Both high and low engine operating loads were evaluated following the split injection strategy of 50(0.97)50 for high load condition and the low load split injection strategy of 61(1.18)39. For the split injection strategy at high load conditions, a signiﬁcant reduction in particulate for all the tested fuels with a marginal change in the NOx emissions was reported. However, at low load conditions, the biodiesel blend exhibited a lower particulate emission compared to the ether blend with split injection strategy. Further reduction in particulate is possible by the combination of split injection with advanced SOI timings. Further, split injection had the beneﬁt of reducing NOx at the retarded injection timings. Hence, it is clear that the multiple-injection has the potential of reducing NO emission from biodiesel fuel unlike conventional diesel engine where in general increase in NO emission is reported with biodiesel fuel. Changes in fuel properties are crucial in this respect and demands further research. The variations in injection parameters, engine calibration and operating conditions are identiﬁed as engine response/effects and discussed in the following section.

5.5.2

Effect of Engine Response

Engine response effects are those that alter the operation of system components due to changes in the fuel property, which correspondingly alter the combustion and emission formation. The higher bulk modulus with biodiesel is a prime example of engine response, wherein the inadvertent advance in start of injection occurs in mechanically operated injection system. However, the biodiesel-bulk modulus effect is less prevalent in CRDI engines afﬁrm that such effects are engine response. ECU calibration is generally executed with fossil diesel, concomitantly running with biofuel can result in operational changes that affect the emission—either increase/decrease/similarity is possible. Thangaraja et al. (2014) examined the biodiesel injection characteristics in an automotive diesel engine with a common-rail direct-injection system. The experiments were conducted on Ford diesel engine consisting of an electronically controlled piezo injector with a maximum injection pressure of 100 MPa. The

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Fig. 5.8 Comparison of pilot and mail injection timings for diesel and karanja-diesel blends (Thangaraja and Anand 2014)

start-of-injection timing is obtained from a KiBox combustion analyzer with the help of a current clamp adapter (E3 N clamp). The pilot and main injection timings for diesel and the 50% Karanja biodiesel–50% diesel blend are shown in Fig. 5.8, which conﬁrms insigniﬁcant changes in both the pilot (see Fig. 5.8a) and the main injection timings (see Fig. 5.8b) at all the tested conditions. The authors concluded that the unaltered system response viz. injection timings yielded similar NO emission with B50 as that of fossil diesel. However, the advantage in smoke emissions was observed in their study (refer Fig. 5.9). Senatore et al. (2005) performed an experimental study in a CRDI engine and compared the results of neat rapeseed biodiesel (B100), blend of rapeseed (75%) and used frying oil (25%) with fossil diesel. A similar value of NOx concentrations was reported and the authors opined to the unaltered start of injection for the three

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Fig. 5.9 Comparison of smoke emissions for diesel and karanja-diesel blends (Thangaraja and Anand 2014)

fuels. However, the smoke/particulate measurement was not measured/reported in this study. Ye and Boehman (2010) investigated the influence of CRDI injection strategy on the biodiesel-NOx effect with fossil diesel and soybean blended diesel (B40). At constant operating conditions, the higher fuel consumption with biodiesel blend is reported to cause an increase in the injection pressure and extend the injection duration. The authors opined the NOx change to the increased injection pressure arising out of the engine response. However comparative analysis has shown that variation in injection pressure have a higher impact on NOx emissions than a change in the injection duration. The authors concluded that the NOx emissions of B40 at an injection pressure of 72 MPa were mitigated than that of diesel at 108 MPa and SOI of 7 and 9° bTDC. Further, the NOx emissions were similar at the SOI of 5° bTDC. Suh and Roh (2008) investigated the fuel injection proﬁles and reported similar patterns between the tested fuels in the absence of pilot injection.

5.5.3

Coupled Effects and Control Methods

A comparative analysis with methyl esters of rapeseed, soybean, waste cooked oil and diesel was performed on a common-rail turbocharged engine by Grimaldi et al. (2001). The test runs were performed in steady state mode according to the European13-Mode test cycle without change in ECU injection timing and absence of EGR. A signiﬁcant reduction in smoke emission for all biodiesel fuels in respect to fossil diesel fuel was reported. However, at full load conditions, the speciﬁc NOx emissions increased about 17% with the biodiesel fuels. The authors did not indicate the reason for these changes. Mikulski et al. (2016) conducted an

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experiment in a CRDI diesel engine with swine lard methyl esters–diesel mixture concentration up to ester blend of 75% by vol. Accumulator injection system (Common Rail) CR2.0 developed by Bosch was employed for their study. A maximum increase of NOx emissions by 22% were observed for the B75 mixture at 1500 rpm. However at high speed conditions (3000 rpm), the NOx emissions did not exceed 5% for the B75 mixture. The authors justiﬁed this trend to the change in cylinder pressure conditions and physico-chemical properties of fuel. The smoke opacity levels reduced by 73 and 89% on average at 1500, 3000 rpm respectively with the ester component in the fuel mixture (B75). Senatore et al. (2006) calibrated an electronic control unit (ECU) to optimize the performance and emissions of a light duty CRDI engine fuelled with rapeseed methyl ester (B100) and diesel blends (B25). Similar torque and power output as that of diesel was achieved with B100 through ECU optimization, by increasing the biodiesel quantity per cycle. However, the NOx emissions of B100 with the standard ECU setting for two engine speeds (1500 and 2000 rpm) were higher and consequently mitigated by varying the start of injection and introducing EGR as shown in Table 5.2. Hence, for various fuels, the authors recommended ECU modiﬁcations which are relevant to CRDI engines. How et al. (2018) recommended multiple split injections strategy for the simultaneous reduction of NOx and smoke emissions. The authors carried out an experimental study with coconut blended diesel (B20, B50) and observed a decrease in NOx emissions across the tested injection strategies which was attributed to the increase in cetane number and reduction in caloriﬁc value of the biodiesel blends in comparison with fossil diesel. Further with triple injection scheme, a considerably lower NOx emission (*100 ppm) was attained by utilizing late SOI timing. However, the triple injection yielded slightly higher smoke emission with earlier SOI timings. For concurrent reduction in NOx and smoke emission with B50, the authors recommended the combined strategy of retarded SOI timing and triple injection. Effect of biodiesel on NOx emissions in single and multi-cylinder CRDI engines are summarized in Table 5.3.

5.5.4

Summary

While this section is not able to nail the particular reason for the change in NOx emissions between biodiesel and fossil diesel, it has provided the major factors Table 5.2 NOx (ppm) emission with diesel and biodiesel at 2000 rpm and 12 bar BMEP (Senatore et al. 2006) Diesel fuel

Biodiesel (ECU standard setting)

Biodiesel (ECU modiﬁed injection)

Biodiesel (ECU modiﬁed EGR)

470

1180

830

460

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Table 5.3 Effect of biodiesel on NOx emission in CRDI engines Author

Fuels

Engine type

Effect on NOax

Remarks

Mangus et al. (2014)

Soybean, jatropha, palm, beef tallow

Lower oxides of nitrogen (BSNOx) was reported with the tested biodiesels

Reported an inverse relationship between NOx and viscosity

Dhar and Agarwal (2015)

Karanja methyl ester blended diesel (B10, B20 and B50) Karanja methyl ester blended diesel (B10, B20 and B50)

Single cylinder common rail direct injection (CRDI) Single cylinder research engine with CRDI system Single cylinder research engine with CRDI system

In the case of split injection strategy, the BSNOx emissions from B10, B20 were higher than fossil diesel For the tested fuel injection pressures (300, 500, 750 and 1000 bar), the NOx emissions of B50 were lower than B20 and B10 and almost similar to fossil diesel. Both similar and higher NOx emissions were reported for B40 at the tested conditions

No soot measurements

Agarwal et al. (2015)

Lee et al. (2017)

Karanja methyl ester blended diesel (B40)

Four cylinder, turbocharged and intercooled CRDI Soriano Blend of 72% Four et al. soybean and cylinder, (2018) 28% palm turbocharged biodiesel by and volume intercooled CRDI Unless mention otherwise aNOx changes are

NOx emission with biodiesel is slightly higher than diesel and opined to the presence of oxygen

Recommended B10 to be the optimal blend for performance and emissions, without the need of ECU recalibration

Change in NOx was attributed to the lower caloriﬁc value, higher cetane number and presence of fuel bound oxygen Maintained the default ECU conﬁguration

referred with respect to fossil diesel

responsible to such changes. However, the biodiesel-NOx penalty could be abated comfortably with the CRDI engines than in the case of mechanically operated diesel engines. Further, the controversies for the biodiesel-NOx penalty tend to vary with different authors. Also, the presence of oxygen in the biodiesel fuel molecule has the favorable soot oxidation process in both the electronically and mechanically operated injection systems.

5.6

Conclusions and Future Scope

This chapter presented the development and experimental investigations on CRDI engines. Due to the advancement of automotive electronics, the flexible injection scheduling with multiple-injection provided much needed solutions to the diesel

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engine speed limitation and NOx-soot trade-off. The multiple-injection scheduling is central to realize the advantage of CRDI engine technology. Thus, the multiple-injection strategies in CRDI engines helped resolving NOx-soot trade-off problems in diesel engine through improvement of the combustion process. It is evident that the simultaneous reduction of NOx and soot and smoother combustion in diesel engines are observed to depend on the quality (metering, timing and stability) of the pilot injection, additional post injection after the main injection pulse and dwell between these pulses and the timings of multiple-injection. Thus the multiple-injection strategy requires sophisticated injection systems to control the injection process over the entire engine mapping. While, the pilot alleviates the rate of premixed burning and hence reduces NOx emission, the post injection is capable of providing an additional fuel-air mixing that introduces a new combustion phase to provide higher temperature for soot oxidation. Hence, for a given engine at a particular operating condition, the judicious selection of the multiple injection schedules comprises the injection timing, fuel quantities in each pulse and the intervening dwell between the pulses helps to achieve the simultaneous reduction of NOx and smoke emissions. The increased NOx emission from biodiesel can be mitigated with multiple-injection strategy in CRDI engines. Thus the future scope to extend the systematic research on biodiesel fuelled engines is attractive. The capability of multiple-injection in achieving low temperature combustion can also be pursued.

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Chapter 6

On-Board Post-Combustion Emission Control Strategies for Diesel Engine in India to Meet Bharat Stage VI Norms Rabinder Singh Bharj, Rajan Kumar and Gurkamal Nain Singh

Abstract Emissions from diesel vehicles are the main concern of air pollution-related deaths worldwide. Its impacts are growing in most of the developing nations especially India, in spite of the regulatory limits. In 2016, the Indian government declared that the nation would skip the Bharat Stage (BS) V norms completely and adopt progressively stringent BS VI norms by 2020 in which the level of nitrogen oxides (NOx) and particulate matter (PM) emissions will be reduced by 89 and 50%, respectively, from BS IV norms. Consequently, the exhaust control technologies will play an important role to achieve these reduced NOx and PM levels. The existing strategies to combat abatement of NOx and PM emissions would not be able to resolve these issues. This chapter provides an insight and suggests the ways and means to achieve BS VI emissions standards by the Government of India.

Keywords Air pollution Diesel engines Emission norms Bharat Stage VI norms On-board diagnostic Exhaust after-treatment strategies

6.1

Introduction

The quality of air is a life-threatening concern worldwide. While air quality is affected by many sources, vehicular emissions are the key source for spoiling the air quality of the numerous Indian cities since the rising income leads to an increase in buying power of the customer which results in more population of vehicles. The environmental protection (air) policies are progressively concentrating on regulation of both fuel quality speciﬁcations and vehicular engine design. India is an agrarian country, so diesel fuel is provided substantial subsidy to promote farming, and therefore, consumers get attracted to diesel fuel in automotive R. S. Bharj (&) R. Kumar G. N. Singh Department of Mechanical Engineering, Dr. B. R. Ambedkar National Institute of Technology Jalandhar, Jalandhar 144011, Punjab, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2019 A. K. Agarwal et al. (eds.), Advanced Engine Diagnostics, Energy, Environment, and Sustainability, https://doi.org/10.1007/978-981-13-3275-3_6

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transport too to realize fuel economy, and thus, it becomes an attractive option for consumers. Despite the attractive fuel economy, high emissions of NOx and PM are the major challenges in diesel engines. Different researchers have found that the NOx and PM emitted from diesel vehicles are the most harmful pollutants, whereas ozone and other air pollutants have minor effects. The combustion of fuel in diesel engines has become a bigger threat to human health than an apparently more visible source of road dust (Dockery et al. 1993; Marrack 1995; Pope et al. 1995). The higher compression ratio in the diesel engine produces higher gas temperature as compared to a gasoline engine, which is responsible for higher diesel NOx emissions. On the other hand, a complex mixture of solid and liquid species produces diesel particulate matter. Due to improper and inadequate mixing of the diesel and air in the engine causes excess fuel which forms carbonaceous soot particles. Considerably, the reduction in these emissions has also been done from past decades. High emissions of NOx and PM are the main target for government emissions reduction policies. Development in a diesel engine in India to meet Bharat Stage VI emission norms and the implementation of renewable fuels is a most challenging research objective. Globally, the automobile industries are continuously doing research to control the emission-related concerns both off-road and on-road diesel vehicles with advanced technologies for cleaner diesel fuels, advanced engine design, fast responding electronic control units, and effective exhaust after-treatments.

6.1.1

Worldwide Scenario of Emission Norms

The G-20 nations account for 90% of worldwide vehicle sales, and 17 out of the 20 nations are obeying the European regulatory pathway to control the vehicle emissions. First European exhaust emissions norm was presented in 1970 for passenger cars. After 22 years, in 1992 the “Euro 1/I” norm heralded the installation of catalytic converters to gasoline cars to decrease the emissions of carbon monoxide (CO). The European regulatory pathway contains six stages of progressively stringent emission control requirements, beginning with Euro 1/I, and progressing through to Euro 6/VI in 2015 (Williams and Minjares 2016). Euro 1, 2, 3, 4, 5, and 6 refer to the emission norms for passenger cars and other light vehicles; however, Euro I, II, III, IV, V, and VI are for heavy-duty vehicles. A number of Asian and Latin American countries are following different emission policy timelines as shown in Fig. 6.1.

6.1.1.1

European Standards History

In July 1992, the ﬁrst Euro emissions standard (Euro 1/I) was introduced only for passenger cars. This emissions standard required new petrol cars to be ﬁtted with catalytic converters to reduce CO emissions. It also marked the switch toward

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Fig. 6.1 Vehicle policy timelines (Source Chambliss et al. 2013)

unleaded petrol. Euro 1 emission limits for petrol and diesel engines are given in Table 6.1. The Euro 2 standard was implemented in January 1996, which further lowered the limit for CO emissions and also lowered the combined limit for HC + NOx for both petrol and diesel vehicles as given in Table 6.1. The Euro 3 standard was implemented in January 2000. In Euro 3 standard, the Urban Driving Cycle (ECE) + Extra-Urban Driving Cycle (EUDC) test procedure was modiﬁed to eliminate the idling period; i.e., engine starts at 0 s and the emission sampling starts at the same time. This modiﬁed cold-start test procedure is known as the New European Driving Cycle (NEDC). Euro 3 further lowered the permitted CO and PM limits for diesel vehicles. For diesel engines, Euro 3 introduced a separate limit for NOx emission; however, for petrol engines it introduced separate limits for HC and NOx emissions as given in Table 6.1. The Euro 4 standard was implemented in January 2005, focused on reducing the emissions from diesel cars, especially reducing PM and NOx emissions as shown in Table 6.1. Few Euro 4 diesel cars were ﬁtted with particulate ﬁlters. For diesel engines, the Euro 5 standard was introduced in two different stages; however for petrol engines, Euro 5 was only governing standard. Euro 5 and Euro 5a standards were implemented in September 2009, while Euro 5b standard was implemented in September 2011. In Euro 5b standard, ﬁrst time, a particle number (PN) emission limit was introduced for diesel engines, to address the effects of very ﬁne particle emissions. This standard further

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Table 6.1 European emission standards for passenger cars [Category M (Before Euro 5, passenger vehicles >2500 kg were accepted as light commercial vehicles)] Euro standard

CO (g/ km)

THC (g/km)

NMHC (g/km)

NOx (g/ km)

HC + NOx (g/km)

PM (g/ km)

PN (1/ km)

Petrol (gasoline) Euro 1 2.72 – – 0.97 – – Euro 2 2.2 – – 0.5 (#48%) – – (#19%) Euro 3 2.3 0.20 – 0.15 – – – (#15%) Euro 4 1.0 0.10 – 0.08 – – – (#63%) (#50%) (#47%) – Euro 5 1.0 0.10 0.068 0.06 – 0.005a (#63%) (#50%) (#60%) 6 1011b Euro 6 1.0 0.10 0.068 0.06 – 0.005a (#63%) (#50%) (#60%) Diesel Euro 1 2.72 – – – 0.97 0.14 Euro 2 1 – – – 0.7 (#28%) 0.08 – (#63%) (#43%) Euro 3 0.66 – – 0.50 0.56 0.05 – (#76%) (#42%) (#64%) Euro 4 0.50 – – 0.25 0.30 0.025 – (#82%) (#50%) (#69%) (#82%) Euro 5a 0.50 – – 0.180 0.230 0.005 – (#82%) (#64%) (#76%) (#96%) Euro 5b 0.50 – – 0.180 0.230 0.005 6 1011 (#82%) (#64%) (#76%) (#96%) Euro 6 0.50 – – 0.080 0.170 0.005 6 1011 (#82%) (#84%) (#82%) (#96%) Note CO: carbon monoxide, THC: total hydrocarbon (HC), NMHC: non-methane hydrocarbons, NOx: nitrogen oxide, PM: particulate matter, PN: particle number a Applies only to vehicles with direct injection engines b 6 1012/km within ﬁrst three years from Euro 6 effective dates • CO: reduced 63% for petrol vehicles, reduced 82% for diesel vehicles since 1992 • THC: reduced 50% for petrol vehicles since 2000 • NOx: reduced 60% for petrol vehicles, reduced 82% for diesel vehicles since 2000 • PM: reduced 96% for diesel vehicles since 1992

reduced the PM emission limits as shown in Table 6.1 and to achieve these limits, all diesel cars needed particulate ﬁlters. The limits of NOx emission were also tightened (28% decreased compared to Euro 4), in addition, for the ﬁrst time, a PM limit was introduced for petrol engines, which was valid for direct injection engines only. In September 2014, EU launched the Euro 6 standard. Euro 6 standard imposes a further considerable decrement in NOx emissions for diesel cars (a 67% decreased compared to Euro 5). Exhaust gas recirculation (EGR) system substitutes some of

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the intake air (containing 80% nitrogen) with recycled exhaust gas which decreases the available amount of nitrogen to be oxidized to NOx emission throughout the combustion; however, further effective exhaust after-treatments (SCR—selective catalytic reduction, DOC—diesel oxidation catalyst, AdBlue Nozzle) were required in addition to the diesel particulate ﬁlters (DPF), to achieve Euro 6 emission’s limits. The emission limits for Euro 6 standard for petrol vehicles and diesel vehicles are given in Table 6.1. Since September 1, 2017, more stringent and realistic tests are performed for the measurement of exhaust emission. Instead of the previous NEDC test procedure, emissions are tested in the worldwide harmonized light-duty vehicles test procedure (WLTP) and real-driving emissions (RDE) cycle. In NEDC Euro 6b, NOx emission limit must not exceed 80 mg/km. The new Euro 6c tests apply to the WLTP cycle which is performed on a dynamometer, the standard Euro 6d-Temp or 6d tests apply to the RDE cycle which is performed in the middle of the trafﬁc with a portable emission-measuring system (PEMS) attached at the rear of the car. The emission limits remain same as Euro 6 standard; however, in the test bench measuring method WLTP, petrol and diesel engine must not exceed the NOx limit of 60 and 80 mg/km, respectively. For the road measurement RDE, these limits are increased to 126 mg/km for petrol engine and 168 mg/km for the diesel engine. RDE testing is far more challenging than the dynamometer tests. For RDE cycle, the emission limits have been updated to incorporate the effect of different test conditions such as PEMS weight must not exceed (Thompson et al. 2014), driving in the middle of the trafﬁc, road gradient, etc. The difference between Euro 6d and 6d-Temp is the factor by which the measurement in the RDE method may deviate from the test bench results. Until the end of 2019, the Euro 6d-Temp will apply, as long as the cars are allowed to produce 110% more nitrogen oxides in real operation than in the WLTP. From January 1, 2020, it will only be 50% more exhaust gases (Euro 6d).1

6.1.2

Implementation of Emission Norms in India

Indian automotive industry is continuously working toward regulatory emissions as proposed by the Auto Fuel Policy and proactively developing environment-friendly technologies. The ﬁrst stage of emission norms implemented in 1991 for petrol engine and in 1992 for diesel engines. From April 1995, new petrol passenger cars with the mandatory ﬁtment of catalytic converters were introduced in the four metro cities, Delhi, Kolkata, Chennai, and Mumbai, along with a supply of unleaded petrol. The accessibility of unleaded petrol was further expanded to 42 main cities, and it is now accessible in whole country.

1

Auto Zeitung. https://www.autozeitung.de/euro-normen-192219.html#.

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In 2000, India 2000 or Bharat Stage I (BS I) emission norm equivalent to Euro I norm was introduced for passenger cars and commercial vehicles. Further, BS II emission norm equivalent to Euro II norm was imposed in 2001 in Delhi, Kolkata, Chennai, and Mumbai. The details of various Euro norms have been discussed in detail in Sect 6.1.1.1. In 2003, Auto Fuel Policy recommended the roadmap for fuel standards and emission norms for new 2, 3, and 4/more wheeled new vehicles. As given in the roadmap, 4 wheeled vehicles moved to BS III norm in 13 major cities from April 2005 and rest of the country moved to BS II norm; however, new 2 and 3 wheeled vehicles moved to BS II norm from April 1, 2005. From April 2010 onwards, BS IV emission norm was executed for 13 major cities and the rest of the nation moved to BS III. BS IV norm was expanded to additional 20 cities from October 2014 onwards. The National Capital Region (NCR) of India had the severe condition due to a drastic rise in air pollution levels. This severe condition led to the Indian Government taking the boldest decision of skipping BS V emission norm that was subjected to execution in 2020, as well as advancing introduction of BS VI emission norm from 2024 to 2020.2 Since India started a formal emission control regime in 1991, a gap in the execution of the BS emission norms as compared to Euro norms can be noticed. But, this gap has assisted in the technologies to mature which helped the Indian auto sector to achieve the emission norms at a reasonable cost for the Indian customers.

6.2

Role of On-Board Diagnostic Device

On-Board Diagnostic (OBD) is a computer-based system which identiﬁes exhaust emission-related failures in light duty trucks, passenger vehicles, and from some years also in heavy-duty vehicles. To fulﬁll the legal requirements of emission norms, efﬁcient exhaust emission control and cleaning systems are ﬁtted by original equipment manufacturers (OEMs). These systems and the associated components have to be supervised by a so-called On-Board Diagnostic system. In 1990, the ﬁrst widespread use of OBD was in California, to monitor emissions control components and parameters. The requirements of California OBD system used in light- and heavy-duty vehicles have been introduced in two steps:

2

Society of Indian Automobile Manufacturers. http://www.siamindia.com/technical-regulation. aspx?mpgid=31&pgidtrail=33.

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OBD I—The California Air Resources Board (CARB) wanted that all new vehicles sold in California in 1991 and newer vehicles have some basic OBD capability to monitor some of the emission control components. The OBD I was relatively simple and restricted to checking only some of the emission control components, and this was not calibrated to an exact level of emission performance. OBD I was the ﬁrst step; therefore, the problem of lack of standardization occurred between different manufacturers and vehicle models. A repair technician had to purchase different adapters to work on different vehicles, and some systems could only be accessed with expensive OEM scan tools. Another limitation of OBD I was that it could not detect certain kinds of problems, e.g., a dead catalytic converter, ignition misﬁres, or evaporative emission problems. Moreover, OBD I systems were incapable to monitor advanced deterioration of emission-related components (Bordoff 2003; CARB 2003). OBD II—This harder OBD regulation was started in 1994. Since 1996, its implementation had been essential in all-new petrol and alternate fuel passenger vehicles and trucks sold in California. All 1997 and newer diesel passenger cars and trucks were also required to meet OBD II requirements. OBD II system was developed to address the limitations of OBD I and form the system more user-friendly. OBD II systems have been designed to decrease vehicle emissions by observing the failure or deterioration of the power train on an essentially continuous basis. OBD II general requirements are that3: • Virtually, all emission control systems must be monitored, • Malfunctions must be identiﬁed before emissions beat standards by a speciﬁed threshold (generally 1.5 times of emission standard), and • In most cases, malfunctions must be noticed within two driving cycles. The European Union (EU) imposed European On-Board Diagnostics (EOBD) was compulsory for all gasoline and all diesel cars sold in EU, in 2001 and 2003, respectively. The requirement of OBD laws is that all components and subsystems having an emission influence are connected to an engine control unit (ECU) and need to be monitored and diagnosed. The components can be divided into two following categories: (i) Sensors: O2 sensor, pressure sensors, temperature sensors, etc. (ii) Actuators: Fuel injectors, throttle blades, ignition coils, EGR valve, cam phasers, etc. On the system side, many subsystems have to be detected such as a malfunction of a complete subsystem which causes a certain augment in the emission. Such subsystems are as follows: (i) fuel injection system, (ii) ignition system, (iii) exhaust gas cleaning system, (iv) canister purge system.

3

SEMA, Information from the Specialty Equipment Market Association Internet Site. http://www. sema.org.

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The law requires only diagnostics on components which are responsible for an augment in the emissions. But, the breakdowns of the components which cause a degradation of the OBD system have also to be monitored. The development in electronics makes it possible to diagnose all or most of the sensors and actuators connected to an ECU. Overall, OBD system identiﬁes the emission controls in need of repair and also provides effective/inexpensive emission inspections to achieve regulatory exhaust emission standards. Therefore, OBD system is getting more and more attention and OBD regulation is imposed in emerging countries with the same extent as in developed ones.

6.3

Technology Upgradation in Conforming to BS IV to BS VI

There are several challenges related to upgradation of automotive engineering from BS IV to BS VI, like ﬁnance, rise in vehicle cost, and readiness of BS VI fuel in short duration. However, this cost would be recompensed by the drop in diseases due to air pollution which are severe threats to human healthiness in India. Upcoming norms would be an important step in the track of a clean and healthy environment which will cut the expenses on polluted airborne diseases. India requires a broad transition in the ﬁelds from mechanical to electronic controlled engines and the development of a nationwide urea infrastructure to support vehicles which will be using selective catalytic reduction (SCR) for NOx emission control. In addition, OBD systems are required to ensure proper usage of the SCR systems. The transition to electronic controls and the introduction of SCR presents new challenges for industry and environmental regulators. Table 6.2 illustrates the technology upgradation to achieve BS IV to BS VI standards for diesel engines.

6.3.1

Importance of the Fuel Quality

The quality of fuel affects the quantity and type of exhaust emissions from the vehicles. It, directly and indirectly, affects the quality of the air and the amount of greenhouse gas in the environment. Improving fuel standards would facilitate vehicles and their emission control systems to work effectively and assist in the adoption of advanced engine and exhaust emission control technologies. The dominant factor in fuels is sulfur. During combustion, sulfur in diesel fuel is converted into PM emissions via sulfuric acid and sulfur dioxide emissions that lead to subordinate particle development in the atmosphere. These chemicals can also cause acid rain. Sulfur also obstructs the proper working of after-treatment systems designed to low down the tailpipe emissions and corrodes engines and pipes. The

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Table 6.2 Technology upgradation in conforming to BS IV to BS VI (Sharpe and Delgado 2016) Technologies

BS IV ! BS VI

Fuel quality Combustion

• BS IV-grade fuels • Enhancements in engine combustion and calibration for PM control • Intercooling along with turbocharging • VGT • NOx controllers OBD Stage I must monitor thresholds: • Complete elimination of the catalyst when installed in separate housing from DPF or deNOx systems • Efﬁciency reduction of the deNOx system

On-board diagnostics (OBD) requirements

After-treatment system

• NOx control: SCR systems (open loop) • PM control (for EGR pathway only)

• BS VI-grade fuels • Further upgradation in engine combustion and calibration • Multiple injection fuel system • Upgrading VGT • Improvement in NOx controllers • Advanced EGR rates OBD Stage II adds the following: • Checking of the interface between the ECU and other power train and vehicle electrical or electronic systems for steadiness • Adoption of standardized OBD systems across manufacturers and also access to repair • Additional monitoring requirements for EGR flow, EGR cooling system, boost (turbo and superchargers) and fuel injection systems • NOx control: SCR systems (closed loop) • Ammonia slip catalyst • PM control: DOC + DPFs

influence of fuel sulfur content is predominantly damaging the three types of after-treatment systems: diesel particulate ﬁlters (DPFs), lean NOx traps (LNTs), and selective catalytic reduction (SCR). In a CI engine, PM emissions can endure a direct relation to the sulfur content in the fuel. By dropping down the sulfur content in the fuels, effects in lowering down the PM emissions from any diesel engine, irrespective of any vehicle emission standard. Even more important, sulfur in diesel can harm or obstruct the performance of engine exhaust after-treatment devices required for controlling PM and NOx emissions like DPF and certain catalysts equipped in SCR systems (Fung et al. 2010). In India from 1999 to 2017, the sulfur content has been reduced in diesel fuel from 10,000 to 50 ppm, whereas 10 ppm sulfur content in diesel is planned to be supplied nationwide from 2020. Some of the other characteristics like cetane number, polyaromatic content, density, ash content, distillation, suspended solids content, and viscosity in diesel fuel can affect the engine exhaust emissions. A number of studies have shown that the improvement in the quality of fuel directly reduces the generated pollutants during the combustion process and permit the use of more effective engine exhaust after-treatment devices (Central Pollution Control

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Board, Ministry of Environment and Forests (India) 2010; International Council on Clean Transportation and the Energy and Resources Institute 2011; Panda 2010). Better emission control can be attained if both fuel and vehicle emission standards are executed correspondingly.

6.3.2

Advanced Engine Combustion Strategies

The advanced engine combustion technologies focus on in-cylinder engine combustion and how emissions are generated within the engine cylinders along with how combustion and emissions depend on different parameters, i.e., in-cylinder air motion, fuel type, and fuel spray characteristics. The better understanding of these combustion parameters will help in the progress of advanced combustion approaches in the engine such as low-temperature combustion and clean diesel combustion which will decrease the production of oxides of nitrogen (NOx) and particulate matter (PM).

6.3.2.1

Low-Temperature Combustion Strategy

Low-temperature combustion (LTC) strategy is a flameless, staged burning of the fuel in a combustion chamber at a lower temperature as related to the conventional combustion chamber. It was noted that LTC has a perspective of 20% improvement in the efﬁciency over existing diesel engines. The lower temperature results from compression of a fuel–air mixture that has been diluted with either excess air or recirculated exhaust gas. This process raises the density and temperature of the dilute mixture and causes it to auto-ignite which a process is known as compression ignition. To dilute the fuel–air mixture so that it has a lower proportion of fuel as related to the conventional combustion, the engine uses either excess intake air or recirculated exhaust gas. Staged burning is the other signiﬁcant part of LTC which is achieved by controlling the auto-ignition timing and heat release rate. This method removes the excessive combustion rates which cause engine noise and structural damage, particularly at higher loads. LTC offers a number of advantages over conventional combustion4: • The fuel–air mixture and combustion properties permit the engine to be more efﬁcient than conventional combustion engines. • The energy loses in the engine is less over the cylinder walls to the atmosphere due to lower combustion temperature. This reduction in energy loss allows the cylinder to continue higher pressure for a longer period, permitting the engine to 4

Ofﬁce of Energy Efﬁciency and Renewable Energy. https://www.energy.gov/eere/vehicles/ advanced-combustion-strategies.

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do more work. Some of the energy seems to appear in the form of higher exhaust energy that turbocharger can partly arrest. • LTC may be able to achieve ultra-low engine exhaust emissions, which could signiﬁcantly decrease the cost, after-treatment devices requirement, and penalties of fuel economy.

6.3.2.2

Clean Diesel Combustion Strategy

In clean diesel combustion strategy, the burning process is similar to the conventional diesel combustion. In conventional diesel combustion process, the rate at which the fuel spray mixes with air inside the cylinder before it ignites determines the rate at which the fuel and air burn in the flame. In clean diesel combustion strategy, excess fuel–air mixing occurs before the flame formation. This permits cleaner combustion which further produces less soot along with retaining and increasing the efﬁciency of the engine. Recirculating the exhaust gases to the intake air stream dilutes the fuel-air mixture, which further results in lower combustion temperatures and reduction in NOx formation as shown in Fig. 6.2.5 More, computer control, high-pressure fuel injection, multi-pulse fuel injection, manipulation of in-cylinder gas flow technologies and use of exhaust gas recirculation can enhance the clean diesel combustion strategy and make it cost viable for all diesel governed vehicles.

6.3.3

Exhaust After-Treatment Strategies

Exhaust emissions of NOx and PM from diesel engines have been lowered by 80– 90% over the previous years. These signiﬁcant reductions in emissions have been attained mainly by modiﬁcations in the engine design such as retarded timing, cooled EGR systems, and higher injection pressures. However, engine modiﬁcations only will not be enough to meet stringent emission norms, and exhaust control systems will be needed to remove NOx and PM from the engine exhaust gas.

6.3.3.1

Oxidation Catalysts

Oxidation catalysts are the automotive catalysts which are equipped since mid-1970. Oxidation catalysts function is to convert CO and HC to CO2 and water but has slight effect on NOx formation. Diesel Oxidation Catalysts (DOC) is the key technology for diesel engines where the high oxygen content of the exhaust averts the use of three-way catalysts. DOCs not only convert CO and HC but also reduce 5

DieselNet, Engine and Emission Technology. https://www.dieselnet.com/tech/engine_egr.php.

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Fig. 6.2 Clean diesel combustion process (Source https://www.dieselnet.com/tech/engine_egr. php)

the mass of diesel particulate emissions by oxidizing some of the HC that is adsorbed onto the carbon particles (Horiuchi et al. 1990) as shown in Fig. 6.3. All modern diesel engines mounted in passenger cars, buses, and light-duty and heavy-duty trucks are now equipped with DOCs. DOCs can be equipped in combination with NOx absorbers, DPFs, SCR catalysts to rise the NO2: NOx ratio or to abate any sort of left behind injected reductant used for NOx reduction like ammonia.

Fig. 6.3 Diesel oxidation catalyst (Source http://www.emissol.com/about-us/)

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6.3.3.2

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Diesel Particulate Filter (DPF)

DPF is an in wall-flow ﬁlter. PM is removed from the engine exhaust by trap ﬁltration method which uses a honeycomb structure similar to a catalyst substrate. In place of, the engine exhaust gas flows through the walls between the channels and the PM is deposited as a soot cake on the walls as shown in Fig. 6.4. The ﬁlters are made of ceramic [cordierite (Kasai et al. 2004), silicon carbide (SchäferSindlinger et al. 2003), or aluminum titanate (Ingram-Ogunwumi et al. 2007)] honeycomb materials. Ceramic wall-flow ﬁlters eliminate the carbonaceous and metallic particulates, including ﬁne particulates of less than 100 nanometers (nm) diameter with an efﬁciency of >95% in mass and >99% in a number of particles over a wide range of engine operating conditions (May et al. 2008). The latest BS VI emissions’ limit values are set on both the basis mass as well as number counts to ensure control of the ultraﬁne particles, which are believed to have more negative impact on human health. Since the persistent stream of soot into the ﬁlter channel would in the long run block it, it is important to “regenerate” the ﬁltration properties of the ﬁlter channel by burning off the gathered particulate. The strategies to accomplish regeneration include: • Joining an oxidation catalyst upstream of the ﬁlter channel that and in addition working as an ordinary oxidation catalyst additionally expands the proportion of

Fig. 6.4 DPF ﬁtted in Audi A8 3.0 TDI Quattro (Source prensa.audi.es)

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NO2 to NO in the exhaust stream (Hawker et al. 1998). NO2 gives a more viable oxidant than oxygen and thus gives ideal inactive regeneration efﬁciency. • Integrating a coated catalytic on the ﬁlter channels in order to lower down the temperature at which particulate flames. New interpretations and process advancement plan to bring down the back-pressure (Maunula et al. 2007) and to substitute platinum by palladium where ultra-low-sulfur fuels are accessible. In Pfeifer et al. 2007, Pt/Pd formed at a 3:1 proportion had brought down light-off temperature (the temperature at which the impetus begins to work) than Pt itself as catalysts (240 °C vs. 295 °C) in the matured state, produced as much NO2 for inactive soot oxidation. The production of NO2 impervious to sulfur sullying. In one examination (Johansen et al. 2007), platinum was totally substituted for palladium with utilization of a base metal catalyst. New creations are utilizing ceria or zirconia to influence the sediment to respond speciﬁcally with oxygen at catalyst–soot interface. One paper (Harada et al. 2008) demonstrates another zirconia-based sediment catalyst that exchanges oxygen from the gas to the catalyst soot interface for 70% speedier soot oxidation rates at 75 °C lower temperatures. Improved adaptations in light of ceria are demonstrating potential to oxidize sediment at temperatures as low as 260 °C with next to no valuable metal (Southward and Basso 2009). • Utilizing little amounts of fuel-borne catalyst (FBC, for example, ceria (Zelenka et al. 1998) or iron-added substance mixes added to the fuel utilizing on-board dosing systems. The FBC, when gathered on the ﬁlter as a close blend with the particulate, enables the particulate to consume at exhaust temperatures (around 350 °C rather than 650 °C) and builds the burning energy (ordinarily 2–3 min) while the strong buildups of the catalyst are held in the ﬁlter channel as cinders. The third era of FBC (Harlé et al. 2008) depends on 3 ppm iron enabling a 1.7 L tank to last the life of the vehicle (240,000 km) and requiring no procedure for slag cleaning. Fuel injector set in the exhaust line upstream of the DPF (Fasolo et al. 2009). This gives a wellspring of hydrocarbons to start the temperature ascends for regeneration. • Electrical warming of the trap either on or off the vehicle (Kitagawa et al. 1991) and (Lee et al. 2009). Caught particulates consume off at ordinary exhaust temperatures utilizing the great oxidative properties of NO2 and can consume in oxygen when the temperature of the exhaust gases is occasionally expanded through post-ignition. Most extreme exothermic temperatures must be controlled, particularly in most pessimistic scenario “drop-to-sit” conditions when the soot ignition begins at high temperature and stream and after that the engine drops to idle condition (Boger et al. 2009). As the comprehension of DPF essentials has moved ahead, a permeable ﬁlm would now be able to be added to the inlet wall with the goal that soot is kept out of the DPF walls (Mizuno et al. 2008). This enhances ﬁltration efﬁciency and back-pressure and, in addition, the relationship between back-pressure and soot

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stacking. This connection can be utilized for OBD reason, and for instance, soot models utilizing wall porousness calculations have been created (Dabhoiwala et al. 2008). Residue soot sensors may likewise be required in the future (Sandig and Zikoridse 2008).

6.3.3.3

Selective Catalytic Reduction (SCR)

SCR is a procedure that uses a catalyst to change over NOx in deplete gases to nitrogen and water, which are then discharged into the air. SCR was initially created and used to lessen NOx discharges from coal-, oil-, and gas-operated power stations, marine vessels, and stationary diesel motors. SCR innovation allows the NOx diminishment response to happen in an oxidizing air. It is called “selective” on the grounds that the reactant lessening of NOx with ammonia (NH3) as a reductant happens positively to the oxidation of NH3 with oxygen. Particulate outflow emissions are additionally brought down and SCR exhaust catalytic converter systems can be utilized alone or in couple with a particulate ﬁlter channel. Once the engine exhaust system is sufﬁciently warm, suitable measures of ammonia reductant are infused into the exhaust stream which additionally brings about a high-state reduction of NOx. For versatile source applications, ammonia is utilized as a selective reductant, within the sight of overabundance oxygen, to change over 70% (up to 95%) of NO and NO2 to nitrogen over an uncommon catalyst system. Diverse forerunners of ammonia can be utilized; however for vehicles, the most well-known alternative is a solution of urea in water (e.g., AdBlue®) deliberately metered from a different tank and showered into the exhaust where it hydrolyzes into ammonia in front of the SCR catalyst as shown in Fig. 6.5. AdBlue® is a stable, non-combustible, colorless, and odorless solution containing 32.5% urea which is not named as risky to health wellbeing and does not require any exceptional taking care of precautionary measures. It is made to international perceived norms. Urea is utilized as counterfeit compost and is found in items, for example, beauty care products. The utilization of AdBlue® for BS VI vehicles will emphatically rely upon the auto producer product technique, vehicle application, driving style, load, and pathways conditions and the urea tank should be topped up occasionally. Advancement of SCR innovation is extremely powerful, and enhancements are being made in low-temperature performance execution, urea conveyance systems, system outline, and exhaust stream mixing devices, urea dosing system, and giving contrasting options to fluid urea. Urea infusion quality and blending are mind-boggling and fundamentally critical. An examination indicates (Gorbach 2009) that the urea droplet quality from different nozzle outlines can affect the deNOx system productivity by up to 10% while the urea circulation over the catalyst can bring about effectiveness variations from 60 to 95%. A few sorts of catalysts can be utilized as a part of SCR system, the decision of which is controlled by the temperature of the exhaust condition. Initially, SCR catalysts depended on vanadium which can be utilized where

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Fig. 6.5 SCR catalytic converter technology (Source dieselnet.com)

resilience to sulfur is required, if temperatures are underneath 600 °C (for the most part for heavy-duty diesel applications). Improved operation of SCR catalysts relies upon control of adsorbed ammonia and utilization of oxidation catalysts to deliver the proper NO2/NOx proportion. In fact, the “quick SCR response” utilizes both NO and NO2 at an ideal proportion of 1:1 and this is basic for good performance underneath 200 °C. In any case, overabundance NO might be expected to oxidize ammonium nitrate (NH4NO3) which can condense and block catalytic sites (Kröcher and February 2007). Copper and iron can be utilized together for an adjusted performance over an expansive scope of temperatures (Girard et al. 2009; Iretskaya et al. 2008). Vanadium is less expensive and more tolerant to sulfur yet weakens at temperatures more noteworthy than 600 °C while zeolites are next to no influenced with long exposure at 800 °C (Cavataio et al. 2009). Like vanadium, Fe–zeolites are very tolerant to sulfur yet Cu–zeolite performance depreciates and can be reestablished with a desulfation cycle (Anderson 2008). New zeolite is being created for low-temperature transformation without copper (Iretskaya et al. 2008) and new catalyst families in light of acidic zirconia are likewise rising (Verdier et al. 2008). On-Board Diagnostic (OBD) and closed-loop SCR control are utilizing either the trustworthy NOx sensors (Walde and Nakasone 2007) or another ammonia sensor (Weisgerber 2007) which has a ±5 ppm ammonia recognition precision up to around 30 ppm alkali, and has immaterial impedance from NOx, HC, and CO.

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Combined PM and NOx Control Technologies

Due to the improvement of diesel particulate ﬁlter substrates with a higher porosity (Kattouah 2015), systems would now be able to integrate the Selective Catalytic Reduction catalyst onto the DPF substrate as shown in Fig. 6.6 (Lee et al. 2009; Oladipo et al. 2008). SCR with DPF consolidated frameworks in close-coupled position gives noteworthy change in the NOx transformation proﬁciency contrasted with particular segments (Krüger et al. 2015; Knirsch et al. 2014), because of the higher temperature level in the nearby coupled position particularly under low load conditions run of the mill for urban driving. SCR with DPF systems must be utilized increasingly to meet light- and heavy-duty BS VI emissions standard prerequisites.

6.4

Concerns and Conflicts

Bharat stage VI emission standards are the acquaintance of cutting edge innovations with guaranteed reduced pollutants released from the vehicles as per the speciﬁed limits. It will likewise mean various changes to be made in the engine systems. To comprehend the difﬁculties, it merits plunging into the fundamental emission technologies. To accomplish a diminishment in particulate matter by 82% and oxides of nitrogen (NOx) by 68%, automakers require arrangements of technologies —DPF to expel diesel particulate matter, or soot, from the engine exhaust gases. Then, there are SCR and EGR, which is for NOx decrease. In EGR, the engine re-courses a segment of the exhaust gas back to the engine cylinder denying it of a speciﬁc measure of oxygen accordingly prompting lower temperature consume. This lessens NOx discharges yet delivers more PM, which is decreased utilizing DOC and DPF.

Fig. 6.6 After-treatment devices for upgradation in BS IV to BS VI (Sharpe and Delgado 2016)

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It is not about the advancement in the technologies, but the challenge is to multiply it over different platforms in an excellent order. In the aspect of doing that, there should not to be any sort of compromise with fuel efﬁciency and a decent test facility are required to build up a good DPF system. It needs numerous hours on the proving ground and various tests on the case dynamometer, a gadget for estimation and testing created to reproduce the road on a roller in a controlled domain. To test these advancements for best outcomes, their execution should be observed in each season and crosswise over different landscapes for real-time investigation. Any sort of carelessness in testing and approving the working of the gadgets can be a noteworthy safety hazard. There is also the challenge of assembling the after-treatment devices in the limited space without compromising with the vehicle fuel efﬁciency. The addition of parts and aggregation of DPF, a urea tank, dozing unit for NOx (required in SCR) will increase the weight of the vehicle. The extra weight can affect fuel efﬁciency. Diesel fuel parameters for which BS VI speciﬁcations differ from Euro VI speciﬁcations include density (at 15 °C, BS VI: 820–860 kg/m3; Euro VI: 845 (max) kg/m3), 95% distillation boiling point (in °C, max. BS VI: 370; Euro VI: 360), and polycyclic aromatic hydrocarbons (PAH) (mass%, max. BS VI: 11; Euro VI: 8). A collective vision for each of these fuel parameters is responsible for reduction of emissions from diesel engine with the implementation of advancement in design and efﬁciency of after-treatment control technologies. New engines integrating progressive combustion control and exhaust after-treatment systems have largely decreased or removed the effects of small changes in these fuel parameters on engine emissions. These modern engine designs need low-sulfur content in the fuels to keep vital emissions performance during their useful life cycles. Therefore, the main fuel parameter speciﬁed in the proposed BS VI emission regulation is sulfur content limit, i.e., 10 ppm for gasoline and diesel fuels. Sulfur is used as a lubricant within the fuel, which helps in better combustion as it keeps pumps and fuel injectors healthy. However, sulfur is also responsible for higher particulate matter emissions (PM 2.5 and PM 10)—the concern of diesel vehicles. Ultra-low-sulfur fuels introduce recent and low-emitting BS VI vehicles which integrate best available technologies for regulatory air pollutant emissions. Air pollutant emissions consequential from differences between BS VI and Euro 6/ VI fuel speciﬁcations are likely to be insigniﬁcant. By 2023–2025 time span, India should attempt to match, or even improve upon, Euro 6/VI fuel speciﬁcations. Nevertheless, the differences in fuel quality speciﬁcations must not delay the full implementation of BS VI emission standards norms in 2020 (International Council on Clean Transportation 2016).

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123

Conclusions

As India transitions to move to more stringent vehicle emissions standards over the coming years, new engines are going to experience signiﬁcant technology changes. The shift from the current national standard to BS VI commencing in 2020 is going to require manufacturers invest in a number of technologies to achieve the target brake-speciﬁc levels of NOx and PM emissions. India and other developing countries can take beneﬁt from the experienced gained by the developed countries. The developed markets of USA, Europe, and Japan indicate that these engine technology changes include transitioning from mechanical to electronic controls, improvements in engine combustion and calibration, increased injection and cylinder pressures, reﬁnement in fuel injection systems, and the implementation of NOx and PM after-treatment solutions. Innovations exist for control of CO, HC, NOx, PM, and PN, for diesel engines. The technologies for controlling criteria pollutant emissions often have efﬁciency impacts. For example, selective catalytic reduction (SCR), which is required to achieve the most stringent NOx levels, allows engines to be tuned for increased fuel efﬁciency. Moreover, the introduction of electronic controls and more sophisticated fuel injection strategies is a boon to efﬁciency. On the other hand, certain emission control strategies such as exhaust gas circulation (EGR) and diesel particulate ﬁlters (DPFs) often have negative fuel use complications. Moreover, regulatory progress in Japan, China, and the EU is also expected to promote the propagation of a number of fuel-saving technologies for engines. These technology advances include improvements to combustion and air handling, reduced friction and parasitic loads, high-efﬁciency after-treatment, and waste heat recovery. Ceaseless change in substrate and coating innovations, as a major aspect of a coordinated system involving electronic control and fuel quality, will permit meeting more and more stringent vehicle emission norms directions under an extensive range of engine working conditions. A roadmap for vehicle emission and standards for fuel quality has already been established for smoothening the progression of BS VI emission standard norms in India by skipping BS V.

References Anderson P (2008) SCR catalysts for low-emitting diesels. In: US Dept. of energy diesel engine emissions and energy reductions conference (DEER), Detroit Boger T, Rose D, Tilgner IC, Heibel AK (2009) Regeneration strategies for an enhanced thermal management of oxide diesel particulate ﬁlters. SAE Int J Fuels Lubricants 1(1):162–172 Bordoff F (2003) OBD what is it and what does it mean to you, Fact Sheet—Fred’s Garage, Apr 2003. http://www.fredsgarage.com/OBD.htm CARB, Personal Communication—M. McCarthy, California Air Resources Board, Sept 2003-1 Cavataio G, Jen HW, Warner JR, Girard JW, Kim JY, Lambert CK (2009) Enhanced durability of a Cu/zeolite based SCR catalyst. SAE Int J Fuels Lubricants 1(1):477–487

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Krüger M, Maier R, Naber D, Scherer S, Schumacher H, Strobel M (2015) Robert Bosch GmbH, Stuttgart: emission optimization of diesel passenger cars to fulﬁll real driving emission (RDE) requirements. In: 24th Aachen colloquium automobile and engine technology, Aachen Lee DH, Kim KT, Cha MS, Lee JO, Song YH, Cho H, Kim YS, Song Y, Jee T (2009) Active regenerative DPF using a plasma assisted burner. SAE Technical Paper Lee JH, Paratore MJ, Brown DB (2009b) Evaluation of Cu-based SCR/DPF technology for diesel exhaust emission control. SAE Int J Fuels Lubricants 1(1):96–101 Marrack D (1995) Public health and toxic particles: danger in the air: toxic air pollution in Houston —Galveston Corridor. Report by the Galveston-Houston Association for Smog Prevention (GHASP), 72–78 Maunula T, Matilainen P, Louhelainen M, Juvonen P, Kinnunen T (2007) Catalyzed particulate ﬁlters for mobile diesel applications. SAE Technical Paper May J, Bosteels D, Such C, Nicol A, Andersson J (2008) Heavy-duty engine particulate emissions: application of PMP methodology to measure particle number and particulate mass. SAE Technical Paper Mizuno Y, Miyairi Y, Katsube F, Ohara E, Takahashi A, Makino M, Mizutani T, Yuki K, Kurachi H (2008) Study on wall pore structure for next generation diesel particulate ﬁlter. SAE Technical Paper Oladipo B, Bailey O, Price K, Balzan N, Kaul S (2008) Simpliﬁcation of diesel emission control system packaging using SCR coated on DPF. DEER Panda P (2010) Treating diesel exhaust: BSV and beyond Pfeifer M, Koegel M, Spurk PC, Jeske G (2007) New platinum/palladium based catalyzed ﬁlter technologies for future passenger car applications. SAE Technical Paper Pope CA, Thun M, Namboodiri M, Dockery D, Evans J, Speizer F, Heath C (1995) Particulate air pollution as a predictor of mortality in a prospective study of U.S. adults. Am J Respir Crit Care Med 151:669–674 Sandig R, Zikoridse G (2008) Partikelsensoren zur Überwachung der DPF-Funktion-Ergebnisse der Felderprobung an Off-Road-Maschinen. In FAD-Konferenz Herausforderun Abgasnachbhandlung für Dieselmotoren, Dresden Schäfer-Sindlinger A, Vogt CD, Hashimoto S, Hamanaka T, Matsubara R (2003) New materials for particulate ﬁlters in passenger cars. Auto Technol 3(5):64–67 Sharpe B, Delgado O (2016) Engine technology pathways for heavy-duty vehicles in India. Working paper. International Council on Clean Transportation Southward BW, Basso S (2009) An investigation into the NO2-decoupling of catalyst to soot contact and its implications for catalysed DPF performance. SAE Int J Fuels Lubricants 1 (1):239–251 Technical background on India BS VI fuel speciﬁcations. International Council on Clean Transportation (2016) Thompson GJ, Carder DK, Besch MC, Thiruvengadam A, Kappanna HK (2014) In-use emissions testing of light-duty diesel vehicles in the United States. International Council on Clean Transportation Verdier S, Rohart E, Bradshaw H, Harris D, Bichon P, Delahay G (2008) Acidic zirconia materials for durable NH3-SCR deNOx catalysts. SAE Technical Paper Walde T, Nakasone O (2007) Smart NOx-sensor-application in diesel systems. In: Car Training Institute Exhaust Systems Forum, Nuertingen, Germany Weisgerber V (2007) Delphi NH3 ammonia sensors. In: Car Training Institute Exhaust Systems Forum, Nuertingen, Germany Williams M, Minjares R (2016) A technical summary of Euro 6/VI vehicle emission standards. ICCT International Council on Clean Transportation Zelenka P, Reczek W, Mustel W, Rouveirolles P (1998) Towards securing the particulate trap regeneration: a system combining a sintered metal ﬁlter and cerium fuel additive. SAE Technical Paper

Chapter 7

Non-Noble Metal-Based Catalysts for the Application of Soot Oxidation Pravesh Chandra Shukla

Abstract Diesel engines have become very popular due to their durability and higher efﬁciency. In fact, diesel engines today are the backbone of the transportation. However, these engines are also one of the prime emitters of PM and NOx. These emissions are harmful to the living life as well as to the environment. After-treatment devices were being used nowadays in diesel engines, which are mostly coated with noble metal catalysts. These noble metal catalysts are costly and rare too. In last decades, some of the non-noble metal-based catalysts have also been considered for these applications. In this chapter, a discussion has been performed in order to provide the current level of progress in the application of non-noble metal-based catalysts for the application of soot oxidation. Details about the catalysts are explained in two subsections under the heading of (1) transition and alkali metal-based catalysts and (2) perovskite-based catalysts.

Keywords Diesel particulate emission After-treatment devices Diesel Oxidation Catalysts (DOCs) Non-noble metal-based catalyst Transition metals Perovskite materials

7.1

Introduction

Diesel Oxidation Catalysts (DOCs) are very suitable for the diesel exhaust emission reduction. DOC provides quite good efﬁciency (can be up to 90%) in the reduction of organic fraction of the particulates (Westphal et al. 2012; Khair and Majewski 2006). Most of the commercially available DOCs are coated with noble metal-based catalysts such as platinum, palladium, rhodium. These catalysts provide good performance for the soot oxidation purpose, however, noble. However, noble metals are available at a very high cost and rare too. Several researchers P. C. Shukla (&) Department of Mechanical Engineering, Indian Institute of Technology Bhilai, Sejbahar, Raipur, Chhattisgarh, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2019 A. K. Agarwal et al. (eds.), Advanced Engine Diagnostics, Energy, Environment, and Sustainability, https://doi.org/10.1007/978-981-13-3275-3_7

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(Shangguan et al. 1998; Milt et al. 2003; Ciambelli et al. 2003; Van Setten et al. 2001; Nejar et al. 2005) have attempted to test non-noble metal-based catalysts for the soot oxidation application under laboratory environment or with a real diesel engine exhaust. The non-noble metal-based catalyst may be prepared at lower costs which can provide comparable performance with the adoption of certain preparation method. The non-noble metal-based catalyst can be prepared at lower costs which may provide comparable performance with the adoption of certain preparation method. The performance of the catalyst for soot reduction, its durability, stability, and activity at elevated temperature, etc., is some of the main criteria for a suitable catalyst for the soot oxidation (Heck et al. 2012). On the other hand, biodiesel is a promising fuel for the partial replacement of diesel fuel which resulted in signiﬁcantly lower particulate mass emissions. Several studies (Hoekman et al. 2012; Jung et al. 2006) showed that biodiesel reduced the particulate emission compared to diesel fuel. The application of a non-noble metal-based catalyst for DOC can be a good option for the particulate reduction in combination with biodiesel fuel. The lower particulate emission characteristics of biodiesel fuel may be an added advantage for the non-noble metal-based catalysts. In the present section, a detailed literature review has been performed in order to provide current level of progress in the application of non-noble metal-based catalysts for the application in DOCs. Details about the catalysts are explained in two subsections under the heading of (1) transition and alkali metal-based catalysts and (2) perovskite-based catalysts.

7.1.1

Transition and Alkali Metal-Based Catalysts

Elements which have partially ﬁlled d- or f-subshell in any common oxidation state are called transition elements. Most of these elements can show multiple oxidation state which is a very good an excellent property for catalytic activity. Yuan et al. (1994) have investigated the catalyst activity of potassium-promoted copper catalyst supported on TiO2 catalyst in a microbalance operated on a temperature-programmed oxidation. They observed that ‘K’ increased the activity of the copper catalyst. Copper catalyst supported on La2O3 and La2O2CO3 associated with other metals like niobium, potassium, vanadium, molybdenum was tested by Bellaloui et al. (1996). They found that niobium and potassium provided the best performance for soot oxidation. Copper catalyst with niobium and potassium promoter further increased the activity of the copper catalyst. Fornasiero et al. (1996) have investigated the redox behavior of Ce0.5Zr0.5O2 solid solution as a catalyst, by using temperature-programmed reduction (TPR) measurement. They observed that ZrO2 introduction with CeO2 signiﬁcantly increases activity for oxidation in comparison to pure CeO2. CeO2 has oxygen storage capacity, which helps in rapid reduction/oxidation cycles as follows:

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2CeO2 ! Ce2 O3 þ ð1=2ÞO2 This property of CeO2 is very important for automobile exhaust after-treatment techniques. A series of La2Cu(1 − x)PdxO4 compounds were prepared for their investigation of CO and NO oxidation for three-way catalytic (TWC) converter application (Guilhaume et al. 1996). They reported that the light-off temperature for CO and NO was reduced signiﬁcantly with increasing content of ‘Pd’ (Fig. 7.1) (Guilhaume et al. 1996). They also observed that the oxidation performance of La2Cu0.9Pd0.1O4 and La2Cu0.8Pd0.2O4 was comparable to that of the performance of Pt-Rh/CeO2-Al2O3 catalyst for NO oxidation, and it was higher for CO oxidation. Badini et al. (1998) have observed that although the activity of the two catalysts (CsVO3 + KCl and KVO3 + KCl) was low compared to Cu–K–V–Cl-based catalyst, the activity of (CsVO3 + KCl) becomes comparable to the Cu–K–V–Cl-based catalyst over a prolonged thermal exposure in wet air. The activity was not affected even in the presence of SO2. Masuda et al. (1998) have prepared Ag-Pd/mordenite catalysts for the removal in diesel exhaust under the practical condition of high space velocity on honeycomb monoliths and observed that the conversion of NOx is dependent on the Pd loading (Fig. 7.2). They concluded that a small amount of Pd could effectively remove the NOx from diesel exhaust in the presence of (CH3)2O as reducing agent.

Fig. 7.1 Activity of catalyst La & O, for CO, NO, and C, H, conversions in the presence of feed stream A, in stationary conditions (Guilhaume et al. 1996)

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Fig. 7.2 Effect of the content of Pd in Ag(3)/Pd/mordenite(20) on the catalytic activity. (O) 0.01 wt%-Pd, (D) 0.1 wt%-Pd, ( ) 1.0 wt%-Pd, (+) 0 wt%-Pd, NOx, 650 ppm; (CH3)2O, 1250 ppm; total flow rate 250 cm3/s; wash-coated catalyst weight 2.0 g (Masuda et al. 1998)

Co, K/MgO catalysts were prepared with 12% Co and 1.5, 4.5, and 7.5% of K, and it was found that K with 4.5% showed lowest soot burn temperature which was 378 °C (Calcined at 400 °C) (Querini et al. 1999). When 1.5 and 4.5% K catalysts were calcined at 500 °C, they lose their activity because of the solid solution formation of Co and Mg. The role of the potassium is to (i) increase surface mobility, (ii) retain the stability against thermal treatment, and (iii) promote the formation of carbonates from carbon, thus reducing soot (oxidation). Saracco et al. (1999) have prepared Cs- and K-based pyrovanadate-based catalyst for the combustion of carbon at low temperature and noticed that cesium pyrovanadate showed carbon ignition temperature at 255 °C and high carbon combustion rate at 300 °C (about). Griesel and Nieuwenhuys (2001) have studied the low-temperature CO oxidation and CH4 oxidation over Au/Al2O3 and Au/MOx/Al2O3 (M = Cr, Mn, Fe, Co, Ni, Cu, Zn) and stated that addition of MOx to Au/Al2O3 stabilizes the Au particles. Multi-component catalysts showed an improved activity for low-temperature CO oxidation compared to the mono-component catalysts. Au particle size was an important parameter on which activity was dependent; it was not much dependent on the type of MOx (Fig. 7.3). Oi-Uchisawa et al. (2001) have examined the catalytic activity has increased when Pt/MOx was supported on SiC, where MOx refers to TiO2, ZrO2, and Al2O3. The activity of Pt/TiO2/SiC was highest among all catalysts for the soot oxidation. Three catalysts, Pt/c-Al2O3, Au/a-Fe2O3, and CuO-CeO2, were evaluated for their performance in the presence of CO2 and H2O with the reactant gases (Avgouropoulos et al. 2002). It was reported that Au/a-Fe2O3 catalyst provided the best performance for selective CO oxidation at a relatively low temperature ( Zr > Si > Al. The order of catalytic activity was found as Ti > Zr > Si > Al. Redox property and mobility of surface atoms are the two important parameters which mark the catalytic activity. Uner et al. (2005) have observed that the soot oxidation temperature was lowered to 385 °C for a mixed oxide prepared by using

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Fig. 7.4 V-loading dependence of Tr at R = 150 lg/g-s for the titania samples (Craenenbroeck et al. 2002)

cobalt oxide power and lead acetate solution by following the incipient method which further reduced with Pt impregnation (Table 7.1). Copper-doped ceria and ceria–zirconia mixed oxides using solgel method were prepared by Wu et al. (2007) for diesel soot oxidation. They observed that Cu doping signiﬁcantly improved the low-temperature activity and selectivity to CO2. Atribak et al. (2008) have investigated the catalytic activity of CeO2-ZrO2 with different proportions for the oxidation of soot particulates and NOx reduction. They observed that Ce0.76Zr0.24O2 delivered the best performance for soot reduction. It

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Table 7.1 Combustion temperatures of homemade soot in the presence and in the absence of the metal oxides (65 cm3/min air) (Uner et al. 2005) Sample Soot CoOx/soot PbOx/soot CoOx–PbOx/soot Pt/CoOx–PbOx/soot Where, Tp is maximum also indicated.

T5 (°C)

T99 (°C)

Tp (°C)

345 555 520 305 462 430 286 361 315 275 420 385 310 375 343 conversion occurs; 5% (T5) and 99%

Catalyst particle size (Dp, nm) – 17–34 12–48 8–17 – (T99) conversion temperatures are

was also efﬁcient for NOx reduction compared to CeO2 at temperatures higher than 500 °C. Liang et al. (2008) have investigated the catalytic activity of Cu/Mn-doped ceria for diesel soot oxidation and observed that Cu–Ce interaction signiﬁcantly enhances the release of lattice oxygen of the oxides (Fig. 7.5). Both catalysts showed better catalytic behavior and selectivity to CO2 for soot oxidation in comparison with pure ceria. Table 7.2 shows the soot activity of the catalysts used by Liang et al. (2008) under different contact conditions. Oxidation characteristics of Co–Mn-based mixed oxide with Al2O3, ZnO2, and TiO2 for VOCs were evaluated (Doggali et al. 2012) which showed the following sequence of reactivity for VOCs oxidation; Cu–Mn/TiO2 > Cu–Mn/ZrO2 > Cu– Mn/Al2O3. They reported that the oxidation depends on the type of support material also. Better oxidation with TiO2 and ZrO2 was observed because of their good redox property. Kumar et al. (2012) have synthesized the CeO2 and Co/CeO2 nano-ﬁbers for the diesel soot oxidation and observed soot oxidation at around 400 °C by using Co impregnated with 10% oxygen content. They also observed a decrease in peak soot combustion temperature by 40 °C with the addition of 500 ppm NO. Based on their study, it was concluded that ﬁbrous structure shows good trapping and combustion of soot.

Fig. 7.5 Reaction mechanism and active oxygen species: (a) the Mn–Ce mixed oxides under loose contact conditions and (b) the Cu–Ce mixed oxides under tight contact conditions (Liang et al. 2008)

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Table 7.2 Soot oxidation activity of the catalysts under different contact conditions (Liang et al. 2008) Samples Mn–Ce Cu–Ce CeO2

7.1.2

Tight contact Tm (°C) Ti (°C)

S(CO2) (%)

Loose contact Ti (°C) Tm (°C)

S(CO2) (%)

348 338 380

94 98 96

491 510 545

95 95 86

368 356 390

503 522 553

Perovskite-Based Catalysts

Perovskites are very good catalytic materials with broad verities of physical properties, such as ferro-, piezo-, and pyro-electricity, magnetism, and electro-optical effects. Chemical properties of perovskites are (González et al. 1997) as follows: (i) Crystalline structure can accommodate various types of cations; (ii) Cations in the two crystallography positions can be partially substituted; (iii) This makes possible a wide range of defects and accessibility of oxygen in the crystal, making it possible for the application in electronics, magnetic, and catalytic materials. Perovskite was ﬁrst reported in 1926 by Goldschmidt (1926), and research on perovskites was booted by Libby (González et al. 1997; Libby 1971). Dhakad et al. (2010) synthesized two types of low-cost perovskite catalysts, La0.8Pr0.2MnO3 and La0.7K0.1Pr0.2MnO3, prepared by using the precipitation method for the potential application in DPF. They observed that perovskite doped with ‘K’ shows higher catalytic activity because of its promotional effect. The substitution of Pr in the perovskite creating a defective perovskite which increases the mobility of oxygen molecule thus increases the reactivity. In addition to this, potassium doping provides further increased activity and selectivity (Table 7.3). La0.7K0.1Pr0.2MnO3 catalyst showed Tinitial as 300 °C and Tﬁnal as 510 °C which was about 110 °C lower than non-catalysed reaction. Catalytic activity of ABO3- and K2NiF4-type (A2BO4) catalyst was investigated for simultaneous removal of soot and NOx (Teraoka et al. 1996). They opined that the appropriate substitution of ‘K’ in the site ‘A’ of perovskite resulted in a good performance for simultaneous reduction of soot and NOx. It is believed that the nature of ‘K’ in the activation of soot surface leads to higher activity for ‘K’-doped catalyst. Song et al. (1997) have prepared Ag/MnOx perovskites by using the spray Table 7.3 Activated carbon oxidation results of lanthanum manganite (Dhakad et al. 2010) S. No.

Sample

T1 (°C)

T50 (°C)

Tf (°C)

1 2 3 4

Bare carbon LaMnO3 La0.8Pr0.2MnO3 La0.7K0.1Pr0.2MnO3

380 350 282 235

530 500 450 445

620 580 510 487

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decomposition method and compared the activity of Ag/MnOx/perovskite with LaMnO3 perovskite activity. It was observed that Ag/MnOx perovskite exhibits higher activity compared to LaMnO3 (Fig. 7.6). LaCrO3 and CrO3 catalysts supported on LaAl11O18 and Al2O3 were formulated by Zwinkels et al. (1999). They reported that the catalysts supported on Al2O3 showed higher activity compared to LaAl11O18-supported catalysts because of the higher surface area availability with Al2O3-supported catalysts. Buciuman et al. (2001) have investigated the catalytic properties of the La0.8Al0.2MnO3 (A = Sr, Ba, K, Cs) and LaMn0.8B0.2O3 (B = Ni, Zn, Cu) perovskite catalysts for NO conversion in the presence of propene as reducing agent and observed a maximum conversion rate in the range of 300–400 °C. He et al. (2001) have investigated the perovskite-type oxides La(1 − x)SrxMO3 (M = Co0.77Bi0.2Pd0.03) for three-way catalytic performance (Fig. 7.7). They observed the following decreasing order for the activity for C3H6 elimination, La0.2Sr0.8MO3 > La0.8Sr0.2MO3 > La0.4Sr0.6 MO3 > La0.6Sr0.4MO3 > LaMO3, and decreasing order of activity for CO oxidation, La0.8Sr0.2MO3 > La0.2Sr0.8MO3 > La0.4Sr0.6MO3 > La0.6Sr0.4MO3 > LaMO3. Catalytic activity of La0.8Au0.2MnO3 (A = Sr, Ba, K, Cs) and LaMn0.8B0.2O3 (B = Ni, Zn, Cu) perovskites was probed by Buciuman et al. (2001) for hydrogen and propene oxidation at lean mixture condition. The temperature range was kept 200–450 °C for hydrogen and 150–550 °C for propene and observed that copper-substituted perovskite gives the lowest activity for oxidation. Fino et al. (2003a) formulated two different types of catalyst, zirconia-toughened-alumina foams catalyzed with Cs–V catalysts and cordierite or SiC wall-flow ﬁlters catalyzed with perovskite catalysts (LaCr0.9O5), for the regeneration purpose in particulate trap. They found that the performance of wall-flow type ﬁlters is much better for practical application due to their high ﬁltration efﬁciency (>95%). In another study, different perovskite catalysts were formulated by using combustion

Fig. 7.6 Conversion of CO oxidation over perovskites as a function of reaction temperature; (O) LaMnO3 and (∎) Ag/MnOx/perovskite (Ag/ La = 3/7) under the reaction conditions; SV = 30,000 h−1 and 1% CO in air (Song et al. 1997)

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Fig. 7.7 Three-way catalytic performance of La1−xSrxMO3: () x = 0, () x = 0.2, () x = 0.4, () x = 0.6, () x = 0.8, and () N2 as well as () N2O selectivity of La0.8Sr0.2MO3 at k = 1.00 and SV = 60,000 h−1 (He et al. 2001)

synthesis method (Fino et al. 2003b). It was observed that the activity order for soot combustion was LaCrO3 > LaFeO3 > LaMnO3 while this sequence was found reverse in case of methane combustion (Fig. 7.8). Labhsetwar et al. (2004) have investigated perovskite catalysts for their possible application in DOC and DPFs. Temperature-programmed desorption studies show that perovskites have a good capability of oxygen desorption at low temperature, which helps in soot oxidation. Their laboratory evaluation of perovskite catalysts shows that the oxidation of soot is possible in the temperature limit of 300–450 °C under tight contact condition. La0.8Sr0.2M(1 − x)RhO3 (where M = Mn, Co, Fe and x = 0, 0.1) perovskite materials using microemulsions were devised and tested for NOx and NH3 reduction which shows enhanced catalytic activity (Wallin et al. 2004). Wu et al. (2004) have prepared La(1 − x)Sr(x)MnO3 (x = 0, 0.1, 0.3, 0.5, 0.7) perovskite catalysts by using co-precipitation method for different calcination temperatures and tested for NO reduction for known gas composition. They observed that La0.7Sr0.3MnO3 catalyst reduces 61% NO at 360 °C in the presence of 10% excess oxygen when it was calcined at 900 °C. The performance of LaCrO3 perovskite with La–Cr sub-stoichiometric or alkali metal-substituted perovskites (La0.9CrO3, La0.8CrO3, La0.9Na0.1CrO3, La0.9K0.1CrO3, La0.9Rb0.1CrO3,

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Fig. 7.8 Results of the screening tests on the activity T50, half-conversion temperature of methane, and Tp, peak temperature of CO2 production during soot combustion, BET speciﬁc surface area and activation energy of the various perovskite catalysts developed. Data concerning the non-catalytic carbon combustion are also shown for a comparison (Fino et al. 2003b)

La0.8Cr0.9Li0.1O3) was compared (Russo et al. 2005). It was observed that the best performance was delivered by the catalyst La0.8Cr0.9Li0.1O3 which was active well below 400 °C (Fig. 7.9). Weakly bonded O surface species is considered as a critical factor for the soot combustion at low temperature. Zhang-Steenwinkel et al. (2005) have examined the dielectric heating of a monolith soot ﬁlter coated with La0.8Ce0.2MnO3 perovskite. Due to the high dielectric loss factor, the catalyst is good for dielectric heating. It was observed that soot which covered the catalyst got converted into CO2 with almost 100% efﬁciency. This catalyst coating also has very good thermal shock resistance and

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Fig. 7.9 Results of the temperature-programmed combustion (TPC) runs performed with all the selected perovskite catalysts (Russo et al. 2005)

thermochemical stability. Stege et al. (2011) have prepared and evaluated La(1 − x) Ca(x)MnO3 catalyst for the oxidation of VOCs and observed that the total conversion of ethanol and n-heptane reached at 230 and 365 °C, respectively (Fig. 7.10). SiTiO3-based mixed oxide perovskite catalyst was devised by Ura et al. (2011). They promoted it with potassium (2 mol%) during the preparation as well as after the impregnation to form Si0.8K0.2TiO3 and K/SiTiO3. After characterization, they performed the soot oxidation for these catalysts and observed a signiﬁcant reduction of 100 and 120 °C in the ignition temperature of soot for K/SiTiO3 and Si0.8K0.2TiO3, respectively. Löpez Suárez et al. (2011) have conducted a study of

Fig. 7.10 Conversion of ethanol and n-hexane as a function of the reaction temperature (Stege et al. 2011)

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Table 7.4 Biodiesel and diesel soot characteristics (Löpez Suárez et al. 2011) H (%)

N (%)

S (%)

O (%)a

SBET (m2/g)

Ash (wt%)b

Volatile matter (%)b

Biodiesel 82.15 2.70 Diesel 86.08 3.73 a—Calculated by difference b—Dry basis

0.69 0.63

0.25 0.14

14.21 9.42

91 86

4.3 1.2

30.4 34.5

Soot

C (%)

the uncatalyzed and catalysed combustion of diesel and biodiesel soot. They used two copper-based catalysts (5%Cu/Al2O3 and Sr0.8K0.2Ti0.9Cu0.1O3). They observed that there is a double advantage with biodiesel fuel. They found that the reactivity of biodiesel soot is higher than that of the diesel soot. The higher reactivity of biodiesel soot is because of the metal with catalytic activity. These metals come in the fuel during the synthesis of fuel which helps in increasing the reactivity of biodiesel soot. They found that the soot production rate and uncatalyzed combustion rate are equal for biodiesel soot at 515 °C. For copper catalyzed, it is 450 ° C, and for diesel, it is much higher. Table 7.4 shows the biodiesel and diesel soot characteristics (Löpez Suárez et al. 2011). Figures 7.11a, b shows the soot conversion in percentage for diesel and biodiesel with two different catalysts mentioned above in Table 7.4. It has been observed that the uncatalyzed combustion of diesel and biodiesel occurs at the same temperature range, but biodiesel shows higher reactivity. It is because of some metallic component (Mg, K, Cu, Cr) and the higher oxygen content of the biodiesel. It has also been also found that the application of the catalysts increases the combustion rate with both the fuels. Finally, in their work, they concluded that biodiesel soot has higher reactivity than diesel because of higher oxygen content and metallic elements with catalytic activity.

Fig. 7.11 Conversion proﬁles in TRP experiments: a soot conversion for diesel soot, b soot conversion for biodiesel soot (Löpez Suárez et al. 2011)

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Summary

Studies have been attempted to test non-noble metal-based catalysts for the soot oxidation application under laboratory environment or with a real diesel engine exhaust. Non-noble metal-based catalyst can be prepared at lower costs which can be developed for comparable performance with the adoption of certain preparation methods and catalysts. Non-noble metal-based catalyst can be prepared at lower costs which can be developed for comparable performance with the adoption of certain preparation methods and catalysts. The performance of the catalyst for soot reduction, its durability, stability, and activity at elevated temperature is some of the main criteria for a suitable catalyst for the soot oxidation. It is ironic that India has a huge demand for transportation fuel like diesel for its energy fulﬁllment which causes pollution and India also needs to control the emissions coming from this transportation sector. Therefore, it is necessary to develop and evaluate the non-noble metal-based diesel oxidation catalysts for the engine emission control from the Indian perspective of energy availability and vehicular emissions.

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Chapter 8

Ceria-based Mixed Oxide Nanoparticles for Diesel Engine Emission Control P. K. Shihabudeen, Ajin C. Sajeevan, N. Sandhyarani and V. Sajith

Abstract One of the effective methods for the control of harmful emissions from diesel engines is the use of fuel-borne catalyst. Ceria is commonly used as a redox catalyst, and the catalytic activity of ceria decreases due to particle sintering, especially at high temperatures. The catalytic activity of ceria nanoparticle can be improved by doping it with transition metals such as zirconium, yttrium. A comparative study on the catalytic activity and various physicochemical properties of CeyZr1−yO2, CeyY1−yO2, and CexZryY1−x−yO2 mixed oxide nanoparticles, synthesized by co-precipitation method, is presented in this chapter. The synthesized mixed oxide nanoparticles of ceria were characterized using scanning electron microscopy (SEM), X-ray diffraction (XRD), Raman spectroscopy, thermogravimetric analysis (TGA), and Brunauer–Emmett–Teller (BET) analysis. The catalytic activity of ceria and its mixed oxide nanoparticles was compared by means of temperature-programmed reduction with H2 (H2-TPR) technique. The catalytic nanoparticle-dispersed diesel was prepared by mixing mixed oxide nanoparticles in diesel, with oleic acid as surfactant by means of ultrasonicator. Stability studies were done to optimize the concentration of catalytic nanoparticles in diesel for maximum stability. Engine studies on a four-stroke single-cylinder diesel engine show a reduction in the engine exhaust emissions, especially smoke, which agrees with the TPR study.

Keywords Diesel engine emissions Particulate matters Oxygen storage capacity Nanofluids

Ceria

P. K. Shihabudeen A. C. Sajeevan N. Sandhyarani V. Sajith (&) School of Nano Science and Technology, National Institute of Technology Calicut, Kozhikode, Kerala, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2019 A. K. Agarwal et al. (eds.), Advanced Engine Diagnostics, Energy, Environment, and Sustainability, https://doi.org/10.1007/978-981-13-3275-3_8

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Introduction

Diesel engines are widely used all over the world as a prime mover for transportation (Wallington and Wiesen 2014; Dhanasekaran and Mohankumar 2014) and also for power generation (Breeze 2005). The popular usage of diesel engines is attributed to its high-performance characteristics and higher thermal efﬁciency as compared to spark ignition (SI) engines. Even though diesel engines are much more economical and efﬁcient than the petrol engines, they are major contributors of harmful exhaust emissions such as volatile organic fraction (VOF) of burned and unburned hydrocarbons, carbon dioxide (CO2), carbon monoxide (CO), polynuclear aromatic hydrocarbons (PAHs), particulate matter (PM), carbon soot, oxides of nitrogen (NOx), aldehydes, sulfur oxides, which are mainly responsible for biological phenomena such as acid rain and photochemical contamination. Particulate matter or soot is a by-product of incomplete combustion of diesel, consisting of unburned long-chain hydrocarbons, chemicals like sulfates, ammonium, nitrates, elemental carbon, carcinogenic compounds, and heavy metals. Size of soot varies from small particles of nanometers to large particles of micrometers (Fino et al. 2007; Lloy and Cackette 2001). Smoke is a visible product of combustion, which occurs in the early stages of combustion cycle in the localized volume of rich air–fuel mixture. It is formed by the aggregation of chain line clumps of carbon in the exhaust. Exposure to fumes of diesel engines leads to irritation to eyes and respiratory tracts, and prolonged exposure to diesel fumes leads to cough and breathlessness. Diesel engine emissions cause cancer (Vermeulen et al. 2014), respiratory and cardiovascular health effects (Mauderly et al. 2014), air and water pollution, reduction in visibility, and global climate change (Lloy and Cackette 2001). The harmful components of diesel engine exhaust gas become a major threat for the environment. Despite these ill effects, the usage of diesel as a fuel in CI engines is increasing, as diesel engines are inevitable in all sectors of life, as a prime mover for transportation and stationary applications. Hence, stringent environmental legislation is essential to control these harmful emissions. Extensive research has been carried out worldwide for controlling the harmful emissions, thereby adhering to stringent regulations. Various exhaust gas after-treatment technologies being adopted for the control of harmful emissions include electronic fuel injection system (Parlak et al. 2012; Youn et al. 2011; Yao et al. 2010), diesel particulate ﬁlter (DPF) for PM reduction (Hsieh et al. 2011), NOx absorber catalyst, selective catalytic reduction (SCR), and exhaust gas recirculation (EGR) for the reduction of nitrous oxides (NOx) (Basha et al. 2014; Ibrahim and Ramesh 2013; Yamashita et al. 2014). The toxic diesel engine emissions cannot be controlled solely by modiﬁcations in engine or exhaust after-treatment systems. An attractive method for the reduction of harmful emissions, especially particulate matters, is the use of metal oxides as catalysts in fuel, to catalyze the combustion reactions, resulting in the reduction of emissions (Brijesh and Sreedhara 2013). Catalysts can be used in the form of coating in catalytic convertors, diesel particulate ﬁlters, etc., or as fuel-borne

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additive, especially in nanosized form. Advantage of using catalytic nanoparticle in fuel is increase in the catalytic reaction time, as catalytic reactions occur in engine cylinder and exhaust pipe also. Due to the small size, nanoparticles have high surface-to-volume ratio which increases the surface energy than bulk material. Nanomaterial catalyst has high reactivity and selectivity as compared to non-nanocatalysts, and hence, the concentration required also will be less. Various metal oxide nanoparticles used as fuel-borne additives include aluminum oxide, copper oxide, and ceria (Mehta et al. 2014). Among these metal oxides, ceria is a potential catalyst for both oxidation and reduction of harmful diesel engine emissions.

8.2

Cerium Oxide—An Excellent Catalyst

Cerium oxide is an abundant element in rare earth family group with good thermal stability. Cerium oxide is a highly stable, refractory ceramic material with a melting point of 2600 °C and a density of 7.13 g/cm3. In the face-centered cubic (FCC) structure of ceria as shown in Fig. 8.1, Ce4+ ions form a cubic close-packing arrangement and oxide ions occupy all the tetrahedral sites, whereas the octahedral sites remain vacant. The crystal structure is fluorite face-centered cubic with a lattice constant of 5.11 Å and has exceptional magnetic and electronic properties due to their unﬁlled 4f electronic structure. In the case of cerium oxide nanoparticles, most of the Ce ions are located near the surface, which enhances the stability of oxygen vacancies, as compared to the bulk. Cerium oxide has an electronic conﬁguration of 4f25d 6s2, and it can exist in cerium oxide (CeO2) or cerous oxide (Ce2O3) form, i.e., with oxidation states of +3 and +4, respectively. Being thermodynamically unstable, the conversion of cerium oxide (CeO2) to cerous oxide (Ce2O3) takes place easily and the conversion mainly depends on the partial

Fig. 8.1 Structure of ceria (Eyring 1991)

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pressure of oxygen and temperature. The +3 state closely resembles the other trivalent rare earths, the +4 state is stable in an aqueous environment and it is, therefore, a strong oxidizing agent. At elevated temperature and low oxygen pressure, the CeO2 reduces to oxygen-deﬁcient non-stoichiometric oxides and will reorganize on cooling. Cerium oxide is a good antioxidant owing to the presence of oxygen vacancies on its surface and the auto-regenerative cycle of its oxidation states, Ce3+ and Ce4+. Being an efﬁcient oxygen buffer, cerium oxide enhances the reduction and oxidation reactions under fuel-rich and lean conditions, respectively, and hence can be used as an excellent catalyst for the reduction of harmful emissions from diesel engines (Mullins et al. 1998; Das et al. 2007; Qiu et al. 2006; Zhang et al. 2004, 2009; Huang et al. 2009; Vidmar et al. 1997). In a diesel engine, a signiﬁcant amount of soot is formed during its operation, which may adhere to the surface of combustion chamber as deposits, along with the lubricating oil mist. These carbon deposits lead to the friction losses and variations in the internal surface heat transfer behavior. The addition of ceria nanoparticles in diesel leads to a reduction in the activation temperature of carbon, and the carbon deposits get oxidized, leading to cleaner and efﬁcient engine. Cerium oxide provides the oxygen for the reduction of the soot as well as the hydrocarbon and gets converted to cerous oxide (Ce2O3), which in turn is reoxidized to CeO2 through the reduction of nitrogen oxide, as per the following reactions. Hydrocarbon combustion: ð2x þ yÞCeO2 þ Cx Hy !

x y 2x þ y Ce2 O3 þ CO2 þ H2 O 2 2 2

ð8:1Þ

Soot burning: 4CeO2 þ Csoot ! 2Ce2 O3 þ CO2

ð8:2Þ

NOx reduction: Ce2 O3 þ NO ! 2CeO2 þ

1 N2 2

ð8:3Þ

Various commercial applications of cerium include metallurgy, glass and glass polishing, ceramics, phosphors, and catalysts. Ceria (CeO2) is commonly used as an oxygen ion conductor in solid oxide fuel cells and oxygen pumps due to its high oxygen ion conductivity. Cerium has high refractive index and is used as a pacifying agent in glass polishing. Being an antioxidant agent, cerium oxide nanoparticle is considered to be one of the most interesting nanomaterials for application in therapy, as many disorders are due to the oxidative stress and inflammation (Xia et al. 2008; Horie et al. 2011). The toxicity studies of CeO2 nanoparticle on human health, based on local site of contact (dermal) irritation, general cytotoxicity, mutagenicity, and environmental effects, revealed that there is

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no difference in biological effects between non-nano- and nanocerium oxide (Park et al. 2007). The catalytic activity of ceria is mainly influenced by its oxygen storage capacity (OSC). A major drawback of ceria is that signiﬁcant deactivation occurs due to particle sintering, especially at high temperatures, which will diminish the OSC of ceria particle. Doping of ceria is one of the methods to improve its thermal stability. Ceria-based binary and ternary mixed oxides have been developed for achieving thermal stability, even at elevated temperatures. The modiﬁcation of ceria by doping with transition metals, such as La3+, Zr4+, Y3+, is likely to improve the surface area, enhance the redox properties of ceria, and also prevent the decline of oxygen storage capacity due to thermal deactivation (Shehata et al. 2012). Studies show that, out of these lanthanides, zirconium- and yttrium-based mixed oxides showed most promising results (Scheffe et al. 2013). Incorporation of zirconium into ceria structure results in modiﬁcations involving non-equivalent oxygen atoms. The oxygen anions near to the doping center have considerably lower reduction energies and larger displacements, resulting in higher mobility of ions. An oxygen vacancy is most easily created close to zirconium centers, and hence, Zr doping centers might serve as nucleation centers for vacancy clustering (Yang et al. 2006) causing the distortion of the O2− sub-lattices. The distortion of the O2− sub-lattices in the mixed oxides permits a higher mobility of lattice oxygen. Hence, the reduction not only occurs on the surface but also extends deep into the bulk (Damyanova et al. 2008; Yeste et al. 2013). In the case of yttrium, the Y3+ surface enrichment hinders the crystallite growth (Atribak et al. 2009). Yttrium doping increases the oxygen ion conductivity of ceria, and the surface segregation of Y3+ results in oxygen vacancies (Dudek and Molenda 2006; Shih et al. 2011). When both zirconium and yttrium are used as dopants, the deformation on the lattice due to zirconium doping favors yttrium incorporation, while zirconium promotes the formation of cerium-rich surfaces and yttrium hinders the accumulation of cerium on the surface (Atribak et al. 2009). The addition of catalytic nanoparticles in diesel increases the time of catalytic reaction leading to better reduction of harmful emissions. Though the use of catalysts in nanoscale form as fuel-borne additive has got much more attention recently, one of the main challenges is the lack of stability of the catalytic nanoparticles in fuel. Most of the earlier works reported the enhancement in the efﬁciency and reduction of emissions, with the use of catalytic nanoparticle in diesel, but much emphasis has not been given on stability aspect. This chapter reports a comparative study on the catalytic activity and various physicochemical properties of CeyZr1−yO2, CeyY1−yO2, and CexZryY1−x−yO2 mixed oxide nanoparticles. The nanoparticles were synthesized using co-precipitation method and were characterized using scanning electron microscopy (SEM), X-ray diffraction (XRD), UV–Vis spectroscopy, Raman spectroscopy, temperatureprogrammed reduction with H2 (H2-TPR), thermogravimetric analysis (TGA), and Brunauer–Emmett–Teller (BET) analysis. Mixed oxides of ceria were dispersed in diesel, and their stability in diesel was improved by the use of surfactant. Oleic acid was used as the surfactant. Stability of catalytic nanoparticle-added diesel was studied systematically by means of zeta potential measurements. Effect of these catalytic

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nanoparticles on the performance and emissions of a water-cooled four-stroke single-cylinder diesel engine was also investigated.

8.3 8.3.1

Synthesis of Ceria-Based Mixed Oxide Nanoparticles Precipitation Method

Precipitation method is one of the popular methods for the synthesis of cerium oxide nanoparticles. Chemicals required are reagent-grade cerium nitrate (Ce (NO3)36H2O) (Sigma-Aldrich, purity 99%), analytical-grade isopropanol, and 3 M aqueous ammonia. Cerium (III) nitrate solution (0.08 M) in water–isopropanol mixture (1:6) was prepared and vigorously stirred by means of a magnetic stirrer. Fivefold excess of 3 M aqueous ammonia solution is added to the stirring solution, while noting down the pH of the solution. The pH was adjusted to 10 by adding ammonium hydroxide to the reacting solution, as alkaline medium contributes smaller particles than acidic one. The reaction was left stirring at room temperature for 2 h. After one hour of the reaction, the red color of reactants turned yellow, showing the formation of cerium oxide nanoparticles. The particles precipitate at the bottom of the round bottom flask after completion of reaction. The resultant precipitate was washed three times with isopropanol to wash out contaminants and unreacted reagents. Samples were puriﬁed by centrifuging the solution. The resultant precipitate was then dried at 60 °C in an oven for 2 h, and nanocrystalline cerium oxide was obtained. The reaction is as follows. Precipitation of cerium hydroxide CeðNO3 Þ3 6H2 O þ NH4 OH ! CeðOHÞ4 þ NH4 NO3

ð8:4Þ

Conversion of amorphous cerium hydroxide to cerium oxide on heating D

Ce(OH)4 !CeO2 2H2 O

8.3.2

ð8:5Þ

Co-precipitation Method

Cerium zirconium mixed oxide nanoparticles were synthesized by co-precipitation method using Ce (IV) precursor (Rossignol et al. 1999). The chemicals used in this method are ammonium cerium (IV) nitrate, zirconium oxychloride (ZrOCl28H2O), yttrium nitrate (Y(NO3)36H2O), and aqueous ammonia. The precursors were dissolved in distilled water to obtain desired molarity as shown in Table 8.1 and stirred for 30 min at 60 °C by means of a magnetic hot plate stirrer. To this solution, aqueous ammonia (NH3H2O) was added drop-wise, and a pale purple precipitate

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Table 8.1 Precursors for mixed oxide preparation Sample

Ce(NO3)36H2O

ZrOCl28H2O

Y(NO3)36H2O

Ce0.90Zr0.10O2 Ce0.80Zr0.20O2 Ce0.70Zr0.30O2 Ce0.95Y0.05O2 Ce0.90Y0.10O2 Ce0.85Y0.15O2 Ce0.80Zr0.10Y0.10O2 Ce0.70Zr0.20Y0.10O2 Ce0.60Zr0.30Y0.10O2

90 80 70 95 90 85 80 70 60

10 20 30 0 0 0 10 20 30

0 0 0 5 10 15 10 10 10

started to form, which turned to light yellow on continuous stirring. Ammonia addition was continued till the pH of the solution reached more than 10. The solution was further stirred for two hours to get monodispersed particles. The precipitate was ﬁltered and washed repeatedly with water to remove excess ammonia and unreacted precursors. The sample was dried for 8 h in an oven at 60 °C, and the yellow powder obtained was grounded in a mortar to get ﬁne powder. The powder was then calcined at 500 °C for 4 h.

8.3.3

Flame Spray Pyrolysis

Flame spray pyrolysis (FSP) is a popular technique for the synthesis of high-purity nanosized materials with controlled size and crystallinity in a single step. A wide array of high-purity nanopowders ranging from single to complex mixed oxides, metals, and catalysts can be synthesized by means of FSP. In this method, flame is used to force chemical reactions of precursors resulting in the formation of clusters, which increase their size to a range of nanometers by coagulation and sintering. Flame spray pyrolysis is a single-step process in which a metal precursor(s) dissolved in a solvent is sprayed with an oxidizing gas into a flame zone. The spray is combusted, and the precursors are converted to nanosized metal or metal oxide particles, depending on the operating conditions. FSP allows the use of a wide range of precursors, solvents, and process conditions, with proper control over particle size and composition. In flame spray pyrolysis (FSP) process, the chemical and physical properties of nanoparticles depend on various parameters such as the burner design, gas-to-liquid mass ratio, oxygen content of the dispersion gas, atomization, fuel and precursor properties, concentration of the precursor in the solution. Basically, it is necessary to optimize all of the relevant experimental parameters in order to control the phase, particle size, and morphology. Figure 8.2 shows the experimental setup of FSP process. FSP setup consists of a burning chamber made of quartz tube with a ﬁlter at the top and a spray nozzle at

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Fig. 8.2 Flame spray pyrolysis setup

the center surrounded by six nozzles between two brass plates. A ﬁlter is connected to a vacuum pump to draw the combustion products from the quartz tube. The spray nozzle made of stainless steel is used to pump the precursor to the burning chamber. The spray nozzle consists of a capillary tube inside another tube. Precursor and fuel flow through the capillary tube, while the dispersion gas passes through the annular gap. The annular gap is controlled to vary the atomization of the precursor and hence the flame height. Cooling water circulation was provided around the nozzle to maintain the nozzle temperature. Cerium(III) acetate hydrate and zirconium acetylacetonate were used as precursors for the synthesis of cerium zirconium mixed oxide nanoparticles. The precursors were mixed and dissolved in acetic acid mixture, pumped to center spray nozzle, with the aid of a syringe pump, and further atomized by means of oxygen gas. A mixture of methane and oxygen was fed to the six nozzles around the precursor nozzle to obtain premixed supporting flames. The precursor burns in the flame at the center nozzle forming nanoparticles, which are collected in a ﬁlter paper with the aid of a vacuum pump. The ﬁlter paper was washed in toluene to recover the nanoparticles adhered on it. The collected samples were again washed and centrifuged with toluene to remove excess amount of impurities.

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151

Characterization of Catalytic Nanoparticles

The particle size and morphology of the nanoparticles were characterized by means of scanning electron microscope (make: Hitachi SU6600 Field Emission Scanning Electron Microscopy (FESEM)). BET surface area of the catalysts was estimated by physical adsorption of N2 at −196 °C in an automatic volumetric system (make: Autosorb-6, Quantachrome). X-ray diffraction studies were done using Bruker AXS D8 Advance diffractometer (Cu Ka radiation k = 1. 5406 Å). Thermogravimetric analysis was performed to study thermal stability of particles (make: NETZSCH STA 449 F3 thermal analyzer). Raman spectra were acquired with a Bruker RFS 100/S Fourier Transform Spectrometer (Nd:YAG laser source 1064 nm, 85 mW laser power). Experiments of temperature-programmed reduction with H2 (H2-TPR) were performed in a tubular quartz reactor coupled to a TCD analyzer for monitoring H2 consumption. The experiments were conducted with 50 mg of catalyst, while maintaining the mass flow rate as 30 ml/min flow of 7.7 vol.% H2 in argon atmosphere.

8.4.1

Textural and Structural Properties

The SEM (Fig. 8.3) images show that the particles are spherical in shape with a mean diameter of 60 nm. SEM images also conﬁrmed that the size or morphology of nanoparticles is not affected by the formation of mixed oxide with zirconium and yttrium. BET surface area of the mixed oxides is estimated and presented in Table 8.2. Highest surface area of 116.3 m2/g was observed for the sample CexZryY1−x−yO2 nanoparticle as compared to others, due to a larger number of defects on the surface, and CeyY1−yO2 nanoparticles have the lowest surface area of 44 m2/g. From the point of view of surface area or the oxygen release from the surface, it can be seen that sample CexZryY1−x−yO2 nanoparticles exhibit more catalytic activity, as compared to other samples. Mixed oxides of cerium, zirconium, and yttrium can be synthesized by means of two methods. In the ﬁrst method, oxides of cerium, zirconium, and yttrium are synthesized separately and mixed physically to obtain the mixture of oxides such as CeO2–ZrO2, CeO2–Y2O3, and CeO2–ZrO2–Y2O3. In the second method, mixed oxides of cerium, zirconium, and yttrium are synthesized by means of co-precipitation method. XRD patterns for the mixed oxides synthesized by means of co-precipitation method and by physical mixing of oxide nanoparticles are compared and are shown in Fig. 8.4. X-ray diffraction pattern shows the peaks’ characteristic for fluorite-type structures, with planes corresponding to (1 1 1), (2 0 0), (2 2 0), and (3 1 1) (Atribak et al. 2009). Due to the incorporation of zirconium and yttrium, a shift of 2h to higher angles was observed, which conﬁrms the formation of solid solution (Yu et al. 2003). Small peaks were also observed in the XRD patterns, due to ordered

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Fig. 8.3 SEM images of a CeyZr1−yO2, b CeyY1−yO2, and c CexZryY1−x−yO2 mixed oxide nanoparticles

Table 8.2 Surface area of various mixed oxide nanoparticles Mixed oxide nanoparticle

Surface area (m2/g)

CeyZr1−yO2 CeyY1−yO2 CexZryY1−x−yO2

64.4 44 116.3

Table 8.3 Speciﬁcations of engine Type

Four strokes, diesel, vertical, water cooled, single cylinder (Kirloskar AV1)

Bore and stroke Compression ratio Capacity Rated output Dynamometer

80 mm 110 mm 16.5:1 553 CC 3.7 kW/5 hp Eddy current, water cooled with loading unit

arrangement of cations (Nagai et al. 2008). For physically mixed samples, two small peaks are observed, corresponding to (4 0 0) and (6 6 2) planes which are the characteristic planes of yttria and other two peak indexes on planes (1 1 2) and (1 2

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Fig. 8.4 X-ray diffraction patterns of various mixed oxide nanoparticles

1) being the characteristic planes of zirconia (Nagai et al. 2008). Mixed nanoparticles have broader peaks which indicates semicrystalline structure (Yu et al. 2003). The absence of characteristic planes of ZrO2 and Y2O3 in XRD patterns of in situ synthesized mixed nanoparticles also conﬁrms the formation of a solid solution. Raman spectra of CeO2, CeyZr1−yO2, CeyY1−yO2, and CexZryY1−x−yO2 are shown in Fig. 8.5, where the Raman active F2g mode which can be regarded as a symmetric O–Ce–O stretching for the cubic fluorite structure is compared (Ma et al. 2009). In the Raman spectrum of pure ceria, which has a fluorite structure, the F2g mode is centered at 465 cm−1, as seen in Fig. 8.5. A shift in the peak is observed, mainly due to the coexistence of cubic (CeO2) and tetragonal (ZrO2) solid solution phase, as the band near to 465 cm−1 is observed together with the components typical of the tetragonal phase, though the cubic phase is still largely predominant.

Fig. 8.5 Raman spectra of various mixed oxide nanoparticles

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Comparison of Raman spectra of CeO2 and CeyZr1−yO2 samples shows that the formation of mixed oxides of ceria with zirconium improves the oxygen vacancies. Since Y2O3 has cubic structure and there is no formation of bonds with yttrium incorporation, CeyY1−yO2 exhibits the same Raman band as CeO2.

8.4.2

Thermal and Catalytic Properties

Thermal stability of mixed oxide is of utmost important, as it has to withstand high in-cylinder temperature, when mixed with diesel as fuel additive. TGA of calcined and uncalcined samples for a temperature range of 25–950 °C is shown in Fig. 8.6. For calcined sample, there is only a weight loss of 6.4%, whereas in the case of uncalcined sample a weight loss of 65% was observed. The weight loss below 100 °C is mainly due to the desorption of water. The weight loss above 100 °C is due to the removal of methoxide (Gnanam and Rajendran 2011) in the mixed oxide nanoparticles, and as temperature goes up, the complete removal of organic residues takes place. Catalytic activity of mixed oxides of ceria mainly depends on its reducibility. Temperature-programmed reduction (TPR) with hydrogen is a standard technique to characterize the reducibility of ceria-based materials. Samples selected for H2TPR analysis include Ce0.90Zr0.10O2, Ce0.80Zr0.20O2, Ce0.85Y0.15O2, Ce0.90Y0.10O2, Ce0.70Zr0.20Y0.10O2, and Ce0.60Zr0.30Y0.10O2, and Fig. 8.5 shows the results obtained. The literature (Mehta et al. 2014; Yao and Yao 1984; Kennedy 1975) shows that the TPR proﬁle of ceria has a two-peak pattern, one corresponding to surface reduction (around 500 °C) and the other for the bulk reduction (around 900 °C) peak, showing that the reduction of ceria may follow a two-step process. On doping cerium with zirconium and yttrium, TPR proﬁles of the mixed oxides essentially show a broad reduction signal in agreement with the expectation of an enhanced reduction of the bulk mixed oxide (Atribak et al. 2009; Meng et al. 2010). Fig. 8.6 Thermogravimetric analysis of calcined and uncalcined mixed oxide nanoparticles

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TPR results show a shift in peaks toward lower temperatures for the CeZrO2 mixed oxide nanoparticles. There exists a small peak around 450 °C which is the surface reduction peak and a broad peak around 600 °C which is the bulk reduction region (Meng et al. 2010). It was observed that the catalytic activity is increased with increase in the doping level of ZrO2 and Ce0.80Zr0.20O2 gives better result. Shift in peaks for Y2O3-doped CeO2 nanoparticle is less compared to CeyZr1−yO2 nanoparticle. Among the yttrium-doped CeO2 nanoparticle, the sample Ce0.85Y0.15O2 nanoparticle shows the best catalytic property. In this case, a broad bulk reduction region was observed at a temperature near to 750 °C (Wang et al. 2003). On comparison with TPR results of various mixed oxides of ceria (Fig. 8.7), the mixed oxides containing both zirconium and yttrium were found to possess the best catalytic activity. Ce0.60Zr0.30Y0.10O2 mixed oxide nanoparticles show best catalytic activity even though it has only a single sharp peak; if the H2 consumption is compared, it has a sharp peak near 550 °C which is the surface reduction peak. It was also observed that as the proportion of zirconium increases while keeping the concentration of yttrium constant, catalytic activity also increases. On the basis of comparison of TPR results of various mixed oxides of cerium, Ce0.80Zr0.20O2, Ce0.85Y0.15O2, and Ce0.60Zr0.30Y0.10O2 mixed oxide nanoparticle samples were selected as additives in diesel for the stability studies and engine performance and emission studies.

Fig. 8.7 H2-TPR of various mixed oxide nanoparticles

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Synthesis of Nanofluid

Nanofluids can be prepared by a single-step or two-step method. A two-step approach of the synthesis of nanofluid is more commonly used, especially for the bulk synthesis of nanofluids. In a two-step method, the catalytic nanoparticle is mixed with oleic acid, by means of ultrasonic shaker for the duration of 90 min. This highly concentrated additive was then dispersed in diesel and is sonicated by means of ultrasonic shaker for the duration of 30 min. Lack of stability of catalytic nanoparticles in diesel is one of the main challenges to be addressed for their practical applications in diesel engines. The stability of catalytic nanoparticle dispersed in diesel was improved with the addition of oleic acid as surfactant. Optimized concentration of oleic acid in diesel for maximum stability of catalytic nanoparticles was determined based on the estimation of the formation of reverse micelle. The addition of surfactant in diesel leads to a decrease in surface tension and eventually the aggregation of surfactant molecules, as the concentration of surfactant increases. The concentration of surfactant at which sudden variation of surface tension occurs, corresponding to the formation of reverse micelle (critical micelle concentration), was measured by maximum bubble pressure method (Fainermanl et al. 1994). In this method, the pressure required to force a gas bubble out of a capillary tube which is vertically immersed in the liquid to be investigated changes with the surface tension of the liquid. An air pump was used for applying pressure and a valve to control the air flow. The variation in pressure was noted using a differential manometer with the aid of a CCD camera. The pressure required to force a gas bubble through diesel out of a capillary was determined, while varying the concentration of oleic acid in the range 0.01–0.1 vol. % and the surfactant concentration corresponding to the point of minimum pressure; i.e., the point of reverse micelle formation was determined. Figure 8.8 shows the variation of bubble pressure with surfactant concentration in volume percentage. A sudden drop in pressure was observed at a surfactant concentration of 0.05% by volume, which corresponds to optimum surfactant concentration. The minimum concentration value corresponds to the formation of micelle of surfactants in the base fluids. Stability of surfactant-coated mixed oxide of cerium was compared with uncoated mixed oxide of cerium.

8.5.1

Stability Study

Stability of nanofluid, i.e., catalytic nanoparticle-added diesel, was estimated from the measurement of zeta potential by using dynamic light scattering technique (Malvern Zetasizer Nano ZS). Particle with zeta potentials more positive than +30 mv and more negative than −30 mv is considered to be stable. The concentration of catalytic nanoparticle in diesel was varied from 2.5 to 15 ppm, with a 2.5 ppm interval. Surfactant was added in diesel, by employing two methods. In the

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Fig. 8.8 Variation of pressure with concentration of oleic acid in diesel

ﬁrst method, catalytic nanoparticle was coated with surfactant by mixing it with oleic acid and toluene and stirred for 24 h. The mixture was then centrifuged and dried to obtain the surfactant-coated catalytic nanoparticles, which is then added to diesel. In the second method, oleic acid was added along with the catalytic nanoparticles and mixed thoroughly by means of ultrasonic shaker. Stability of surfactant-coated and surfactant-uncoated nanoparticles was also compared, in order to determine the best method of stabilization of catalytic nanoparticle in diesel. Figure 8.9 shows the variation of zeta potential with respect to concentrations for (a) CeyZr1−yO2, (b) CeyY1−yO2, and (c) CexZryY1−x−yO2 mixed oxide nanoparticles. It was found that both coated and uncoated nanoparticles show good stability (more than ±30 mV). Out of three mixed oxide nanoparticles, the CeyZr1 −yO2 mixed oxide nanoparticles show the highest stability and CeyY1−yO2 shows the least. Based on the stability studies, it was concluded that sample with 10 ppm concentration catalytic nanoparticles shows the maximum stability.

8.6

Engine Performance and Emission Study

Load test was carried out in a single-cylinder four-stroke diesel engine (Table 8.3) for investigating the effect of these nanoparticles on performance and emission characteristics of diesel engine. The experimental test rig consists of a single-cylinder four-stroke compression ignition engine, eddy current dynamometer, fuel supply system, and various sensors and instruments coupled with data acquisition system for online measurement of load, exhaust emissions, and smoke. The performance parameters were determined by conducting a constant speed load test. Commercially available laboratory view-based engine performance analysis software package—engine soft LV—was used for online performance

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Fig. 8.9 Stability analysis at various concentrations for a CeyZr1−yO2, b CeyY1−yO2, and c CexZryY1−x−yO2 mixed oxide nanoparticles

evaluation. The exhaust emissions of the engine were measured using a AVL exhaust gas analyzer (AVL DIGAS 444), and smoke intensity was measured in terms of ﬁlter smoke number (FSN) by means of AVL smoke meter (AVL Type 415SG002). Ce0.80Zr0.2O2, Ce0.85Y0.15O2, and Ce0.60Zr0.30Y0.10O2 mixed oxide nanoparticles were selected for performance and emission studies, based on the oxygen storage vacancies of catalytic nanoparticles, as mentioned earlier. Various emissions analyzed in the present work include smoke and NOx. The catalytic nanoparticle-added diesel was prepared by means of ultrasonic shaker and ﬁlled in the fuel tank. The concentration of catalytic nanoparticles in diesel was ﬁxed as 10 ppm in the present study, based on the stability studies. For each sample, two load tests were conducted and average results are presented here. It was observed that there is a considerable reduction in the emission of smoke and NOx emissions with the addition of mixed oxides of ceria nanoparticles in diesel. Figure 8.10 shows the variation of smoke with respect to the BMEP. A considerable reduction in smoke was observed up to half load for all samples. Among the samples, Ce0.80Zr0.20O2 mixed oxide nanoparticles show a maximum smoke reduction of 54% and an average reduction of 33%. In a diesel engine, smoke is mainly formed due to the lack of oxygen (Kennedy 1975) and pyrolysis and nucleation are the ﬁrst two steps in the soot formation.

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Fig. 8.10 Variation of smoke emission with BMEP for various nanoparticles

The presence of oxygen in ceria-based nanoparticles may reduce the chance of pyrolysis and nucleation (Lenin et al. 2013; Keskin et al. 2008), thus reducing the chance of soot formation as per Eq. (8.2). The doping of ceria with zirconium and yttrium increases the number of defects in the crystal lattice and hence the oxygen storage capacity. The high temperature in engine cylinder leads to the release of the oxygen from the crystal lattice of ceria-based mixed oxide nanoparticles, thus reducing the smoke. Figure 8.11 shows the variation of NOx emissions with respect to BMEP. Even though a considerable reduction in NOx emissions was observed at lower loads, an increase in NOx emissions was observed at higher loads for all samples. This may be due to the fact that at the higher loads the combustion chamber temperature is high, which will enhance NOx emissions as per Zeldovich thermal mechanism. Ce0.80Zr0.20O2

Fig. 8.11 Variation of NOx emission with BMEP for various nanoparticles

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nanoparticles show a maximum reduction of 9% and an average reduction of 3%, while Ce0.85Y0.15O2 nanoparticles show a maximum reduction of 17% and an average reduction of 5%, in NOx emissions. For Ce0.60Zr0.30Y0.10O2 nanoparticles, a maximum reduction of 23% and an average reduction of 6% were observed (Sprague et al. 2006). Figure 8.12 shows the variation of brake thermal efﬁciency with respect to BMEP and the entire samples exhibit an improvement in efﬁciency throughout the load range, while maximum enhancement was observed for Ce0.85Y0.15O2 sample. Addition of catalytic nanoparticles leads to the enhancement in combustion efﬁciency, through the enhancement of oxidation/reduction reactions in the cylinder, leading to the improvement of brake thermal efﬁciency. Ce0.85Y0.15O2 shows an average improvement of 4.5%, while Ce0.60Zr0.30Y0.10O2 nanoparticles show an improvement of 3% in the brake thermal efﬁciency. Possible explanations for the reduction of smoke and improvement of brake thermal efﬁciency are elucidated here. The catalytic nanoparticles in fuel result in the micro-explosion of fuel droplets leading to better atomization and hence shorter ignition delay (Mehta et al. 2014). In addition, the presence of catalytic nanoparticles in diesel increases the time available for the catalysis reaction and hence the emission reduction occurs up to the end of the exhaust tailpipe (Lenin et al. 2013). Even though higher reduction in the emissions and enhancement in brake thermal efﬁciency can be obtained by using higher concentration of mixed oxide of ceria nanoparticles, the maximum concentration is limited by the stability of the catalytic nanoparticles in diesel.

Fig. 8.12 Variation of BTE with BMEP for various nanoparticles

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8.7

161

Conclusion

Various ceria-based mixed oxide nanoparticles were synthesized by co-precipitation method. It was observed that the synthesized particles have an average size of 60 nm and have a spherical morphology. Characterization of synthesized particles using XRD and Raman spectroscopy conﬁrms the presence and the formation of mixed oxide nanoparticles. The thermal stability of nanoparticles was found to be increased by calcination. BET analysis shows that CexZryY1−x−yO2 nanoparticles have highest surface area. Temperature program reduction of samples with hydrogen shows that among all samples, and Ce0.60Zr0.30Y0.10O2 shows the best catalytic activity. The optimum amount of surfactant in diesel was found to be 0.05% by volume, and the mixed oxide nanoparticles in diesel show better stability at 10 ppm concentration. Load test on a single-cylinder diesel engine for performance and emission studies shows a considerable reduction in smoke as well as improvement in efﬁciency, with the addition of mixed oxide of ceria nanoparticles in diesel.

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Vermeulen R, Silverman DT, Garshick E, Vlaanderen J, Portengen L, Steenland K (2014) Exposure-response estimates for diesel engine exhaust and lung cancer mortality based on data from three occupational cohorts. Environ Health Perspect 122:172–177 Vidmar P, Fornasiero P, Kâspar J, Gubitosa G, Graziani M (1997) Effects of trivalent dopants on the redox properties of Ce0.6Zr0.4O2 mixed oxide. J Catal 171:160–168 Wallington TJ, Wiesen PA (2014) N2O emissions from global transportation. Atmos Environ 94:258–263 Wang JB, Hsiao SZ, Huang TJ (2003) Study of carbon dioxide reforming of methane over Ni/ yttria-doped ceria and effect of thermal treatments of support on the activity behaviors. Appl Catal A Gen 246:197–211 Xia T, Kovochich M, Liong M, Madler L, Gilbert B, Shi H, Yeh JI, Zink JI, Nel AE (2008) Comparison of the mechanism of toxicity of zinc oxide and cerium oxide nanoparticles based on dissolution and oxidative stress properties. ACS Nano 2:2121–2134 Yamashita A, Ohki H, Nakatani K, Tomoda T (2014) A control strategy of low-pressure loop exhaust gas recirculation and NOx storage catalyst for diesel engines. Int J Engine Res 15:565– 571 Yang Z, Woo TK, Hermansson K (2006) Effects of Zr doping on stoichiometric and reduced ceria: a ﬁrst-principles study. J Chem Phys 124:224704 Yao HC, Yao YFY (1984) Ceria in automotive exhaust catalysts. J Catal 86:254–265 Yao M, Wang H, Zheng Z, Yue Y (2010) Experimental study of n-butanol additive and multi-injection on HD diesel engine performance and emissions. Fuel 89:2191–2201 Yeste MP, Hernandez-Garrido JC, Arias DC, Blanco G, Rodriguez-Izquierdo JM, Pintado JM, Bernal S, Perez-Omil JA, Calvino JJ (2013) Rational design of nanostructured, noble metal free, ceria–zirconia catalysts with outstanding low temperature oxygen storage capacity. J Mater Chem A 1:4836–4844 Youn IM, Park SH, Roh HG, Lee CS (2011) Investigation on the fuel spray and emission reduction characteristics for dimethyl ether (DME) fueled multi-cylinder diesel engine with common-rail injection system. Fuel Process Tech 92:1280–1287 Yu JC, Zhang L, Lin J (2003) Direct sonochemical preparation of high-surface-area nanoporous ceria and ceria–zirconia solid solutions. J Colloid Interface Sci 260:240–243 Zhang F, Wang P, Koberstein J, Khalid S, Chan SW (2004) Cerium oxidation state in ceria nanoparticles using x-ray photoelectron spectroscopy and X-ray absorption near edge spectroscopy. Surf Sci 563:74–82 Zhang M, Li J, Li H, Li Y, Shen W (2009) Morphology-dependent redox and catalytic properties of CeO2 nanostructures: nanowires, nanorods and nanoparticles. Catal Today 148:179–183

Part IV

Simulations and Fault Diagnostics

Chapter 9

Model-Based Fault Detection on Modern Automotive Engines Deepak Agarwal and Chandan Kumar Singh

Abstract Engine control devices which are nowadays serving to improve the overall performance of modern automotive combustion engines involve both sophisticated digital control systems and complex electronic hardware such as input-output sensors, actuators, and processing units. Such complexity results in an increased probability of failure. To catch the failure, engine control units must have robust fault detection and isolation (FDI) capabilities that can prevent engine catastrophe and premature failure. For performing robust FDI, two approaches can be used either to have physical redundancy or to have analytical redundancy. Physical redundancy involves putting redundant physical devices such as sensors and actuators that can catch faults as and when they occur. However, analytical redundancy approach is based on a completely different principle. The basic idea behind analytical redundancy is to have accurate model of the real process behavior. When the fault occurs, the residual signal, difference between actual value and modeled value of a signal being measured, is generated. The residuals can be used to diagnose and isolate the malfunction. The main advantage of this approach with respect to having physical redundancy is that it is more economical. The disadvantage is that it needs high-ﬁdelity process model of the real system to capture faults. This chapter focuses on technique used for model-based system diagnostics in automotive combustion engine.

Keywords Model-based fault diagnosis Modeling of diagnostic system Diesel engine diagnostics Fault detection Diesel engine airpath modeling

D. Agarwal (&) International Center for Automotive Technology, Gurugram, Manesar, India e-mail: [email protected] D. Agarwal EbyT Technology Private Limited, New Delhi, Delhi, India C. K. Singh AVL, Gothenburg, Sweden e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2019 A. K. Agarwal et al. (eds.), Advanced Engine Diagnostics, Energy, Environment, and Sustainability, https://doi.org/10.1007/978-981-13-3275-3_9

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9.1

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Introduction

A fault can generally be deﬁned as an unexpected deviation of at least one characteristic property, called the feature of the system, from the normal condition which tends to degrade the overall performance of a system and leads to undesirable but still tolerable behavior of the system (Gertler 1998). Modern engineering systems have strict performance and reliability requirements. These requirements dictate that systems should be running under different operating conditions all the time. Operation under different operating conditions has increased the possibility of faults leading to system breakdown, higher operating costs, and even catastrophic failure leading to damage of property and human life (Chen 2007; Frank 1996). Therefore, to ensure that modern engineering systems satisfy the demands for reliability, availability, safety, and maintainability of an industrial process, it is important to catch the faults or deviating process parameters before they can cause system failure. A traditional approach for fault diagnosis consists of having a redundant parallel sensors and computers that monitors the process parameters (Fig. 9.1). These sensors are in addition to the system sensors that are used to control the system. Such an approach is more focused on building hardware redundancy in the system to catch faults. This method is costly and sometimes may cause complex problems when incorporated with other redundant devices (Chen and Patton 1999). Contrary to hardware approach, there is analytical approach where the analytical models of system are built to monitor and track changes in process parameters. Analytical approach, also known as model-based fault diagnosis, focuses on building high-ﬁdelity plant models to track changes in plant dynamics, and if predicted plant output fails to match with actual plant output within a prescribed threshold, then error is flashed. In model-based fault diagnosis, the main idea is to generate directional residuals by failure detection ﬁlters. Different fault effects can be mapped into different directions or planes in the residual vector space so that fault isolation can be achieved (Frank 1996). The existing analytical redundancy fault diagnosis approaches can be broadly divided into • knowledge-based FDI methods • signal-based FDI methods • model-based FDI methods: Knowledge-Based FDI Methods—These methods of fault diagnosis are essentially being developed from the heuristic symptoms. These are obtained either from expert human operators or from a qualitative model. In some of the knowledge-based methods, the model is built up by expert reasoning (Allen and Cagavan 1990), fuzzy reasoning (Patton et al. 2000), and neural networks (Liu et al. 2000; Lu et al. 2001), for mapping the inputs and outputs of the unknown system. Signal-Based FDI Methods—These methods have been developed and can be divided into two categories: spectral analysis (Garcia-Perez et al. 2011) (time-frequency, timescale analysis, etc.) and statistical methods (Carrillo and Kinnaert 2010) (signal classiﬁcation, pattern recognition, etc.). These methods

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Fig. 9.1 Classiﬁcation of fault diagnosis methods

extract from the system signal properties such as spectral power densities, correlation coefﬁcients, and covariances, for the analysis of faults. Although the signal-based techniques do not require a complete analytical model, their efﬁciency is particularly limited for early fault detection and for the detection of faults which occur during transient operation. Model-Based FDI Methods—With the development of digital computers and the system identiﬁcation techniques, model-based FDI methods have received considerable attention in recent years (Chen and Patton 1999; Simani et al. 2003; Floquet et al. 2004; Shields 2005). Model-based fault diagnosis comprised of two main steps: residual generation and residual evaluation. In residual generation, system predicted output is compared with the system actual output. In case of no fault, difference between predicted output and actual output should be zero, while in case when the fault is present, this difference should be nonzero. In the second step, the generated residuals are used to evaluate whether fault has occurred. This is done by comparing residuals with the threshold. An error is triggered when the computed residual exceeds the predeﬁned threshold value. The three most popular model-based FDI methods are described below: (1) Parity equation approach (Gertler 1998; Ding et al. 1999). This approach is based on the test of consistency of parity equations by using measurements and inputs. Any inconsistency of the parity equations can be used to detect the faults. (2) Parameter estimation approach (Liu et al. 2000; Balle and Isermann 1998; Jiang et al. 2008). This approach is based on the assumptions that the occurrence of any fault will change the values of the physical system parameters such as mass, friction, resistance. Parameters of actual process will be repeatedly

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estimated using online parameter estimation techniques. A fault is triggered if there are discrepancies between the true values of process parameters and their estimated values. (3) Observer-based approach (Hammouri et al. 1999; Jiang and Chowdhury 2004; Commault et al. 2002; Jiang et al. 2011; Zhang et al. 2012). The observer-based FDI approaches generally compare the actual system’s measurements with the predicted or estimated outputs by employing observers. Observers calculate the system output, and then, the calculated output is compared with the actual output for residual generation. The most commonly used observers include Luenberger observers (Duan and Patton 2001) in a deterministic setting, Kalman ﬁlters (Brumback 1987) in a stochastic setting, sliding-mode observers, and unknown input observers (UIO) (Chen et al. 1996). These methods will be covered in this chapter.

9.2

Open-Loop Modeling of Diagnostic System

Model-based diagnostics is based on the concept of analytical redundancy (Castillo and Edgar 2008; Venkatasubramanian et al. 2003). The idea behind this concept is the comparison between the actual output of the system being monitored and the analytical mathematical model output of the system. The difference between the system out output and analytical model output is called residual. In case of fault-free system, the residual will be zero or close to zero. However, the residual value is nonzero in the case when the faults are present in the system. The output comparison involves two-stage residual generation and residual evaluation (Miljkovic 2011).

9.2.1

Nominal System Behavior of LTI Systems

Depending on the process dynamics and modeling aims, different system model types can be used for process description; among them, the linear time-invariant (LTI) system is the simplest and mostly used in this monograph. In this monograph, we call disturbance-free and fault-free systems nominal and assume that the nominal systems are LTI. This is fair assumption due to fact that all nonlinear systems can be linearized about an operating point. Based on above assumption, the theoretical analysis for a linear time-invariant system will be presented in this chapter. LTI system can be represented in two mathematical representations: transfer matrix and the state-space description. Transfer matrix is the input–output description of the system in frequency domain, while the state-space representation

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Fig. 9.2 State-space representation of a plant in open loop

is input–output description in time domain. In this monograph, we will explicitly use state-space representation of the system, wherever it is possible, because the state-space analysis can be extended for both linear and nonlinear systems. State space has an advantage over other analysis because matrices theory, used in state-space calculations, is well developed and can be applied to either single input, single output (SISO) or multiple input, multiple output (MIMO) with equal ease. In state-space representation, continuous-time LTI system is given by x_ ðtÞ ¼ AxðtÞ þ Bua ðtÞ

x ¼ x0

yp ðtÞ ¼ CxðtÞ þ Dua ðtÞ

ð9:1Þ ð9:2Þ

where x 2 Rn is called the state vector, x0 is the initial condition of the system, ua 2 Rp is the plant input vector (actuator output vector) and yp 2 Rm is the plant output vector. Matrices A, B, C, D are appropriately dimensioned real constant matrices. In pictorial form, the state-space representation of the system looks like the one shown in Fig. 9.2.

9.2.2

Modeling of Faulty System

The ﬁrst step in model-based fault diagnostic system consists of providing mathematical description of the system under diagnosis, showing all the possible fault cases. Based on the location of faults, system under diagnosis can be separated into the following different parts: • Actuators • Process or system components • Sensors

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where fa ðtÞ is the actuator fault vector deﬁned as fa ðtÞ ¼ ½fa1 ðtÞ fa2 ðtÞ. . . far ðtÞT. Individual entries in vector corresponds to different possible fault situations in a system actuator. Similarly, fp ðtÞ and fu ðtÞ refer to plant faults and sensor faults and deﬁned by fp ðtÞ ¼ ½fp1 ðtÞ fp2 ðtÞ. . .fps ðtÞT and fs ðtÞ ¼ ½fs1 ðtÞ fs2 ðtÞ. . .fst ðtÞT, respectively. Each entry in fault vector corresponds to some fault situation that should be captured through diagnostic system. uðtÞ; ua ðtÞ; yp ðtÞ; yðtÞ are input command, actuator command to the plant, real plant output, and plant output measured by sensor. wðtÞ and vðtÞ are process disturbance and sensor noise, respectively. Sensor noise is characterized by white, zero mean, uncorrelated Gaussian processes. The fault vector fa ðtÞ is an arbitrary and unknown signal which can represent several different fault behaviors of the actuators. Choosing fa ðtÞ to be equal to the sth column of the B matrix gives, for example, the possibility to model a fault in the sth actuator. As fa ðtÞ is arbitrary, it could model a complete loss of the actuator ðfas ðtÞ ¼ ua ðtÞÞ or just an offset (fas ðtÞ = constant) (Chen and Patton 1999). As can be observed from Fig. 9.3, faults may occur in any of the three components of system. Furthermore, the plant dynamics and the sensor measurements are always affected by external system disturbances (or process noises) and measurement noises, respectively. A reliable fault diagnosis system should be able to distinguish faults from system disturbances and measurement noise. The process model with fault can be described by the following equation: x_ ðtÞ ¼ AxðtÞ þ Bua ðtÞ þ Ep fp ðtÞ

ð9:3Þ

yp ðtÞ ¼ CxðtÞ þ Dua ðtÞ þ Fp fp ðtÞ

ð9:4Þ

In a general case, where multistate system is considered, Ep and Fp are known matrices that indicate the place where the fault occurs in the process and its influence on other system components. By neglecting sensor and actuator dynamics, faults on actuator and output sensors are modeled as: ua ðtÞ ¼ uðtÞ þ fa ðtÞ

ð9:5Þ

yðtÞ ¼ yp ðtÞ þ fs ðtÞ

ð9:6Þ

Fig. 9.3 Possible fault location in an open-loop system

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Again, taking the more general case of MIMO system, Ep and Fp represent the fault signature of the actuator and the sensor, respectively. Fault signature describes the way fault affects the system. When the actuator faults coexist with process and sensor faults, the above process model becomes: x_ ðtÞ ¼ AxðtÞ þ BðuðtÞ þ fa ðtÞÞ þ Ep fp ðtÞ

ð9:7Þ

yðtÞ ¼ CxðtÞ þ DðuðtÞ þ fa ðtÞÞ þ Fs fs ðtÞ þ Fp fp ðtÞ

ð9:8Þ

Equations (9.7) and (9.8) are arrived at by substituting Eq. (9.5) in Eqs. (9.3) and (9.4). Next section explains different types of process, actuators, and sensors faults and how they impact the input and output signals.

9.3

Closed-Loop Modeling of Diagnostic System

Model-based fault diagnosis systems are often embedded in closed-loop feedback control systems (Fig. 9.4). Main advantage of closed-loop system is that they are robust to changes in the system. However, the detection and isolation of faults in closed-loop system are more difﬁcult as compared to open-loop system. That is why special attention must be paid to the topic of fault detection in feedback control loops. The reason why we need a separate analysis for closed-loop system is because in the open-loop framework, it is assumed that input and output vectors uðtÞ and yðtÞ are available. However, in practice, it is often the case that uðtÞ is not available in feedback system. For instance, if the control loop is a part of a large-scale system and located remotely from the supervision station, where the higher-level controller and FDI unit are located, the reference signal r ðtÞ instead of process input signal uðtÞ is usually available for the FDI purpose. In those cases, the so-called closed-loop FDI strategy can be applied.

Fig. 9.4 Possible fault location in closed-loop system

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The state-space representation of a nominal plant considering there are no dynamics which are associated with actuator or sensor is shown in Fig. 9.5. As the actuator and sensor dynamics are non-existent, ua ðtÞ will become uðtÞ and yp ðtÞ will become yðtÞ. [K P] are the controller and pre-controller matrices. r ðtÞ and vðtÞ are the pre-controller and controller output, respectively. x_ ðtÞ ¼ AxðtÞ þ BuðtÞ

ð9:9Þ

yðtÞ ¼ CxðtÞ þ DuðtÞ

ð9:10Þ

uðtÞ ¼ PrðtÞ KvðtÞ

ð9:11Þ

Closed-loop fault diagnosis strategy has an additional advantage since advanced control strategy tools can be used to model the dynamics of closed-loop system that may be well modeled in a form of easy modeling. For instance, using a decoupling controller will result in a diagonal closed-loop system matrix, which may reduce a multiple-input, multiple-output (MIMO) system into a number of (decoupled) single-input, single-output (SISO) ones. For the sake of simplifying the problem formulation, we only consider additive faults. The overall system model with sensor, actuator, and process faults is then given by: x_ ðtÞ ¼ AxðtÞ þ BðuðtÞ þ fa ðtÞÞ þ Ep fp ðtÞ

ð9:12Þ

yðtÞ ¼ CxðtÞ þ DðuðtÞ þ fa ðtÞÞ þ Fs fs ðtÞ þ Fp fp ðtÞ

ð9:13Þ

uðtÞ ¼ PrðtÞ KvðtÞ

ð9:14Þ

Fig. 9.5 State-space representation of a plant in closed loop

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Equations (9.12) and (9.13) are the same as (9.7) and (9.8). Equation (9.14) is added to deﬁne the closed-loop control law.

9.4

Fault Classiﬁcation

Faults can be classiﬁed based on several categories. The fault categories along with descriptions are explained below.

9.4.1

Fault Classiﬁcation Based on Time Dependency

The occurrence and growth of faults with respect to time are dependent on time. There are three categories of faults. Figure 9.6 shows different time-dependent faults: a. Abrupt fault is sudden and permanent occurrence of fault in the system or its components. It is modeled as a step function. Abrupt fault sometimes exhibits itself as bias in the monitored signal. b. Incipient fault represents slow growth of the fault in the system or its components. It can be modeled as ramp function. Incipient fault may show itself as time-dependent deviation of the monitored signal from real measurement. c. Intermittent faults are the faults that occur randomly in the system. It may be a combination of impulses with different amplitudes.

Fig. 9.6 Time-based fault classiﬁcation

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Fault Classiﬁcation Based on System Interaction

This type of classiﬁcation segregates faults based on how they influence the process or process output. Figure 9.7 shows the faults based on this classiﬁcation: a. Additive process faults are usually disturbances that act on the system, causing the shift in process output. Faults such as air manifold leaks or engine speed variation fluctuation due to engine load changes can be classiﬁed as additive process faults. For robust fault detection and isolation, additive disturbances need to detected and isolated. Additive process faults in a case where no actuator and sensor faults are present can be described by the following equation: x_ ðtÞ ¼ AxðtÞ þ BuðtÞ þ Ep fp ðtÞ

ð9:15Þ

where Ep is the additive fault matrix. Additive process faults are often caused by parametric uncertainties caused due to linear approximation of the nonlinear system model in a certain operating point. Therefore, the validity of approximations by linearized models is often restricted to small variations around some nominal operating points and parameter values. b. Multiplicative process faults are caused due to abrupt changes in system parameters that also depend on magnitude of known input. In state-space representation of multiplicative process, faults in case where both actuator and sensor faults are absent can be described by the following equation: x_ ðtÞ ¼ ðA þ DAF ÞxðtÞ þ ðB þ DBF ÞuðtÞ

ð9:16Þ

yðtÞ ¼ ðC þ DCF ÞxðtÞ þ ðD þ DDF ÞuðtÞ

ð9:17Þ

DAF ; DBF ; DCF ; DDF are parametric uncertainties in the system matrix which are modeled as faults. Multiplicative faults are characterized by their direct influence on the system stability. The fact that DAF is added to the system matrix A has a direct influence on system stability. c. Additive measurement faults influence the system output/variable by an offset. This offset can be expressed by addition. Additive faults can be due to noise or disturbance in the system. A common additive fault is offset in sensor

Fig. 9.7 Fault classiﬁcation based on system interaction

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measurements or drift in sensors. The former is described by a constant while the latter by a ramp. Additive measurement faults in a case where no actuator faults are present are described by the following equation: yðtÞ ¼ CxðtÞ þ DuðtÞ þ Fs fs ðtÞ

9.4.3

ð9:18Þ

Fault Classiﬁcation Based on Component Failure

Faults can also happen in the system components such as sensors, actuators, or process itself. This way of classiﬁcation segregates the components that are causing the faults. a. Sensor faults: Sensors are interfaces between the system and the external world. Sensors are a medium to convey information about system behavior about its internal state. A fault in sensor causes a substantial system performance degradation. Sensor faults are usually dependent on sensor types. However, common faults are identiﬁed as follows (Fig. 9.8): • Bias: The sensor measurements sometimes show biased value from true value. yðtÞ ¼ yp ðtÞ þ dðtÞ

ð9:19Þ

where d is deﬁned as the bias constant whose value may or may not change with time. • Drift: If the sensor is showing increasing values of the measured state with time, this behavior is termed as drift. In case of sensor drift the sensor, output will be given by the following equation. yðtÞ ¼ ð1 þ dðtÞÞyp ðtÞ

ð9:20Þ

where d is deﬁned as the drift constant whose value may or may not change with time. • Degraded efﬁciency: In this case, sensor will be showing false values, but the changing trends will be same as the system behavior. • Sensor freeze: In this case, the measurements taken from installed sensors will contain either ‘0’ in case of fully damaged or any other value which will remain constant through the system process. yðtÞ ¼ dðtf Þ

ð9:21Þ

where d is deﬁned as the freeze value whose value will not change with time. tf refers to the time when the fault occurs.

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• Calibration error: Sensor zero output coincides with the real physical zero of the variables being measured, while the maximum value of the sensor is shifted from the real physical value of the variable. b. Actuator faults: Actuator faults are caused due to change in control input to the system. In electromechanical system, actuators are needed to transform control signals to actuation signals such as torque and forces to drive the system. The consequence of faults can cause higher energy consumption or total loss of control. Common actuator faults can be classiﬁed as follows (Fig. 9.9): • Freezing: Actuator freezes at a value and does not respond to any subsequent commands. Freezing is described by the following equation: ua ðtÞ ¼ b tf

ð9:22Þ

b refers to freeze value of the actuator at time tf when the fault occurs. • Unstable: The actuator command oscillates around zero mean value. In this case, the actuator does not contribute to the control authority. Unstable behavior can be represented by the following equation: ua ð t Þ ¼ 0

ð9:23Þ

• Hard over: The actuator command oscillates between the actuator minimum and maximum limits thus causing instability in the system. • Degraded efﬁciency: This actuator fault is characterized by lowering the actuator gain with respect to nominal value. Degraded efﬁciency or loss of accuracy can be represented by the following equation: ua ðtÞ ¼ k ðtÞuðtÞ

ð9:24Þ

where kðtÞ 2 ½; 1 denotes the actuator effectiveness coefﬁcient and is the minimum actuator effectiveness coefﬁcient.

Fig. 9.8 Common sensor faults or failure

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Fig. 9.9 Common actuator faults or failures

9.5

Desired Features of Fault Diagnosis System

• Easy fault detection and diagnosis—This refers to the capability of the system to detect and diagnose the faults. Early detection and isolation of the faults prior to its full manifestation are important for the system to prevent catastrophic failure. Early detection of fault will also help in building conﬁdence in the system. While sensitive to the faults, the diagnostic system should be robust enough to reject any false alarms under normal operating conditions of the system. • Identiﬁability—Fault identiﬁability refers to the ease of ﬁnding the severity, type, and nature of fault. It is one of the important criteria for fault prognosis. Accurate fault identiﬁcation is usually very difﬁcult to achieve due to the presence of measurement noise, system disturbances, modeling uncertainties, and coupling/interactions between potential fault sources in the monitored system. • Fault isolation—Fault isolation refers to ability of a diagnostic system to distinguish one fault source with another fault source. Without fault source identiﬁcation, proper countermeasures cannot be taken, thus affecting the system behavior during fault occurrence. Fault isolation not only depends on diagnostic

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system design but also depends on how the faults affect the system. Various sources of uncertainties such as modeling uncertainty/errors and system disturbances pose a serious challenge to achieve a high degree of fault isolation capability. In other words, a diagnostic system with a high degree of fault isolation capability may be too sensitive to these uncertainties. • Robustness—Robustness refers to diagnostic system capability to detect faults under uncertainties such as modeling errors, system disturbance, noise. Therefore, robustness of diagnostic system to above uncertainties is one of the most desired attributes. Robustness essentially augments diagnostic system reliability and effectiveness. • Multiple fault identiﬁability—This refers to diagnostic system capability to detect and diagnose multiple faults at the same time. In many practical situations, multiple faults can coexist, so if the diagnostic system can identify multiple faults simultaneously corrective action can be taken to prevent system failure. This is a rather difﬁcult requirement mainly due to nonlinearities and coupling/interactions that generally exist between the states and the potential fault sources of a dynamical system. • Computing requirement—Nowadays, most of the diagnostic system algorithms are designed in software and implemented on microcontrollers. Microcontrollers for embedded system have limited memory and other computational resources. Therefore, while designing a fault diagnosis system, it is necessary to keep in mind that the computational and memory requirements must always meet the speciﬁcations of the application. Moreover, the application should adhere to power consumption speciﬁcations of the microcontroller.

9.6

Techniques for Residual Generation

Normally, the consistency check based on analytical redundancy is achieved by comparing measured signals with their estimates. The resulting difference for one signal is referred to as residual signal; e.g., ri ¼ yi ^yi ; i k where ri denotes the ith residual, yi the ith measured system output, ^yi the estimated ith system output, and k the number of residuals. Residuals are designed to be equal or converge to zero in the fault-free case ðri 0Þ and deviate signiﬁcantly from zero under occurrence of a fault (jri j [ li [ 0 where li 2 R denotes a threshold). Hence, the residuals represent the fault effects. Depending on the number of residuals and their design, it is possible to detect and isolate occurring faults. Most model-based FDI methods incorporate two sequential steps to obtain FDI: 1—residual generation and 2— residual evaluation. The residual generation for model-based FDI is based on exploiting the available analytical redundancy. In most approaches, the analytical redundancy is represented

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by a set of differential equations. The goal is to generate structured residuals to obtained sufﬁcient FDI capability. One common way to generate residuals is to estimate the system output vector y or the system parameter vector £. Then, the ^ are subtracted from the real measurement y and the nominal estimates ^y and £ value of the parameters £norm . This leads to the following residual vectors: r ¼ y ^y and r£ ¼ £norm £

ð9:25Þ

The residual vector r£ corresponds to the parameter estimation approach. The residual vector r is typical for the observer-based approach but is also used by the so-called parity relation approach. A variety of methods are available in the literature for residual generation. This monogram only focuses on observer-based approaches.

9.6.1

State Observer-Based Approach

The basic idea of the observer-based FDI consists in estimating the outputs of the system from the measurement using an observer and then constructing residuals by properly weighted output’s estimation errors. One speciﬁc diagnostic signal must be generated per each fault to be detected, each diagnostic signal being sensitive only to one fault. It is worth noting that when an observer is exploited for FDI purpose, the estimation of the outputs is necessary, while the estimation of the state vector is usually not needed. Moreover, the advantage of using the observer is the flexibility in the selection of its gains which leads to a rich variety of FDI schemes (Frank 1994; Frank and Ding 1997; Chen et al. 1997). To obtain generalized observer, continuous-time and linear time-invariant, linear dynamic model of the plant is considered. This is a fair assumption because any nonlinear plant can be converted into linear plant model by linearizing the nonlinear plant at ﬁxed operating point. The schematic of linear state observer is shown in Fig. 9.10. The state-space form of linear plant model is given by the following equation: x_ ðtÞ ¼ AxðtÞ þ BuðtÞ

ð9:26Þ

yðtÞ ¼ CxðtÞ þ DuðtÞ

ð9:27Þ

A; B; C; D are estimated plant state-space matrices for the real plant. xðtÞ and yðtÞ are plant states and plant output, respectively. An observer to reconstruct the system variables based on measured inputs and outputs is given by:

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Fig. 9.10 Process and state observer structure

^x_ ðtÞ ¼ A^xðtÞ þ BuðtÞ þ H ðyðtÞ ^yðtÞÞ

ð9:28Þ

^yðtÞ ¼ C^xðtÞ þ DuðtÞ

ð9:29Þ

where ^xðtÞ and ^yðtÞ are model states and model output. Subtracting Eq. (9.29) from (9.27) to compute the output error signal ye ðtÞ, the equation becomes: ye ðtÞ ¼ CxðtÞ C^xðtÞ

ð9:30Þ

State estimation error xe ðtÞ is given by: xe ðtÞ ¼ xðtÞ ^xðtÞ

ð9:31Þ

Dynamics of state estimation error, x_ e ðtÞ, is given by: x_ e ðtÞ ¼ ðA HCÞxe ðtÞ

ð9:32Þ

The state error vanishes asymptotically if the following condition is true: lim xe ðtÞ ¼ 0

t!1

ð9:33Þ

The above condition is achieved by the careful design of observer feedback H. If the process, sensor, and actuator faults are present, the process model will be as per Eq. (9.7)

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183

x_ ðtÞ ¼ AxðtÞ þ BðuðtÞ þ fa ðtÞÞ þ Ep fp ðtÞ Combining non-essential terms together, above state equation can be re-written as: x_ ðtÞ ¼ AxðtÞ þ BuðtÞ þ L1 f ðtÞ

ð9:34Þ

where f(t) is the fault signal and L1 is the fault matrix. Similarly, output equation as per Eq. (9.8) is given below: yðtÞ ¼ CxðtÞ þ DðuðtÞ þ fa ðtÞÞ þ Fs fs ðtÞ þ Fp fp ðtÞ The above equation can also be simpliﬁed to combine all non-essential terms. The above equation can be re-written as: yðtÞ ¼ CxðtÞ þ DuðtÞ þ L2 f ðtÞ

ð9:35Þ

The error dynamics when the fault is present can be obtained by subtracting Eq. (9.28) from Eq. (9.34). x_ ðtÞ ^x_ ðtÞ ¼ A½xðtÞ ^xðtÞ þ L1 f ðtÞ H ½yðtÞ ^yðtÞ

ð9:36Þ

Substituting yðtÞ and ^yðtÞ from Eqs. (9.29) and (9.35), state error dynamics can be written as: x_ e ðtÞ ¼ Axe ðtÞ þ L1 f ðtÞ H ½CxðtÞ þ L2 f ðtÞ C^xðtÞ

ð9:37Þ

x_ e ðtÞ ¼ ðA HCÞxe ðtÞ þ ðL1 HL2 Þf ðtÞ

ð9:38Þ

The output error ye ðtÞ can be obtained by subtracting Eq. (9.29) from (9.35). yðtÞ ^yðtÞ ¼ C½xðtÞ ^xðtÞ þ L2 f ðtÞ

ð9:39Þ

ye ðtÞ ¼ Cxe ðtÞ þ L2 f ðtÞ

ð9:40Þ

In the case of sudden and permanent faults f(t), the state estimation error will deviate from zero. The feedback H of the state observer in Eq. (9.38) is chosen so that fault signals L1 f ðtÞ change in a deﬁnite direction and fault signals L2 f ðtÞ in a deﬁnite plane (Speyer 1999).

9.6.2

Output Observer-Based Approach

In output observer-based approaches, output signal is reconstructed instead of state variables. The basic idea behind the observer is to estimate the outputs of the

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Fig. 9.11 Process and output observer structure

system from measurements (Patton and Chen 1997). The schematic of output observer is shown in Fig. 9.11. Through the linear transformation, new state vector is created. The new state vector is given by equation below: ^zðtÞ ¼ TxðtÞ

ð9:41Þ

where T is the transformation matrix that transforms vector x(t) to vector ^zðtÞ. In terms of new estimated state vector ^zðtÞ, the state equation is given by the following equation: ^z_ ðtÞ ¼ F^zðtÞ þ JuðtÞ þ GyðtÞ

ð9:42Þ

The output of the observer is deﬁned by the following equation: yz ðtÞ ¼ W z^zðtÞ þ PuðtÞ

ð9:43Þ

The state estimation error is given by the following relation: xe ðtÞ ¼ ^zðtÞ TxðtÞ

ð9:44Þ

In fault-free case, error should be zero. Differentiating above equation and substituting ^z_ ðtÞ from equation from (9.42) and x_ ðtÞ from equation from (9.26), the following equations are obtained: x_ e ðtÞ ¼ F^zðtÞ þ JuðtÞ þ GyðtÞ T ½AxðtÞ þ BuðtÞ Substituting ^zðtÞ ¼ TxðtÞ in above equation and rearranging terms

ð9:45Þ

9 Model-Based Fault Detection on Modern Automotive Engines

x_ e ðtÞ ¼ ½FT TAxðtÞ þ ½J TBuðtÞ þ GyðtÞ

185

ð9:46Þ

Substituting y(t) from Eq. (9.27) x_ e ðtÞ ¼ ½FT TAxðtÞ þ ½J TBuðtÞ þ G½CxðtÞ þ DuðtÞ

ð9:47Þ

Rearranging common terms from above equation x_ e ðtÞ ¼ ½FT TA þ GCxðtÞ þ ½J TB þ GDuðtÞ

ð9:48Þ

In fault-free case, error dynamics will be zero. The above equation can be zero if the following conditions are true: FT TA þ GC ¼ 0

ð9:49Þ

J TB þ GD ¼ 0

ð9:50Þ

Similarly, in fault-free case, residuals will be zero. ye ðtÞ ¼ W z^zðtÞ þ W y yðtÞ þ PuðtÞ

ð9:51Þ

Substituting ^zðtÞ from Eq. (9.41) and y(t) from Eq. (9.27) ye ðtÞ ¼ W z TxðtÞ þ W y ½CxðtÞ þ DuðtÞ þ PuðtÞ

ð9:52Þ

ye ðtÞ ¼ W z T þ W y C xðtÞ þ W y D þ P uðtÞ

ð9:53Þ

W zT þ WyC ¼ 0

ð9:54Þ

W yD þ P ¼ 0

ð9:55Þ

In case when process, actuator, and sensor faults are present, the state equation is obtained by differentiating Eq. (9.44) and substituting x_ ðtÞ from Eq. (9.34). x_ e ðtÞ ¼ F^zðtÞ þ JuðtÞ þ GyðtÞ T ½AxðtÞ þ BuðtÞ þ L1 f ðtÞ

ð9:56Þ

Substituting yðtÞ for Eq. (9.35), J with Eq. (9.50) and rearranging common terms, equation becomes: x_ e ðtÞ ¼ F^zðtÞ þ GCxðtÞ þ GL2 f ðtÞ TAxðtÞ TL1 f ðtÞ

ð9:57Þ

Substituting value of GC from Eq. (9.49), the ﬁnal equation becomes: x_ e ðtÞ ¼ Fxe ðtÞ þ GL2 f ðtÞ TL1 f ðtÞ

ð9:58Þ

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It can be seen from above equation that state error solely and totally depends on faults. Similarly, when all the faults coexist, the output error is given by: ye ðtÞ ¼ W z^zðtÞ þ W y yðtÞ þ PuðtÞ Substituting y(t) in above equation with Eq. (9.35) yields ye ðtÞ ¼ W z TxðtÞ þ W y ½CxðtÞ þ DuðtÞ þ L2 f ðtÞ þ PuðtÞ ye ðtÞ ¼ W z T þ W y C xðtÞ þ W y D þ P uðtÞ þ W y L2 f ðtÞ

ð9:59Þ

Using Eqs. (9.54) and (9.55), replacing them in Eq. (9.59), and canceling terms yields: ye ð t Þ ¼ W y L 2 f ð t Þ

ð9:60Þ

Thus, the output error only depends on the fault, and fault is manifested in the observer residual ye ðtÞ. To uniquely isolate a fault concerning one of the system outputs, fs(t), under the hypothesis that inputs are fault-free, ðfa ðtÞ ¼ 0Þ, a bank of classical dynamic observers is used, according to Fig. 9.12. The number of such observers is equal to number of outputs from the system. Each observer is responsible to regenerate one system output and is driven by all inputs to the system (Simani et al. 2013). Such type of dedicated observers is suited to capture sensor faults with each observer responsible to look after faults from one sensor. In this case, a fault on the ith output affects only the residual function of the output observer or ﬁlter driven by the ith output.

Fig. 9.12 Bank of output observers that are used to capture sensor or process faults

9 Model-Based Fault Detection on Modern Automotive Engines

9.6.3

187

Unknown Input Observer-Based Approach

To uniquely isolate a fault concerning one of the system inputs, under the assumption that outputs are fault-free, unknown input observers are used. This class of observers is designed so that they are insensitive to some certain disturbances which we know how they affect the system, but we do not know the disturbances themselves. Unknown input observers are best suitable for generation of robust structured residuals to detect and isolate faults. Figure 9.13 shows process and unknown input observer structure. Consider a linear time-invariant system described by: x_ ðtÞ ¼ AxðtÞ þ BuðtÞ þ DvðtÞ

ð9:61Þ

yðtÞ ¼ CxðtÞ

ð9:62Þ

where x Rn, u Rk, v Rm, and y Rp are the state vector, the known input vector, the unknown input vector, and the output vector of the system, respectively. A, B, C, and D are known constant matrices of appropriate dimensions. We assume that p m and, without loss of generality, rank D = m and rank C = p. The full-order UIO has the following mathematical form (Boubaker 2005): ^z_ ðtÞ ¼ F^zðtÞ þ JuðtÞ þ GyðtÞ

ð9:63Þ

^xðtÞ ¼ ^zðtÞ HyðtÞ

ð9:64Þ

where ^zðtÞ Rn is the state of the UIO, ^xðtÞ the estimated state vector of real states x (t), while F, J, H, and G are matrices to be designed such that ^xðtÞ will

Fig. 9.13 Process and unknown input observer structure

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asymptotically converge to x(t). To guarantee the convergence of the estimate state vector ^xðtÞ to state vector x(t), let us deﬁne the observer reconstruction error as follows: xe ¼ ^xðtÞ xðtÞ

ð9:65Þ

Dynamics of the observer error is given by: x_ e ¼ Fxe þ ðFP þ GC PAÞxðtÞ þ ðJ PBÞuðtÞ PDvðtÞ

ð9:66Þ

where P is deﬁned as: P ¼ I þ HC

ð9:67Þ

Therefore, convergence conditions of ^xðtÞ to xðtÞ are: PD ¼ 0

or

ðI þ HC ÞD ¼ 0

ð9:68Þ

FP þ GC PA ¼ 0

ð9:69Þ

J PB ¼ 0

ð9:70Þ

Equation (9.66) reduces to the following homogenous equation: x_ e ¼ Fxe

ð9:71Þ

The conditions for ^xðtÞ to be an asymptotic state observer of xðtÞ are (9.67)– (9.70), and F must be a stability matrix, i.e., has all its eigenvalues in the left-hand side of the complex plane. To use the well-known results obtained for the classical full-order observer without unknown inputs (9.72), Eq. (9.69) can be written as F ¼ PA KC

ð9:72Þ

K ¼ G þ FH

ð9:73Þ

where

Substituting (9.72) into (9.73), the following equation is obtained: G ¼ K ðI þ CH Þ PAH

ð9:74Þ

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189

Fig. 9.14 Bank of input observers that are used to capture actuator faults

Then, the observer dynamical Eq. (9.63) becomes ^z_ ðtÞ ¼ ðPA KCÞ^zðtÞ þ JuðtÞ þ GyðtÞ

ð9:75Þ

where matrices H, P, J, and G are obtained from (9.68), (9.67), (9.70), and (9.74), respectively. Therefore, the problem of designing the full-order observer with unknown inputs is reduced to ﬁnd a matrix H satisfying (9.68), and a matrix K such that (PA − KC) is a stability matrix. This problem is equivalent to the standard problem of the state observer design when all inputs are known. The eigenvalues of (PA − KC) can be arbitrarily located, by choosing matrix K suitably, if and only if the pair (PA, C) is observable. To uniquely isolate a fault concerning one of the system inputs, fs(t), under the assumption that outputs are fault-free (fa(t) = 0), a bank of UIO is used as shown in Fig. 9.14 (Simani et al. 2013). Such a solution is known as the generalized observer scheme (GOS). The number of these observers is equal to the number k of control inputs. The ith observer is driven by all the inputs except the ith input and all outputs of the system. The ith observer generates a residual function which is sensitive to all the input faults except the ith input fault. This way single input faults can be captured because ith input fault will affect all the observers except the ith observers, which is incentive to ith input.

9.7

Residual Evaluation and Threshold Computation

After generating residuals, the next step is to evaluate the residual to check if any faults are present. A widely accepted way is to generate such a feature of the residual signal, by which faults can be distinguished from the disturbances and

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uncertainties. Residual evaluation and threshold setting serve this purpose. Depending on the type of system under consideration, there are different residual evaluation strategies: • Statistical-based residual evaluation methods—These strategies try to evaluate residual in statistical context. These methods try to maximize the probability of ﬁnding the faults, by reducing the occurrence of false alarms. • Norm-based residual evaluation methods—In these methods, residuals or some function of residual is compared with ﬁxed threshold or threshold function. In this chapter, we shall focus on the norm-based residual evaluation and the associated threshold computation. Because of the residual dynamics, a simple geometrical analysis, such as a ﬁxed threshold logic, can be exploited to detect actuator faults. Clearly, suitable threshold values must be set under fault-free conditions. Therefore, the inequalities given in Eq. (9.76) are obtained, when the symptom evaluation in the noise-free case is performed by comparing residual signals r(t) with the ﬁxed threshold : r ðt Þ

for

f ðt Þ ¼ 0

r ðtÞ [ for

f ðtÞ 6¼ 0

ð9:76Þ

Fixed thresholding works well when the process operates in steady-state environment. When the fault happens the process consistently deviate by the approximately same threshold every time the fault happens. On the other hand, when the process can be run in different operating conditions, adaptive thresholds ðtÞ can be tried. In some cases, the residual is evaluated by a residual function. In such cases, fault evaluation is performed by the following equations: J ðr ðtÞÞ ðtÞ

for

f ðt Þ ¼ 0

J ðr ðtÞÞ [ ðtÞ for

f ðtÞ 6¼ 0

ð9:77Þ

where J ðr ðtÞÞ is some function of residual r ðtÞ. Some of the standard evaluation functions used in residual evaluation are given below. Peak value: The peak value of residual signal r is deﬁned for continuous-time residual signal r(t) as: Jpeak ¼ jjr jjpeak :¼ maxjjr ðtÞjjpeak ; jjr jjpeak ¼ t0

kr X

!1=2 ri2

ð9:78Þ

i¼1

For calculating the threshold peak, value of residual r can be calculated as follows:

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191

Jth;peak ¼ max jjr ðtÞjjpeak

ð9:79Þ

faul free

Using the above relation, residual evaluation strategy can be formulated as: Jpeak Jth;peak for no fault Jpeak [ Jth;peak for fault

ð9:80Þ

where jjr jjpeak is the max value of summation of residual vector r(t), r 2 Rkr . Average value: In this case, residual function is deﬁned by the average value of residual. Javerage ¼ jjr jjaverage ¼ maxjjr ðtÞjjpeak ; r ðtÞ ¼ t0

1 t þZ T r ðsÞds T t

ð9:81Þ

Residual threshold value can be calculated as follows: Jth;average ¼ max jjr ðtÞjjaverage

ð9:82Þ

fault free

Residual evaluation strategy is given as follows: Javerage Jth;average for no fault Javerage [ Jth;average for fault

ð9:83Þ

RMS value: Root-mean-square residual function is deﬁned as 0 JRMS

1 ¼ jjr jjRMS ¼ @ T

tþT Z

11=2 jjr ðsÞjj2 dsA

ð9:84Þ

t

RMS measures the average energy of r over time interval (t, t + T). Residual threshold can be given by the following equation: Jth;RMS ¼ max jjr ðtÞjjRMS fault free

ð9:85Þ

Residual evaluation strategy is given by: JRMS Jth;RMS for no fault JRMS [ Jth;RMS for fault

ð9:86Þ

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9.8

D. Agarwal and C. K. Singh

Fault Detection in Airpath of Diesel Engines

The turbocharger dominates the airpath of diesel engines; it consists of the turbine and compressor. The turbine is driven by the exhaust gas flow. Turbine speed is controlled by variable geometry turbocharger (VGT) actuator that controls the exhaust flow going into the turbine. When the VGT actuator closes a venturi, effect is created, and exhaust enters the turbine at higher flow velocity, thus rotating turbine at higher RPM and vice versa. When the turbine speed is increased, the air in the inlet charge is more compressed, i.e., increased intake manifold pressure. The second path from the exhaust gas to the intake manifold is the exhaust gas recirculation, which is needed for NOx reduction. The burned gas fraction is re-circulated into the intake manifold. This lowers the flame temperature and decreases the NOx in the exhaust gas. The basic structure of the system is shown in Fig. 9.15. The equations of airpath model of diesel engine are derived next (Herceg 2006; Horvath and Edelmayer 2016; Jung 2003). All the model parameters and their corresponding values as proposed by Jung (2003) are summarized in Table 9.1. The air model of diesel engine is given by: dPi RTi ¼ dmcomp þ dmegr dmeng dt Vi

ð9:87Þ

dPe RTe ¼ dmeng þ dmfuel dmegr dmturb dt Ve

ð9:88Þ

Fig. 9.15 Diesel engine airpath schematic and model variables

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193

Table 9.1 Nomenclature of variables used in airpath model Symbol

Variable description

Value

Units

s gm Vi Ve gc Ta Cp Cv c Pa Te R gv Ti VD Pref Tref gt n

Turbocharger time constant Turbocharger mechanical efﬁciency Volume of the intake manifold Volume of the exhaust manifold Compressor efﬁciency Ambient temperature Speciﬁc heat at constant pressure Speciﬁc heat at constant volume Speciﬁc heat ratio Ambient pressure Exhaust gas temperature Gas constant Engine volumetric efﬁciency Gas temperature in the intake manifold Total engine displacement volume Reference pressure Reference temperature Turbine efﬁciency Number of cylinders

0.11 0.98 0.006 0.001 0.61 298 1014.4 727.4 0.286 101,325 509 287 0.87 313 0.002 101,325 298 0.76 4

s – m3 m3 – K J/kg K J/kg K – Pa K J/kg K – K m3 Pa K – –

dPOc 1 ¼ ðgm POt POc Þ s dt

ð9:89Þ

Equation (9.87) governs the dynamics of air in the intake manifold, Eq. (9.88) governs the dynamics of exhaust in the exhaust manifold, and Eq. (9.89) governs the dynamics of compressor power. dmeng is the engine pumping rate which is given by:

dmeng ¼

gv VD N 120 Ti R

ð9:90Þ

dmair is mass flow through the compressor, which depends on compressor power and pressures and is given by: g POc dmcomp ¼ c c ð9:91Þ cp Ta Pi 1 Pa

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c is speciﬁc heat ratio of fresh air and is given by c¼

Cp Cv Cp

ð9:92Þ

dmegr mass flow through the EGR valve is modeled as standard oriﬁce equation and given by: dmegr dmegr

sﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ

ﬃ Pi Pi if Pe Pi 1 2 Pe Pe sﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ

ﬃ Pe Pe Pe ¼ Aegr pﬃﬃﬃﬃﬃﬃﬃﬃ 2 1 if Pi [ Pe Pi Pi RTe Pe ¼ Aegr pﬃﬃﬃﬃﬃﬃﬃﬃ RTe

ð9:93Þ

If Pi [ Pe , then the air flow gets reversed and flow from intake to exhaust. In above equation, Aegr denotes the effective flow area of the EGR valve which is a quadratic function of the normalized position. According to Jung (2003), turbine mass flow rate is given by: dmturb

Pe ¼ Avgt Pref

rﬃﬃﬃﬃﬃﬃﬃsﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ

Tref Pa Pa Pe 2 1 1 þd c T2 Pe Pe Pa

ð9:94Þ

In above equation, Avgt denotes the effective flow area of the VGT. Aegr and Avgt are input to state-space model. Assuming constant turbine efﬁciency, the turbine power is related to its mass flow via the equation: c

Pi POt ¼ dmturb cp Te gt 1 Pa

ð9:95Þ

Substituting Eqs. (9.90), (9.91), and (9.93) into Eq. (9.87) yields: 0

1 sﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ

ﬃ dPi RTi B gc POc Pe Pi Pi g VD N C v ¼ 1 þ Aegr pﬃﬃﬃﬃﬃﬃﬃﬃ 2 @ A dt Vi cp Ta Pi c 1 Pe Pe 120 Ti R RTe Pa

ð9:96Þ

Substituting Eqs. (9.90), (9.93), and (9.94) into Eq. (9.88) yields: sﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ

ﬃ gv VD N Pe Pi Pi þ dmfuel Aegr pﬃﬃﬃﬃﬃﬃﬃﬃ 2 1 120 Ti R Pe Pe RTe s ﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ rﬃﬃﬃﬃﬃﬃﬃ

!

Pe Tref Pa Pa Pe c 2 Avgt 1 1 þd Pref T2 Pe Pe Pa

dPe RTe ¼ dt Ve

ð9:97Þ

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195

Substituting Eqs. (9.94) and (9.95) in Eq. (9.89) yields: dPOc 1 Pe cp Te gt gm Avgt ¼ s dt Pref

! rﬃﬃﬃﬃﬃﬃﬃ

c sﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ

Tref Pi Pa Pa Pe 1 1 1 þ d POc 2 c T2 Pa Pe Pe Pa

ð9:98Þ The above model is linearized around the speciﬁed equilibrium point (Jung 2003), with set values and model variables summarized in Table 9.2. The operating point has been chosen in the medium-speed region at 1900 rpm. According to study in Jung (2003), the control inputs of the LTI system are the position of the exhaust gas recirculation valve (EGR actuator) and variable geometry turbocharger (VGT actuator); the outputs of the model are air mass flow (MAF), exhaust manifold gas pressure (EMAP), and intake manifold pressure (MAP). By linearizing at 1900 rpm, the following LTI model is obtained: 2

3 2 32 3 5:2643 4:7316 28:5021 Pi P_ i 6 _ 7 6 76 7 0 4 Pe 5 ¼ 4 50:7697 156:9287 54 Pe 5 _ c 0 0:4287 9:0909 POc PO 3 2 32 9 Aegr 1:6111 10 0 0 7 6 76 þ 4 1:572 1010 8:3514 104 1:46083 108 54 Avgt 5 dmfuel 0 141:6484 0 ð9:99Þ Output equation can be written as: 2

3 2 Pi 1 4 Pe 5 ¼ 4 0 0 dmair

0 1 0

32 3 0 Pi 54 Pe 5 0 5 3:924 10 POc

ð9:100Þ

Table 9.2 Description of model states, input and output variables of the system Variable

Notation

Function

Value

EGR—actuator effective area VGT—actuator effective area Fueling Intake manifold pressure

Aegr Avgt dmfuel Pi

4 10−5 m2 0.175 m2 0.032 kg/s –

Exhaust manifold pressure

Pe

Turbine power Air mass

POt dmair

Input Input Input State variable 1 Output 1 State variable 2 Output 2 State variable 3 Output 3

– – –

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The above equations are equations for nominal plant as discussed in Eq. (9.1) where: 2 3 5:2643 4:7316 28:5021 6 7 A ¼ 4 50:7697 156:9287 0 5 0 0:4287 9:0909 2 3 1:6111 109 0 0 6 7 B ¼ 4 1:572 1010 8:3514 104 1:46083 108 5 2

0 1

0

6 C ¼ 40 0 2 0 6 D ¼ 40 0

1 0 0 0 0

0

141:6484 3

0

7 0 5 5 3:924 10 3 0 7 05 0

The next step is to design state observer that measures the states of nominal plant. The objective is design the observer gains such that state error as given by Eq. (9.32) should asymptotically converge to zero in fault-free case. This condition can be satisﬁed when the poles of ðA HCÞ lies in left-hand side of complex plane. The gain matrix H of the full-state observer can be computed using row-reduced echelon (RRE) method, singular value decomposition (SVD) method, or place command in MATLAB. Figure 9.16 shows step inputs, effective EGR Aegr , effective VGT area AVGT , and fuel mass flow dmfuel , provided to the nominal plant as given by Eq. (9.99). Figure 9.17 shows the system output, intake manifold pressure Pi , exhaust manifold pressure Pe , and air mass flow dmair , from the nominal plant as given by Eq. (9.100). It takes time for the intake manifold pressure to reach the steady-state pressure because initially when step input is provided turbine does not have enough energy to rotate compressor wheel to produce sufﬁcient boost pressure. This is reflected in the slow rise in intake manifold pressure as shown in Fig. 9.17. To make the observer stable, all the poles of system matrix ðA HCÞ are assigned in negative half of complex plane [−123, −50, −20]. MATLAB place method is used to calculate observer matrix H. 2

14:735 H¼4 4:731 7:2635 105

50:769 33:928 0

3 0 0:4287 5 1:0425 106

The stability of observer response can be ascertained by the fact that observer error asymptotically decreases to zero as shown in Eq. (9.33). As shown in Fig. 9.18, all the residuals of MAP, EMAP, and air mass flow are converging to zero.

9 Model-Based Fault Detection on Modern Automotive Engines

Fig. 9.16 Input signals to nominal plant

Fig. 9.17 Model output of step response input

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Fig. 9.18 Residuals of output signals converging to zero with state observer

The LTI model is extended to include two actuator faults: an EGR actuator fault and a VGT actuator fault, denoted by fegr(t) and fvgt(t,) respectively. Similarly, on the sensor side, two faults are included, fault on the intake manifold pressure sensor and fault on exhaust manifold pressure sensor. In this example, two examples are simulated the details of which are given in Table 9.3. The example is just for illustration purposes, and the proposed methodology can be extended to simulate system output for any number of faults. In both the cases, it is assumed there are no process/system faults. Case 1: In case 1, stuck EGR valve fault is simulated. The residual dynamics of states is given by Eqs. (9.38), (9.39) x_ e ðtÞ ¼ ðA HCÞxe ðtÞ þ ðL1 HL2 Þf ðtÞ ye ðtÞ ¼ Cxe ðtÞ þ L2 f ðtÞ

Table 9.3 Fault simulation conditions and fault timings Cases

Fault conditions

Value

Time at which fault is simulated

Case 1

EGR valve stuck at 50% of input value given in Table 9.2 Intake manifold pressure has constant bias of 5000 pascals VGT stuck at 75% of input value given in Table 9.2

Aegr ¼ 2 105 m2

Fault simulated after 3s Fault simulated after 3s Fault simulated after 2s

Case 2

Pi ¼ 5000 Pa Avgt ¼ 0:13125 m2

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For stuck EGR, fault vector is given by: f ¼ ½ faEGR

faVGT

fadmFuel fsPi

fsdmAir

fsPe

ð9:101Þ

where ﬁrst three are actuator faults, EGR, VGT, and fuel mass flow, and last three are sensor faults, intake manifold pressure, exhaust manifold pressure, and air mass flow rate. Fault vector for stuck EGR is given by: f ¼ 2 105

0

0

0 0

0

ð9:102Þ

As the actuator faults enter the system in the same direction as the input does, the fault direction matrices L1 can be signed as the input matrices B. L1 is given by: 2

1:6111 109 4 L1 ¼ 1:572 1010 0

0 8:3514 104 141:6484 2

0 0 L2 ¼ 4 0 0 0 0

0 0 0

0 0 1:46083 108 0 0 0 0 0 0

0 0 0

3 0 05 0

3 0 0 0 05 0 0

ð9:103Þ

ð9:104Þ

System is simulated in MATLAB. Jump in residuals is clearly observed when the fault happens at 3 s. Initially, before the fault exists, the residual is zero, while after the fault happens, there is sudden jump in MAP and EMAP residual. This change in residual can be diagnosed because there is fault in the EGR valve, causing the intake manifold pressure to deviate from its steady-state value (Fig. 9.19). Case 2: Case 2 is simulated with stuck VGT at 75% of steady-state value given in Table 9.2. In this case, MAP sensor fault is simulated by biasing the sensor output by 0.5 bars. Fault vector for stuck EGR is given by: f ¼ ½0

0:13125

0 5000

0

0

ð9:105Þ

Fault signature matrix L1 is given by: 2

1:6111 109 L1 ¼ 4 1:572 1010 0

0 8:3514 104 141:6484 2

0 0 L2 ¼ 4 0 0 0 0

0 0 0

1 0 8 1 1:46083 10 1 0 1 1 1

3 0 0 0 05 0 0

0 0 0

3 0 05 0

ð9:106Þ

ð9:107Þ

Figure 9.20 shows the model output of nominal system. VGT stuck fault happens at 2 s, and MAP sensor bias develops at 3 s. The EMAP residual transitions

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Fig. 9.19 Fault case 1: model output and residual of different signals when EGR valve is stuck at 50% value

Fig. 9.20 Fault case 2: model output and residual of different signals when VGT valve is stuck at 75% value and MAP sensor has constant bias of 0.5 bars

from zero to nonzero value at 2 s. Moreover, there is a big transition in EMAP residual at 3 s when the sensor fault happens. Similar transitions are visible in MAP and air mass flow residuals. These transitions serve as a precursor to VGT actuator

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and MAP sensor faults because they cause the system states to deviate from their steady-state value. Similar effect of other fault cases can be simulated by changing the L1 ; L2 and fault vector f .

9.9

Fault Diagnostic with Virtual Test Environment

The growing importance of onboard diagnosis (OBD) for automobiles demands for new diagnostic test methodologies and techniques. This in turn demands for more testing and calibration time. To meet shorter development time with complex control system design requires complex tools. These tools help us to communicate with control system and their functions. However, ﬁrst step for the development process is to make engine and after-treatment model which can represent real-world condition accurate enough to optimize design and control. The modeling of engine processes continues to develop as our basic understanding of the physics and chemistry improves (Heywood 1988). Modeling activities can make major contribution to engine engineering at different levels of generality or details. The goal is to predict engine behavior over a wide range of design and operating variables to screen concepts prior to major hardware program. These models describe the thermodynamic fluid heat flow.

9.9.1

Diagnostic with Virtual Test Environment

To set up a working environment, ﬁrst step is to build reliable engine model. This setup requires integration of tools such as hardware in loop (HIL) and software n loop (SIL) (Andric et al. 2018). The combination of HIL and SIL makes a virtual test environment. It is a desktop-based setup and does not require real engine. Engine physical behavior is represented by simulation model which in turn integrated to ECU software where physical calibration parameter can be accessed (Nanjundaswamy et al. 2011). The basic system layout can be seen in Fig. 9.21. To create engine model, 1D simulation is used to get simulated engine output data such as engine flows, exhaust temperatures, emissions. The simulation model is further trained with real engine measurement data collected from transient and steady-state measurements. Model also computes interaction and dependencies of different physical parameters of engine. Modern days engine and after-treatment system have become more complex due to its legal requirement. Because of the increasing complexity of the powertrain systems, their control and optimization become more and more challenging, which also bring more complexity on fault detection and fault tracing. It is important to understand complete engine and after-treatment system behavior to avoid misdetection of faults that can lead to increase in engine emissions on ﬁeld. Most of the work requires test bed testing, validation, ﬁeld testing, and fleet monitoring. Lot of

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Fig. 9.21 Virtual working environment

these tests can be performed with virtual test environment. In terms of engine diagnostics, a virtual sensing system may be integrated into a smart OBD system to give immediate warning of emissions exceedances. Virtual sensing can form the basis of a diagnostic information system and can provide signiﬁcantly more information on engine, vehicle performance, and emissions than is presently available, while still using an existing sensor suite. These virtual sensors can be used to monitor engine performance, and if the predicted engine performance deviates from the real data coming from the engine, a potential fault condition can be identiﬁed.

9.10

Conclusion

Model-based fault diagnostics has an advantage from other traditional method of capturing the system faults. The chapter presented the summary of fundamental concepts involved in developing the model-based control system diagnostics. Model-based fault diagnostics captures the system fault by modeling the system dynamics and comparing the model output with the system output. The difference between the model output and the system output is termed as residual. To identify a fault, the residual is compared with the predeﬁned threshold, which deﬁnes the maximum and minimum value of signal can achieve under different operating conditions of the system. If the residual value exceeds or falls below the threshold value, then the error is flagged. Moreover, model-based system presents a greater flexibility in fault detection by decoupling the effects of sensor noise and process disturbances on residual calculations.

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The concept of model-based fault detection is applied to fault detection on diesel engine airpath. System responses are generated for two fault cases, EGR valve stuck at 25% and second VGT valve stuck at 50% and MAP sensor bias of 5000 Pa. The simulation of intake manifold pressure and exhaust manifold pressure shows the deviation of signals from the steady-state values when the fault happens. The transitions from steady-state value serve as a precursor to VGT actuator and MAP sensor faults because faults cause the deviation of system states. Model-based system can be used to diagnose any number of signal faults. Model-based fault diagnostic methodology presented in this manuscript represents a more speciﬁc case of linear time-invariant systems. However, the concepts developed in this chapter can be extended to more general case of nonlinear systems.

References Allen SM, Cagavan AK (1990) An expert system approach to global fault detection and isolation design. Charles River Analytics Inc. Report Andric J, Schimmel D, Sediako AD, Sjoblom J et al (2018) Development and calibration of one dimensional engine model for hardware-in-the-loop applications, SAE Technical Paper 2018-01-0874. https://doi.org/10.4271/2018-01-0874 Balle P, Isermann R (1998) Fault detection and isolation for nonlinear processes based on local linear fuzzy models and parameter estimation. In: Proceedings of American control conference, pp 1605–1609 Boubaker O (2005) Robust observers for linear systems with unknown inputs: a review Brumback B (1987) A chi-square test for fault-detection in Kalman ﬁlters. IEEE Trans Autom Control 32:552–554 Carrillo MG, Kinnaert M (2010) Sensor fault detection and isolation in three phase systems using a signal-based approach. IET Control Theory Appl 4:1838–1848 Castillo I, Edgar T (2008) Model based fault detection and diagnosis. In: TWCCC conference, Spring 2008, Austin, Texas Chen W (2007) Model based fault diagnosis in complex control systems-robust and adaptive approaches. Ph.D. dissertation, Simon Fraser University Chen J, Patton RJ (1999) Robust model-based fault diagnosis for dynamic systems. Kluwer Academic Publishers, Boston/Dordrecht/London Chen J, Patton RJ, Zhang HY (1996) Design of unknown input observers and robust fault detection ﬁlters. Int J Control 63:85–105 Chen Z, Patton RJ, Chen J (1997) Robust fault tolerant system synthesis via LMI. In: Proceedings of IFAC symposium on fault detection, supervision and safety for technical processes: SAFEPROCESS’97, vol 1, pp 347–352, The University of Hull, UK Commault C, Dion J-M, Sename O, Motyeian R (2002) Observer-based fault detection and isolation for structured systems. IEEE Trans Autom Control 47:2074–2079 Ding X, Guo L, Jeinsch T (1999) A characterization of parity space and its application to robust fault detection. IEEE Trans Autom Control 44:337–343 Duan GR, Patton RJ (2001) Robust fault detection using Luenberger-type unknown input observers-a parametric approach. Int J Syst Sci 32:533–540 Floquet T, Barbot JP, Perruquetti W, Djemai M (2004) On the robust fault detection via sliding mode disturbance observer. Int J Control 77:622–629 Frank PM (1994) Enhancement of robustness on observer-based fault detection. Int J Control 59 (4):955–983

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Frank PM (1996) Analytical and qualitative model-based fault diagnosis-a survey and some new results. Eur J Control 2:6–28 Frank PM, Ding X (1997) Survey of robust residual generation and evaluation methods in observer-based fault detection system. J Process Control 7(6):403–424 Garcia-Perez A, de Jesus Romero-Troncoso R, Cabal-Yepez E, Osornio-Rios R (2011) The application of high-resolution spectral analysis for identifying multiple combined faults in induction motors. IEEE Trans Ind Electron 58:2002–2010 Gertler JJ (1998) Fault detection and diagnosis in engineering systems. Marcel Dekker, New York Hammouri H, Kinnaert M, El-Yaagoubi EH (1999) Observer-based approach to fault detection and isolation for nonlinear systems. IEEE Trans Autom Control 44:1879–1884 Herceg M (2006) Nonlinear model predictive control of a diesel engine with exhaust gas recirculation and variable geometry turbocharger. Diploma thesis Heywood JB (1988) Internal combustion engine fundamentals. McGraw-Hill International Editions Horvath Z, Edelmayer A (2016) Robust model-based detection of faults in the air path of diesel engines, 28 Apr 2016 Jiang B, Chowdhury F (2004) Observer-based fault diagnosis for a class of nonlinear systems. In: Proceedings of American control conference, vol 6, pp 5671–5675 Jiang T, Khorasani K, Tafazoli S (2008) Parameter estimation-based fault detection, isolation and recovery for nonlinear satellite models. IEEE Trans Control Syst Technol 16:799–808 Jiang B, Zhang K, Shi P (2011) Integrated fault estimation and accommodation design for discrete-time takagi-sugeno fuzzy systems with actuator faults. IEEE Trans Fuzzy Syst 19:291–304 Jung M (2003) Mean-value modelling and robust control of the airpath of a turbocharged diesel engine. Ph.D. dissertation, February 2003 Liu XQ, Zhang HY, Liu J, Yang J (2000) Fault detection and diagnosis of permanent-magnet dc motor based on parameter estimation and neural network. IEEE Trans Industr Electron 47:1021–1030 Lu PJ, Zhang MC, Hsu TC (2001) An evaluation of engine faults diagnostics using artiﬁcial neural networks. J Eng Gas Turbines Power 123:340–347 Miljkovic D (2011) Fault detection methods: a literature survey Nanjundaswamy H, Tatur M, Tomazic D, Dahodwala M, Eping T, Virnich L, Xin QH, Gorczowski W, Read M (2011) Development and calibration of on-board-diagnostic strategies using a micro-HiL approach, SAE 2011-01-0703 Patton RJ, Chen J (1997) Observer-based fault detection and isolation: robustness and applications. Control Eng Pract 5(5) Patton RJ, Frank PM, Clark RN (2000) Issues of fault diagnosis for dynamic systems. Springer, London Shields DN (2005) Observer-based residual generation for fault diagnosis for non-afﬁne non-linear polynomial systems. Int J Control 78:363–384 Simani S, Fantuzzi C, Patton R (2003) Model-based fault diagnosis in dynamic systems using identiﬁcation techniques. Springer, London Simani S, Fantuzzi C, Patton RJ (2013) Model-based fault diagnosis in dynamic systems using identiﬁcation techniques Speyer JL (1999) Residual sensitive fault detection ﬁlters. In: MED’99, pp 835–851, Haifa, Israel Venkatasubramanian V, Rengaswamy R, Kavuri SN, Yin K (2003) A review of process fault detection and diagnosis part I: quantitative model-based methods. Comput Chem Eng 27:293–311 Zhang K, Jiang B, Shi P (2012) Observer-based fault estimation and accommodation for dynamic systems. Springer, Berlin

Chapter 10

Study of Instability Nature of Circular Liquid Jet at Critical Chamber Conditions Dhanesh Ayyappan, Aravind Vaidyanathan, C. K. Muthukumaran and K. Nandakumar Abstract Direct Injection (DI) technology that is used in the IC engines has gained signiﬁcant interest over the last few decades. In DI engines, the fuel is directly injected into the combustion chamber through an injector. The efﬁciency and the corresponding emission characteristics of DI, IC engines are dependent on the thermo-physical properties of the fuel and its corresponding spray dynamics and air/fuel mixing. Therefore, a comprehensive understanding of the process involving liquid injection, breakup and atomization, and combustion inside the engine environment is very essential. There are several theoretical and experimental studies carried out to understand the stability of the liquid jets, but there is not much literature available to understand the behavior of liquid jets near the injector at critical and supercritical conditions. This is largely due to the experimental and computational challenges associated with the process of high-pressure injection. The main objective of this paper is to study the instability nature on the liquid jet near the injector at critical conditions. Visualization using high-speed camera is used to capture the images of the disturbances to bring further insight into the jet dynamics at critical conditions. Experiments are carried out for different environmental pressures for both single component system and binary component system where the environment is a mixture of two fluids. Keywords Liquid injection Instability

Supercritical mixing Atomization

D. Ayyappan A. Vaidyanathan (&) Indian Institute of Space Science and Technology, Trivandrum, India e-mail: [email protected] C. K. Muthukumaran K. Nandakumar Liquid Propulsion Systems Centre, ISRO, Trivandrum, India © Springer Nature Singapore Pte Ltd. 2019 A. K. Agarwal et al. (eds.), Advanced Engine Diagnostics, Energy, Environment, and Sustainability, https://doi.org/10.1007/978-981-13-3275-3_10

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Introduction

The efﬁciency and corresponding emission characteristics of diesel engines and rocket engines are very much related to the thermo-physical properties of the fuel and the corresponding spray dynamics. Thus, for the efﬁcient design of the injectors of diesel engines and rocket engines, a thorough understanding of the fluid dynamics as well as the thermodynamic aspect of the jet behavior is very much necessary. Inside the combustion chamber of diesel engines or rocket engines, the pressure and the temperatures are mostly above the critical state of the fluid (fuel) under consideration. There exist various anomalies near the critical point, and studies involving comprehensive and detailed experimentations are unavoidable. The focus of the present study is to understand the behavior of a liquid jet at critical conditions in a single component system and multicomponent system.

10.1.1 Regimes of Liquid Jet Breakup The breakup of a liquid jet into ﬁner drops or more commonly termed as atomization which increases the surface area of the injectant fluid. In high-pressure conditions like in the case of rocket engines and diesel engines, the liquid jet is atomized into small droplets, thereby increasing the surface area for effective mixing and combustion process. Thus, the understanding of the fundamental process of the liquid jet is very much necessary. When a circular liquid jet emanates from an oriﬁce into a quiescent environment, it preserves the cylindrical shape with a distinct interface. The interface separates the injectant liquid from the ambient medium which is characterized by the surface tension. There are various fluid dynamic forces that are responsible for the generation of instabilities and breakup of liquid jets, namely, inertial force, surface tension force, viscous force, and gravity force. For a given set of fluid properties like viscosity and surface tension, the behavior of the liquid jet is heavily depended on the injection velocity. Reitz and Bracco (1982) divided the liquid jet breakup into four regimes (Fig. 10.1): Rayleigh Regime At low injection velocities, the liquid jet is initially cylindrical and at a certain downstream distance, and it is subjected to a growth of axisymmetric waves which are generally in the longitudinal direction. This eventually leads to the formation of drops which are of the order of jet diameter. First Wind-Induced Regime In the ﬁrst wind-induced regime, with further increase in injection velocity, the asymmetric perturbation becomes possible due to the signiﬁcant aerodynamic effect on the jet. The surrounding environment produces irregular weaving on the surface of the jet which results in the decrease in breakup length of the jet. The size of the droplets produced is slightly smaller than those in the Rayleigh regime.

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Fig. 10.1 Regimes of atomization

Second Wind-Induced Regime In the second wind-induced regime, with an increase in injection velocity, the inertia of the jet overcomes the aerodynamic shear which results in increased breakup length of the jet. But due to the increased flow turbulence and the due to the aerodynamic effect, the disturbance wavelengths become shorter. The liquid jet shape is chaotic from the nozzle exit, and the droplets are pinched from the surface of the jet. Taylor Regime or Atomization At high injection velocities, the jet breakups into very ﬁne droplets immediately as it emanates from the oriﬁce. The drops formed in this regime are very ﬁne due to strong wind effects. When a liquid jet is injected into extreme pressure and temperature conditions, the thermodynamic properties of the fluid undergo continuous variation and could affect the spray dynamics which is related to the mixing of the fluid to the ambient medium. Hence, there is a strong relationship between the fluid dynamic and the thermodynamic processes.

10.1.2 Thermodynamic Supercritical State Fluids are generally divided into two phases, the liquid phase and vapor (gas) phase. At subcritical conditions, i.e., where the operating pressure and temperature are below the corresponding critical values (Pc and Tc), the liquid and vapor phase coexist with a distinct interface that is characterized by surface tension. With the increase in the temperature of the system, the vapor pressure increases,

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Fig. 10.2 Thermodynamic supercritical state

whereas the liquid phase density decreases. As the critical point of the system is approached, the vapor pressure of the system approaches the critical pressure with vapor density almost equal to that of the liquid phase. At the critical point, the surface tension becomes null and the fluid exists in a homogeneous state where the distinct interface between the liquid phase and the vapor phase ceases to exist. Beyond the critical point with T > Tc and P > Pc, the system exists in a single-phase regime with physical properties like density in the order of liquid phase and gas-like transport properties. When the operating conditions of the system exceed corresponding critical properties of the fluid (T > Tc and P > Pc), the system is known to be in the supercritical state (Fig. 10.2). There are certain other anomalies exhibited by fluid at supercritical conditions like inﬁnite heat capacity, a decrease in thermal conductivity, and absence of latent heat of evaporation. In engineering applications like diesel engines and rocket engines, where the fuel at subcritical conditions is injected into the chamber at supercritical conditions, the injected jet at some point of time reaches the thermodynamic critical point. With the various anomalies in the transport properties of the fluids in the vicinity of the critical point, the modeling of the fluid dynamics of the jet at supercritical conditions become more challenging.

10.1.3 Earlier Studies on Liquid Jets at Supercritical Conditions Though the study of fluids at critical conditions are mostly used in applications like combustion chamber of diesel engines and rocket engines, most of the earlier studies on the liquid jet at critical conditions investigated the jet that is injected into a thermodynamically controlled, non-reacting environment. This is commonly referred to as a ‘cold flow’ experiment. In cold flow experiment, the physics

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involved in spray phenomena and mixing can be precisely studied as compared to those conducted in an actual combustion chamber. The study of the liquid jet breakup has been started by Plateau over a century before; the study of jet injection at supercritical condition was initiated by Newman and Brzustowski (1971). The authors studied the jet behavior of liquid CO2 into a supercritical environment of N2/CO2 mixture at different compositions. In their study, it was observed that as the chamber condition approached critical state, the spray became ﬁner with very small droplets. The behavior of a single and coaxial jet from subcritical to supercritical conditions was investigated by Mayer et al. (2000). The authors injected cryogenic liquid N2 into gaseous N2 and reported that beyond the critical point with the absence of surface tension there is a change in atomization process and the interface of the jet exhibited steaky like appearances. Chehroudi et al. (1999, 2002) conducted studies to investigate the physics of liquids at supercritical conditions. The authors studied the initial growth rate of the injected liquid nitrogen over a wide range of environment to injectant density ratios. It was observed that the initial spreading angle of the liquid jet at subcritical conditions resembled that of a liquid spray. Their studies reported that supercritical jet exhibited features similar to that of variable density gaseous jets. Reviews by Bellan (2000), Oschwald et al. (2006) and Chehroudi (2012) provide recent developments in the studies of fluids at supercritical conditions. Recently, many researchers used planar laser-induced fluorescence (PLIF) technique to study the mixing characteristics of fluids at supercritical conditions. Segal and Polikhov (2008) investigated the injection of a subcritical jet into a supercritical environment. The results revealed the absence of droplet formation in the supercritical environment. Roy et al. (2013) injected supercritical jet into a subcritical environment. The authors observed the presence of droplet formation from the supercritical jet in subcritical chamber conditions. However, the fundamental physics that cause the liquid jet to cause the transition from classical spray atomization to diffusion dominated mixing are not understood thoroughly. It was assumed that at supercritical pressure conditions, the surface tension becomes zero and that causes the liquid jet to have a thermodynamic transition to the supercritical state. But it was later proved that this is true only for single component system, i.e., where both the injecting fluid and the chamber medium are of the same fluid. But in the case of multicomponent systems, many studies reveal that the liquid jet still exhibits the presence of surface tension at supercritical chamber conditions. Experiments by Muthukumaran and Vaidyanathan (2014, 2015) provide direct evidence for the presence of surface tension at supercritical conditions. The authors investigated the behavior of elliptical liquid fluoroketone jets in gaseous nitrogen and helium environment at near and supercritical chamber conditions. Their investigation revealed that a liquid jet in a multicomponent system does not undergo a thermodynamic transition instantaneously, simply because the chamber pressure and temperature conditions exceed the critical condition of the injecting fluid. Crua et al. (2015) in their experiments also observed the presence of surface tension for heavier hydrocarbons at supercritical conditions. Similar observations

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were also reported by Manin et al. (2015), Falgout et al. (2016), Dahms and Oefelein (2013) in their studies in recent years. Recently, Dahms (2016) proposed a detailed theoretical framework for the physics involved in spray formation in the high-pressure environment using linear gradient theory model. The present works aim at understanding the transition characteristics as well as instability nature of a liquid jet at transcritical and supercritical chamber conditions. The experiments are conducted for low velocity subcritical fluoroketone liquid jet injected into its own environment and nitrogen–fluoroketone environment. The chamber condition is varied from near critical to supercritical conditions. The instability nature on the liquid jet is studied when the composition of the fluid in the chamber environment is varied.

10.2

Experimental Facility

10.2.1 Experimental Setup The experimental facility is built in Advanced Propulsion and Laser Diagnostics (APLD) lab, IIST to conduct experiments at supercritical conditions. A high-pressure chamber has been designed which can withstand a pressure of 70 bar and a temperature of 700 K which is provided with quartz optical access at its four sides for the jet visualization. Four through holes are provided at the corners of the chamber to insert the cartridge heater for heating the chamber. A circular oriﬁce is provided at the top of the chamber through which the injectant fluid is injected. To measure the temperature and pressure of the chamber Omega K-type thermocouple and OMEGA dyne PX309-1KGV pressure transducer is used. The chamber is heated using cartridge heaters and the desired temperature is maintained with the use of PID controller with errors less than 5%. For supply lines, stainless steel tube of 6 mm outer diameter and 1.5 mm wall thickness is used which has a burst pressure of 250 bar. The nitrogen gas is supplied from the cylinder at desired pressure using pressure regulators. The EMERSON Coriolis mass flow meter is used for the purpose of measuring the mass flow rate of the fluid. The measuring range of the flow meter is from 0.2 to 10 g/s. The flow meter is excited using 15 VDC and it gives an output in the range 4–20 mA corresponding to the flow rate. The output is connected to the PXIe-6239 card of National Instruments (NI) system for acquisition. The LABVIEW software is used for monitoring the data obtained from the NI system. It is basically a visual programming language from National Instruments usually used for data acquisition and instrumentation control. Phantom high-speed camera is used for capturing the high-speed images of the liquid jet. The schematic view of the experimental setup is shown in Fig. 10.3. An engineering fluid Dodecafluoro–2–methylpentan–3–one commonly referred to as fluoroketone (Fk) is used for the study of liquid jets at critical conditions

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Fig. 10.3 Schematic view of the experimental facility

primarily because of its low critical pressure Pc = 18.6 bar and critical temperature Tc = 441.8 K. It is a ﬁre suppressant and is thermally stable up to a temperature of 773 K.

10.2.2 Experimental Conditions Single component studies are conducted by injecting the laminar liquid jet into its own medium as the chamber environment. The desired chamber pressure is achieved by introducing sufﬁcient amount of fluoroketone into the chamber which is already maintained at supercritical temperature. This chamber condition is achieved prior to the injection of the fluoroketone jet. Experiments are conducted for varying chamber pressures from subcritical to supercritical conditions. The term subcritical or supercritical conditions are referenced with respect to the critical conditions of injectant fluid, fluoroketone. Binary component experiments are carried out by using a mixture of nitrogen and fluoroketone as the ambient medium. Initially, a known quantity of nitrogen is injected into the chamber up to a known pressure. Then the fluoroketone is introduced in to the chamber until the desired chamber pressure for the experimental condition is achieved. After the chamber environment is ﬁlled with the desired combination of nitrogen and fluoroketone, the fluoroketone jet is injected into the chamber and the behavior of the jet is closely monitored. The mole fraction of the individual species in the chamber environment is approximately estimated as xFk ¼ nFk =ðnN2 þ nFk Þ, where x is the mole fraction, n is the number of moles which is calculated as the ratio of mass of the respective fluid that is introduced into the chamber to the corresponding molecular mass of the fluid.

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The injectant fluid in all the cases is at room temperature, and the chamber pressure (for the single component system) and the mole fraction (for the binary component system) of the chamber environment is varied. To obtain the relationship between vapor pressure and temperature, Maxwell constructed PRSV equation of state is used. The details of the same are described in the work of Muthukumaran and Vaidyanathan (2014, 2015). The Reynolds numbers (Re = qvd/l) is calculated based on the injectant conditions where the transport properties are known; here q is the density of the injected jet, v is the velocity of the jet, d is the oriﬁce diameter, l is the dynamic viscosity of the injectant.

10.3

Results and Discussion

10.3.1 Single Component System Experiments in the single component system were conducted by injecting subcritical liquid fluoroketone into its own environment for varying chamber pressure from subcritical to supercritical conditions. The study is done to understand the nature of the disturbances formed on the liquid jet at transcritical and supercritical chamber conditions. Figure 10.4 shows the injection of fluoroketone into its own environment. At subcritical chamber pressure Pr = 0.718, the injected jet follows classical atomization (Fig. 10.4a). The injected jet is observed to have a sinusoidal mode of instability which are generated due to the aerodynamic drag acting on the jet due to the surrounding gas. As the chamber pressure is increased to Pr = 0.761, the disturbance wavelength of the jet and the drop size decreases. This trend in the decrease of the disturbance wavelength is observed as the chamber pressure is increased further (Fig. 10.4c–e). The droplet size also follows the same trend as the disturbance wavelength. At the critical point, the injected jet is observed to behave like a turbulent gaseous medium. The distinct interface between the liquid and the vapor phase vanishes, indicating the absence of surface tension. The injected jet enters the chamber at room temperature, and when it is exposed to the supercritical chamber condition, it takes time to get heated and get mixed with the medium. As a result, even at supercritical chamber condition, the jet does not experience the thermodynamic transition instantaneously as it is exposed to a supercritical medium. Similar observations were reported from the study of Muthukumaran and Vaidyanathan (2016). Two different kinds of disturbances were observed to form on the surface of the liquid jet in transcritical conditions. For transcritical conditions, the pressure is below the critical pressure (P < Pc) and the temperature is above the critical temperature (T > Tc). Fig. 10.5 shows the high-speed images of injected jet at subcritical chamber conditions Pr = 0.718. The images show two types of disturbances on the liquid jet. The disturbances shown in the marked circle in Fig. 10.5a is due to the aerodynamic drag caused by

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Fig. 10.4 Fluoroketone injected into its own environment with chamber pressure varying from subcritical to supercritical conditions. Chamber temperature of 475 K (Tr = 1.07) from a–f chamber pressure Pr = 0.718 (a), 0.761 (b), 0.819 (c), 0.862 (d), 0.915 (e), and 0.99 (f) and injection Re = 1333 (a), 1293 (b), 1452 (c), 1492 (d), 1492 (e) and 1512 (f)

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Fig. 10.5 High-speed images of fluoroketone injected into its own environment at subcritical chamber conditions Pr = 0.718. Injecting Re = 1293. Time sequence from a to e = 0 s (a), 600 ls (b), 1200 ls (c), 1800 ls (d), and 2400 ls (e)

the dense surrounding gas which results in the breakup of the liquid jet. The other is the shorter wave disturbance observed on the liquid jet surface. The shorter waves formed on the liquid jet surface could be attributed to the heating of the incoming subcritical liquid jet. The formation of shorter waves on the liquid surfaces hastens the breakup of the liquid jet which otherwise would have happened at a farther distance downstream. The effect of shorter waves formed on the liquid jet surface is shown in Fig. 10.5.

10.3.2 Binary Component System Experiments for the binary component system are conducted by injecting subcritical fluoroketone jet injected into mixtures of nitrogen and fluoroketone that exist in a speciﬁed composition. Experiments are conducted for varying compositions of fluoroketone and nitrogen in the chamber environment. Newman and Brzustowski (1971) are the ﬁrst to investigate the composition effects on the behavior of the liquid jets at critical conditions. The authors reported that the ambient fluids play a very crucial role in the behavior of the liquid jets. The main focus of the current section is to study the influence of the composition of the fluids in the chamber environment on the instability nature of the liquid jet. It was observed from the studies of Muthukumaran and Vaidyanathan (2016) that a subcritical jet behavior is dependent on the fluids in the environment. In their study, the subcritical fluoroketone behaved differently for nitrogen environment and fluoroketone environment. Similar observations were also made by Woodward and Talley, when Helium was introduced into the supercritical medium of nitrogen, the

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injected jet showed fluid like structures, thereby indicating the presence of surface tension. In the present study, the chamber is ﬁlled with a mixture of nitrogen and fluoroketone with their mole fraction ratio 1:3. Figure 10.6 shows the instability nature of subcritical fluoroketone jet injected into the mixture of the nitrogen–fluoroketone environment at subcritical chamber pressure Pr = 0.824. It can be seen in Fig. 10.6a that the liquid jet is observed to exhibit Rayleigh mode of instability initially. High-speed images show that as the time proceeds the instability nature of the liquid jet changes. In Fig. 10.6c, the instability mode of the liquid jet corresponds to the sinusoidal mode of instability. As one proceeds further, the instability mode was again changed to screw-like structure (Fig. 10.6e). It can be inferred from the study that, for a multicomponent system, the instability nature strongly depends on the composition of fluids in the chamber. In application like diesel engines, initially the combustion chamber is ﬁlled with compressed air. When the fuel is injected, the instability nature of the fuel starts becoming dependent on the composition of the fluid in the chamber and the composition within the chamber continuously varies as the fuel (diesel) gets mixed with the chamber environment. The behavior of the subsequent fuel injected into the chamber will be dependent on the mixture of fuel and air in the chamber.

Fig. 10.6 Fluoroketone jet injected into Nitrogen–fluoroketone mixture (mole fraction ratio 1:3) with chamber at subcritical pressure Pr = 0.718 and ﬁxed supercritical temperature of 475 K (Tr = 1.07). Pr = 0.824. Injecting Re = 1492. Time sequence from a to e = 0 s (a), 3000 ls (b), 5400 ls (c), 8600 ls (d) and 12400 ls (e)

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Conclusions

The behavior of the instability nature of subcritical liquid jets has been studied under subcritical and supercritical chamber conditions using visualization techniques. The study was performed on both single component and binary component systems. The following conclusions were made from the current study: (1) In the single component system, the disturbance wavelength on the liquid jet due to the aerodynamic effect tends to decrease with the increase in chamber pressure. (2) Two kinds of disturbances were observed on the liquid jet, one is a wavy structure on the jet which is attributed to the aerodynamic effect of surrounding gas, and the other is shorter wavelength disturbances formed on the surface of the liquid jet due to the heating of the injected liquid when exposed to the supercritical condition. It was observed that these shorter wavelength disturbances hasten the atomization of liquid. (3) Experiments in binary component system showed that the mixture composition plays a greater role in the behavior of the liquid jet breakup. It was observed that the nature of the disturbance may vary continuously with time as the injected fluid is mixed in the chamber environment. Acknowledgements The authors like to acknowledge Vinil Kumar R., Ajith Kumar at Advanced Propulsion and Laser Diagnostics (APLD) lab and Virosh at Manufacturing lab, Indian Institute of Space Science and Technology (IIST) for the valuable support in setting up the experimental facility and smooth conduct of experiments.

References Bellan J (2000) Supercritical (and subcritical) fluid behavior and modeling: drops, streams, shear and mixing layers, jets and sprays. Prog Energy Combust Sci 26:329–366. https://doi.org/10. 1016/S0360-1285(00)00008-3 Chehroudi B (2012) Recent experimental efforts on high-pressure supercritical injection for liquid rockets and their implications. Int J Aerosp Eng. https://doi.org/10.1155/2012/121802 Chehroudi B, Talley D, Coy E (1999) Initial growth rate and visual characteristics of a round jet inro a sub- to supercritical environment of relevance to rocket, gas turbine and diesel engines. AIAA J Propul Power 298 Chehroudi B, Talley D, Coy E (2002) Visual characteristics and initial growth rates of round cryogenic jets at subcritical and supercritical pressures. Phys Fluids 14:850–861. https://doi. org/10.1063/1.1430735 Crua C, Manin J, Pickett LM (2015) Transition from droplet evaporation to miscible mixing at diesel engine conditions. In: ICLASS 2015, 13th International conference on liquid atomization and spray systems, pp 1–8 Dahms RN (2016) Understanding the breakdown of classic two-phase theory and spray atomization at engine-relevant conditions

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Dahms RN, Oefelein JC (2013) On the transition between two-phase and single-phase interface dynamics in multicomponent fluids at supercritical pressures. Phys Fluids 25. https://doi.org/ 10.1063/1.4820346 Falgout Z, Rahm M, Sedarsky D, Linne M (2016) Gas/fuel jet interfaces under high pressures and temperatures. Fuel 168:14–21. https://doi.org/10.1016/j.fuel.2015.11.061 Manin J, Pickett LM, Crua C (2015) Microscopic observation of miscible mixing in sprays at elevated temperatures and pressures Mayer W, Schik A, Aerospace G, Tamura H (2000) Injection and mixing processes in high-pressure liquid oxygen/gaseous hydrogen rocket combustors introduction 16 Muthukumaran CK, Vaidyanathan A (2014) Experimental study of elliptical jet from sub to supercritical conditions. Phys Fluids 26. https://doi.org/10.1063/1.4871483 Muthukumaran CK, Vaidyanathan A (2015) Experimental study of elliptical jet from supercritical to subcritical conditions using planar laser induced fluorescence Muthukumaran CK, Vaidyanathan A (2016) Initial instability of round liquid jet at subcritical and supercritical environments. Phys Fluids 28:74104. https://doi.org/10.1063/1.4954312 Newman JA, Brzustowski TA (1971) Behavior of a liquid jet near the thermodynamic critical region. AIAA J 9:1595–1602. https://doi.org/10.2514/3.49962 Oschwald* M, Smith JJ, Branam R, Hussong J, Schik A, Chehroudi B, Talley D (2006) Injection of fluids into supercritical environments. Combust Sci Technol 178:49–100. https://doi.org/10. 1080/00102200500292464 Reitz RD, Bracco FV (1982) Mechanism of atomization of a liquid jet. Phys Fluids 25:1730–1742. https://doi.org/10.1063/1.863650 Roy A, Joly C, Segal C (2013) Disintegrating supercritical jets in a subcritical environment Segal C, Polikhov SA (2008) Subcritical to supercritical mixing

Chapter 11

Transient Reacting Flow Simulations of Chemical-Looping Combustion Reactors Guanglei Ma, Subhodeep Banerjee and Ramesh K. Agarwal

Abstract Chemical-looping combustion (CLC) has shown great promise in addressing the need for high-efﬁciency low-cost carbon capture from fossil-fueled power plants. In recent years, there has been a focus on developing high-ﬁdelity simulations of the CLC process in the literature to facilitate the transition of this technology from laboratory- and pilot-scale projects to deployment on an industrial scale. Detailed computational fluid dynamics (CFD) simulations of two CLC reactors are presented in this chapter. The ﬁrst case employs the Eulerian–Eulerian approach to investigate hot flow behavior with chemical reactions in a packed bed reactor with ilmenite oxygen carrier and carbon monoxide simulating the exact experimental conditions. Previous simulations of this setup were conducted for cold flow without chemical reactions. After 60 minutes of simulation, the results are in excellent agreement with experimental data. The second case is an Eulerian– Lagrangian model of a bubbling bed CLC reactor with hematite oxygen carrier and methane. The experiment is modeled to scale and particle interactions are calculated using the Discrete Element Method (DEM) coupled with CFD to solve the flow ﬁeld. Owing to the computational demands of DEM, only the simulation start-up is investigated and the results show reasonable agreement with the experiment.

Keywords Chemical-looping combustion Eulerian–Eulerian simulation Eulerian–Lagrangian simulation Chemical reactions Ilmenite

11.1

Introduction

The relationship between global surface temperature of the earth and concentration of CO2 in the atmosphere was discovered by Arrhenius as early as 1896 (Arrhenius 1896). CO2 levels in the atmosphere have risen from a value of 280 ppm in pre-industrial times to around 370 ppm today. This increase is due to the carbon G. Ma S. Banerjee R. K. Agarwal (&) Washington University in St. Louis, 1 Brookings Drive, St. Louis, MO 63130, USA e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2019 A. K. Agarwal et al. (eds.), Advanced Engine Diagnostics, Energy, Environment, and Sustainability, https://doi.org/10.1007/978-981-13-3275-3_11

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emissions from fossil-fueled power plants and other anthropogenic sources. Given that fossil fuels are projected to remain world’s primary energy source for years to come, there is an imminent need for high-efﬁciency carbon capture technologies to avoid the unequivocal warming of the global climate system (IPCC 2007). Chemical-looping combustion (CLC) is a new combustion technology that offers great advantage in high-efﬁciency low-cost carbon capture. In traditional (boiler) combustion, fuel and air are mixed directly in the reactor and the combustion process produces a complex mixture of syngas and CO2 and NOx and other pollutant gases, which drives up the energy cost associated with isolating the CO2 for capture and storage. On the other hand, the CLC process is divided over two reactors replacing the traditional boiler where the reduction and oxidation reactions occur, respectively. Fuel combustion occurs in the fuel reactor with metal oxide particles providing oxygen for the reaction or acting as catalysts to speed up the combustion. The reduced oxygen carrier is pneumatically transferred to the air reactor where it is oxidized by air. Although the overall reactants are fuel and air, the same as in traditional combustion, the advantage of CLC lies in the fact that the fuel reaction occurs in the absence of air, the combustion products, CO2 and H2O, are not contaminated by other pollutant gases. The H2O by is easily removed by condensation leaving behind a high concentration CO2 stream for treatment and storage. CLC has gained more and more attention in recent years and is likely to become the dominant technology in the near future. Computational fluid dynamics (CFD) simulations of fluidized beds for CLC in the literature using the Eulerian–Eulerian approach or two-fluid model have accurately captured the formation of gas bubbles and solids recirculation of cold flow systems (Shuai et al. 2011; Seo et al. 2011; Nguyen et al. 2012) as well as the bulk chemical reaction behavior of experimental reactors using gaseous fuels (Ahmed and Lu 2014; Banerjee and Agarwal 2016). Alternatively, the Lagrangian particletracking approach known as Discrete Element Method (DEM) can be coupled with Eulerian simulation of the fluid phase by modeling the interaction between particle and fluid individually for each particle. The Eulerian–Lagrangian simulation can resolve fluid and particle variables such as position, velocity, temperature, and composition with high accuracy. Recently, simulations using coupled CFD-DEM have proven capable in accurately matching the particle dynamics of various laboratory-scale fluidized bed experiments using relatively large particles and in incorporating chemical reactions into the DEM framework (Tsuji et al. 2008; Alobaid et al. 2013; Parker 2014; Banerjee and Agarwal 2015). This paper considers simulations of the experimental packed bed fuel reactor of Gallucci et al. (Gallucci et al. 2015) and the bubbling bed fuel reactor at the National Energy Technology Laboratory (Breault et al. 2017). The emphasis of this work is on the validation of the chemical reaction models incorporated into the CFD-DEM framework against experimental data. It is expected that the results of this work will provide a better understanding of the CLC reactor for future design and optimization.

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11.2

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Eulerian–Eulerian Simulation of a Packed Bed with Ilmenite

In a packed bed reactor, the inlet gas velocity is kept lower than the minimum fluidization velocity of the particles, so the particles do not move. Because of the low gas velocity, the timescales of the reactions in a packed bed reactor can be hours, which is beyond the scope of most CFD models given the computing resources typically available. The research presented in this section attempts to focus on simplify the model and speed up the computing time to run a simulation for up to an hour while still ensuring the accuracy of the results.

11.2.1 Modeling Approach and Numerical Solution Procedure The modeling work in this paper is performed using the commercial CFD solver ANSYS Fluent, release version 17.1. The flow in the packed bed reactor of Gallucci et al. (2015) involves chemical reactions and heat transfer. Hence, the continuity equation, Navier–Stokes momentum equations, and the energy equation are all enabled in Fluent to resolve the flow ﬁeld. To capture packed bed behavior accurately, the location of the solid phase is ﬁxed so the equations to calculate solid motion are omitted.

11.2.1.1

Conservation Equations

The continuity equation for each phase is written as X 1 @ _ _ ~ ðaq qq Þ þ r ðaq qq vq Þ ¼ ðmpq mqp Þ qrq @t

ð11:1Þ

where qrq is the phase reference density or the volume-averaged density of the qth phase in the solution domain. The solution of this equation for each secondary phase, constrained by the condition that the volume fractions sum to one, allows for the calculation of the primary-phase volume fraction. This treatment is common to fluid–fluid and granular flows. When solving the conservation equations for chemical species, Fluent predicts the local mass fraction of each species through the solution of a convection– diffusion equation for species j. The conservation equation form is given by

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@ ðqYj Þ þ r ðq~ vYj Þ ¼ r J j þ Rj þ Sj @t

ð11:2Þ

where Ri is the net rate of production of species i by chemical reaction and Si is the rate of creation by addition from the dispersed phase plus any user-deﬁned sources. An equation of this form will be solved for N 1 species where N is the total number of fluid phase chemical species present in the system. Given that the mass fraction of the species must sum to unity, the Nth mass fraction is determined as one minus the sum of the N 1 solved mass fractions. To minimize numerical error, the Nth species should be selected as the species with the overall largest mass fraction, such as N2 when the oxidizer is air. By default, Fluent uses the dilute approximation (also called Fick’s law) to model mass diffusion due to concentration gradients, under which the diffusion flux can be written as J i ¼ qDm;i rYi DT;i

rT T

ð11:3Þ

where Dm;i is the mass diffusion coefﬁcient for species i in the mixture and DT;i is the thermal diffusion coefﬁcient. When the Lewis number of any species i, Lei ¼

k qcp Di;m

ð11:4Þ

where k is the thermal conductivity is far from unity, neglecting the transport of enthalpy due to species diffusion can lead to signiﬁcant errors. This term is included in the numerical model in Fluent.

11.2.1.2

Computational Model

The packed bed fuel reactor used by Gallucci et al. (2015) is a cylinder with a height of 1580 mm and a diameter of 63 mm. The middle part of the reactor is ﬁlled to a height of 830 mm with an oxygen carrier comprising 75% ilmenite (FeTiO3) and 25% Mn2O3. The upper and lower parts (375 mm each) are ﬁlled with an inert material with the same size and shape particles as the oxygen carrier to create a packed bed conﬁguration. It is necessary to run the simulation for an hour to compare the reaction results with the experimental CLC reactor of Gallucci et al. (2015). The cylindrical geometry is thus modeled as a 2-D reactor with height 1580 mm and width 63 mm and meshed with a structural mesh comprising 11,000 3 3-mm cells. It should be noted that since the solid phase is treated as a stationary bed, the requirement of the Eulerian two-fluid model that the cell size be greater than the particle diameter does not apply. The model parameters are listed in Table 11.1.

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Table 11.1 Model parameters for packed bed reactor simulation Internal height of reactor Internal diameter of reactor Primary phase species Secondary phase species Solids diameter Oxygen carrier loading Oxygen carrier composition Oxygen carrier density Inlet gas composition Inlet gas pressure Gas volumetric flux Inlet boundary condition Outlet boundary condition Time step size

1580 mm 63 mm CO, CO2, N2 FeTiO3, TiO2, Fe, Mn2O3, inert 3 mm 3.3 kg 75% ilmenite (FeTiO3), 25% Mn2O3 Ilmenite: 4789 kg/m3, Mn2O3: 4500 kg/m3 30% CO, 15% CO2, 55% N2 4 bar 40 L/min Velocity inlet Pressure outlet 0.2 s

The initial temperature is set to 1123 K everywhere, and the wall boundary condition is set as adiabatic (zero heat flux at the walls). The bottom of the reactor is prescribed as a velocity inlet—the velocity magnitude of 0.0535 m/s, equal to a flow rate of 40 L/min, zero gage pressure, and species composition of 30% CO, 15% CO2, and 55% N2 by mass all exactly match the experimental conditions (Gallucci et al. 2015). The top of the reactor is modeled as a pressure outlet boundary open to the atmosphere. The reaction rate for the reduction of ilmenite is derived from the work of Shadman and Zhao (1991). The pre-exponential factor and the activation energy of the reaction are obtained from their plot of the temperature dependence of the apparent reaction rate by rearranging the Arrhenius equation to get 3 and 41.3 MJ/ kg/mol, respectively. However, in Gallucci et al.’s experiment, the packed bed consists of not only FeTiO3 but also Mn2O3, which has been shown to work as a catalyst for the ilmenite reduction reaction by Zhang et al. (2017). Preliminary simulations were run to calibrate the reaction rates to account for the catalytic effect of Mn2O3, and it was found that increasing the pre-exponential factor by ﬁve times shows greater consistency with the experimental data for the reaction including the catalyst. Hence, a pre-exponential factor of 15 and activation energy of 41.3 MJ/kg/ mol are used in the ﬁnal simulation.

11.2.2 Simulation Results Figure 11.1 compares the molar composition of the outlet gases between the simulation and the experiment of Gallucci et al. (2015). In both simulation and experiment, it takes about two minutes for the gases to travel the length of the

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Fig. 11.1 Gas mole fractions at outlet

reactor to reach the outlet. This is followed by a spike in the CO2 mol fraction suggesting a rapid generation of CO2 by the reduction of FeTiO3. Around the 15 min mark, the mole fraction of CO2 starts to gradually decline as ilmenite is consumed by the reaction, while the mole fraction of CO starts to rise since not all the fuel can be used by the diminishing ilmenite supply. The reaction is complete after about 30 min and the mole fractions of CO2 and CO level out around 14 and 23%, respectively. On all these points, the simulation results are consistent with the experimental. Since the reaction occurs during the ﬁrst 30 min of simulation, it is of interest to investigate the depletion and formation of the reactants and products during this time. Figure 11.2 shows the mass fraction distribution of FeTiO3 in the reactor over time. Initially, the FeTiO3 is ﬁxed at the middle of the reactor, held in place by the clay particles above and below. It is noted that since a phase diffusion model is applied in this case, the presence of FeTiO3 does not end abruptly at the black line marking the boundary between the metal oxide and the clay from the initial packing. As the CO enters from the bottom, FeTiO3 is consumed gradually from bottom to top. The plot at 30 min in Fig. 11.2 conﬁrms that all of the ilmenite has been consumed. Conversely, Fig. 11.3 shows how the mass fraction distribution of Fe, a product of the reduction of ilmenite, appears at the bottom of the metal oxide packing area and the front moves toward the top of the reactor. By aggregating the two plots above, it is clear what happens to the solid phase species during the reaction process is in agreement with the expectation of the experiment. Figure 11.4 shows the change in temperature at different heights in the packed bed reactor over the simulation time. At a height of 0.2 m, the packed bed material is inert clay so the temperature remains constant. On the other hand, the temperature curves at heights of 0.4, 0.6, and 0.8 m all show an initial drop due to the reduction of ilmenite, which is an endothermic reaction. This dip in temperature is greater and lasts longer higher up in the reactor but ultimately, as the reaction rates decrease, the reactor is warmed back up by the inlet gas temperature. Hence, the temperature curves at all heights show a trend to go back to 1123 K.

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Fig. 11.2 Distribution of mass fraction of ilmenite over 30 min of simulation. The x-axis gives the simulation time and the plot is colored by mass fraction of ilmenite

Fig. 11.3 Distribution of mass fraction of Fe over 30 min of simulation. The x-axis gives the simulation time and the plot is colored by mass fraction of Fe

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Fig. 11.4 Fluid phase temperature at different heights

11.3

Eulerian–Lagrangian Simulation of a Bubbling Bed with Hematite

In this section, the Eulerian–Lagrangian approach is employed to investigate in detail the solid–gas mixing and reactions in the 50 kWth bubbling bed reactor experiment of Breault et al. (2017) at the National Energy Technology Laboratory (NETL). In a bubbling bed reactor, the inlet gas velocity exceeds the minimum fluidization velocity of the particles, which causes the particles to become fluidized and move while bubbles of gas periodically form at the base of the reactor and pass through the particle bed to the top resulting in much more complicated hydrodynamics compared to a packed bed that must be captured by the model. The bubbling phenomenon greatly increases the mixing between the gas and solids and accelerates the overall reaction rate.

11.3.1 Modeling Approach and Numerical Solution Procedure In the Lagrangian particle-tracking approach, also known as the Discrete Element Method (DEM), the trajectory of each particle is resolved by performing a force balance calculation on the particle. The interphase transfer of mass, momentum, and energy are computed separately for each particle in a fluid cell and aggregated and collisions between particles and between particle and wall are resolved individually.

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227

Conservation Equations

As with the Eulerian–Eulerian model, the equations of fluid dynamics are slightly modiﬁed to account for the presence of the solids. This is done by including the porosity which is deﬁned to be equal to the volume fraction of the fluid af in the fluid cell. Furthermore, source terms are added in the equations to account for the transfer of mass, momentum, and energy between the phases. The volume-averaged continuity equation, Navier–Stokes equations, and energy equation for the CFD-DEM coupled simulation can be written as @ ðaf qf Þ þ r ðaf qf uf Þ ¼ m_ sg @t

ð11:5Þ

@ ðaf qf uf Þ þ r ðaf qf uf uf Þ ¼ af rpf r sf þ af qf g K sg ð11:6Þ @t X @ ðaf qf Ef Þ þ r af uf ðqf Ef þ pf Þ ¼ r kf rTf hj J j þ ðsf uf Þ þ Sh @t ð11:7Þ where qf , uf , pf , Ef , and Tf are the density, velocity, pressure, internal energy, and temperature of the fluid, respectively; g is the acceleration due to gravity; kf is the thermal conductivity; and hj and J j are the enthalpy and diffusion flux of species j. The source term in the momentum equation, K sg provides the momentum coupling between the solid and gas phases by accounting for the drag from the particles. The source terms in the continuity and energy equations, m_ sg and Sh , respectively, capture the mass and heat fluxes from the solid to the gas phase due to chemical reactions in the multiphase flow. For a Newtonian fluid, the shear stress tensor sf can be written as 2 sf ¼ lf ruf þ ruTf þ kf þ lf ruf I 3

ð11:8Þ

where uf is the fluid viscosity. The trajectory of each particle is computed by integrating the force balance on the particle, which can be written in the Lagrangian frame per unit particle mass as ðqf qp Þ @up ¼g þ FD ðuf up Þ þ Fcon @t qp

ð11:9Þ

where the subscript p denotes an individual particle. The terms on the right-hand side of Eq. (11.9) account for the gravitational and buoyant forces, the drag force, and an additional contact force due to particle–particle or particle–wall collisions. Forces such as the virtual mass force and pressure-gradient force can be neglected for gas–solid flows given qp far exceeds qf . The net drag coefﬁcient FD is given by

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FD ¼

18lf CD Rep qp dp2 24

ð11:10Þ

where dp is particle diameter, CD is the particle drag coefﬁcient, and Rep is the Reynolds number based on the particle diameter deﬁned as

qf dp uf up

Rep ¼ lf

ð11:11Þ

The corresponding momentum transfer from the solid phase to the gas phase is incorporated by adding the source term K sg ¼ bsg ðuf up Þ in the momentum equation for the gas phase. bsg is the solid–gas exchange coefﬁcient and is obtained by using the relation

as ag qg us ug 2:65 3 bsg ¼ CD ag 4 ds

ð11:12Þ

The drag coefﬁcient can be modeled using various empirical relations each with its advantages and drawbacks (Wen and Yu 1966; Gidaspow 1992; Syamlal and O’Brien 1989). The drag model of Wen and Yu (1966) used in this section deﬁnes CD ¼

i 24 h 1 þ 0:15ðag Res Þ0:687 ag Res

ð11:13Þ

The collision force in Eq. (11.9) is computed using the soft-sphere model, which decouples its normal and tangential components. The normal force on a particle involved in a collision is given by Fncon ¼ ðkn d þ cðu12 eÞÞe

ð11:14Þ

where d is the overlap between the particle pair involved in the collision and c is the damping coefﬁcient, a function of the particle coefﬁcient of restitution g; e is the unit vector in the direction of u12 . The work of Link previously demonstrated that for large enough values of kn , the results of the soft-sphere model are identical to those obtained using a hard-sphere model (Link 1975). The tangential collision force is calculated based on the normal force as Ftcon ¼ lFncon where the coefﬁcient of friction l is a function of the relative tangential velocity vr given as lðvr Þ ¼

lstick þ ðlstick lglide Þðvr =vglide 2Þðvr =vglide Þ if vr \vglide ð11:15Þ lglide if vr vglide

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229

Parcel Concept

For large granular systems, tracking each individual particle by the standard DEM approach is extremely computationally intensive as the total number of particles and collisions increases exponentially as the particle size becomes smaller. For instance, even in a laboratory-scale CLC system, the particle number can be around 700 billion which is far beyond the capacity of current computational resources (Peng et al. 2015). To address this issue, the parcel concept proposed by Patankar and Joseph (2001) is employed where each “parcel” represents a group of particles with the same properties (size, density, chemical composition, etc.). The total mass and volume of each parcel are obtained by summing the individual masses and volumes of each constituent particle. The parcels participate in the collisions instead of individual particles, affecting a large reduction in the computational demand, and the forces due to collisions are calculated according to the physical properties of the parcels. For a given point in the flow, the driving force on a parcel due to fluid forces Ff ;p is assumed to be the same as the sum of the fluid force acting on the group of particles it represents.

11.3.1.3

Model Parameters

The bubbling bed fuel reactor used by Breault et al. (2017) has a height of 1543 mm and an internal diameter of 203 mm. An 80 mm diameter pipe near the top of the reactor forms the gas outlet. Figure 11.5 shows the mesh used for the simulation. A parcel diameter of 2 mm is used, and the mesh size is constrained to between ﬁve and ten times the parcel diameter to ensure stability and convergence. The mesh immediately adjacent to the gas outlet is the only unstructured mesh volume. The minimum fluidization velocity of the hematite particles is reported as 0.15 m/s (Breault et al. 2017). Monazam et al. (2013) have shown that the minimum bubbling velocity is approximately 1.2–1.5 times the minimum fluidization velocity depending on the particle diameter. Hence, the inlet velocity is set at 0.3 m/s to ensure the bubbling bed conﬁguration is achieved; the inlet gas composition is 7% CH4 and 93% N2 in line with the experiment. Since the original experiment was insulated, the wall boundary condition can be set as adiabatic for simplicity without loss of accuracy. For the reduction of Fe2O3 by CH4, the pre-exponential factor and activation energy are calculated as 133.0 and 39.3 MJ/ kg/mol based on Monazam et al. (2013). The model parameters are summarized in Table 11.2.

230 Fig. 11.5 Mesh used for bubbling bed reactor simulation

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Table 11.2 Model parameters for bubbling bed reactor simulation

Internal height of reactor Internal diameter of reactor Primary phase species Discrete phase species Particle diameter Parcel diameter Oxygen carrier composition Oxygen carrier density Inlet gas composition Inlet boundary condition Outlet boundary condition Time step size

231 1543 mm 203 mm CH4, CO2, H2O, N2 Fe2O3, Fe3O4 0.238 mm 2 mm 100% hematite (Fe2O3) 5240 kg/m3 7% CH4, 93% N2 Velocity inlet Pressure outlet 0.001 s

11.3.2 Simulation Results Even using the parcel concept to slightly offset the high computing cost associated with the particle-tracking approach, the computing power available limited the coupled CFD-DEM model of the bubbling bed reactor to four seconds of simulation. The outlet mole fraction of CH4 in the experimental reactor reached 5% after an hour as shown in Fig. 11.6 (Breault et al. 2017). Looking at only the ﬁrst four seconds, the resolution of the experimental data is poor so the ﬁtted curve used to compare the simulation results, as shown in Fig. 11.7. The simulation results show excellent agreement with the ﬁtted curve. The bubbling behavior can be clearly observed in Fig. 11.8, which shows the solids volume fraction over three seconds of simulation. After an initial large bubble moves to the surface in the ﬁrst 0.5 s of simulation, the bed reaches a steady bubbling state whereby the bubbles form at the bottom of the bed and break at the upper surface. The progress of the reaction can be monitored via Figs. 11.9 and 11.10, which show the distribution of the fluid phase temperature and the mole fraction of CO2, respectively. Since the reduction of hematite is endothermic, the temperature drops at the bottom of the reactor as the reaction starts to take place. The reduced temperature front develops upward gradually until it ﬁnally reaches the gas outlet at around three seconds. The distribution of CO2 follows a similar path as the reduced temperature since they both signal the occurrence of the reduction reaction. The presence of the bubbles is also evident from the heat map in Fig. 11.6; since there is no solid in the bubbles, no new CO2 is formed in those regions resulting in a local minimum. It can also be seen that a vortex forms at the dead zone near the top of the reactor and prevents the reduced temperature and CO2 fronts from ﬁlling the entire reactor.

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Fig. 11.6 Outlet mole fraction of CH4 in the experimental bubbling bed reactor (Breault et al. 2017)

Fig. 11.7 Comparison of outlet mole fraction of CH4 after four seconds

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Fig. 11.8 Distribution of solids volume fraction over three seconds of simulation. The x-axis gives the simulation time and the plot is colored by solids volume fraction

Fig. 11.9 Distribution of fluid phase temperature over three seconds of simulation. The x-axis gives the simulation time and the plot is colored by fluid phase temperature in Kelvin

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Fig. 11.10 Distribution of mole fraction of CO2 over three seconds of simulation. The x-axis gives the simulation time and the plot is colored by mole fraction of CO2

11.4

Conclusion

Overall, the simulation of packed bed with ilmenite shows great agreement with the experimental data of Gallucci et al. (2015) in terms of outlet gas composition right from the start of the reaction to approaching steady state. The heat maps of the distributions of the solid and gas phases in the reactor provide a good explanation of the reaction process and exhibit excellent consistency between one another. It is concluded that the 2-D Eulerian two-fluid model applied this packed bed case enables the simulation to reproduce the experiment with high accuracy while at the same time accelerating the computing speed. The CFD-DEM simulation of the experimental bubbling bed fuel reactor at NETL (Breault et al. 2017) accurately predicts the start of the reaction as seen by comparing the mole fraction of CO2 at the reactor outlet. The model takes into account all multiphase phenomena and thermal conduction and heterogeneous reactions and captures the complex fluidization behavior and the reaction progress in great detail down to the particle level that is inaccessible by experimental methods. This work establishes a strong baseline for bubbling bed simulations using the Eulerian–Lagrangian approach.

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References Ahmed B, Lu H (2014) Modeling of chemical looping combustion of methane using a Ni-based oxygen carrier. Energy Fuel 28:3420–3429 Alobaid F, Ströhle J, Epple B (2013) Extended CFD/DEM model for the simulation of circulating fluidized bed. Adv Powder Technol 24(1):403–415 Arrhenius S (1896) On the influence of carbonic acid in the air upon the temperature of the ground. Philos Mag 41:237–277 Banerjee S, Agarwal RK (2015) Transient reacting flow simulation of spouted fluidized bed for coal-direct chemical looping combustion. J Thermal Sci Eng Appl 7(2):021016 Banerjee S, Agarwal RK (2016) An Eulerian approach to computational fluid dynamics simulation of a chemical-looping combustion reactor with chemical reactions. J Energy Resour Technol 138(4):04421 Breault R, Weber J, Straub D, Bayham S (2017) Computational fluid dynamics modeling of the fuel reactor in NETL’s 50 kWth chemical looping facility. J. Energy Resour Technol 139 (4):042211 Gallucci F, Hamers HP, van Zanten M, van Sint Annaland M (2015) Experimental demonstration of chemical-looping combustion of syngas in packed bed reactors with ilmenite. Chem Eng J 274:156–168 Gidaspow D (1992) Multiphase flow and fluidization. Academic Press, San Diego IPCC (2007) Climate change 2007: synthesis report. In: Pachauri RK, Reisinger A, Core Writing Team (eds) Contribution of Working Groups I, II and III to the fourth assessment report of the intergovernmental panel on climate change. IPCC, Geneva, Switzerland Link JM (1975) Development and validation of a discrete particle model of a spout-fluid bed granulator. Ph.D. dissertation, University of Twente, Enschede, The Netherlands Monazam ER, Breault RW, Siriwardane R, Richards G, Carpenter S (2013) Kinetics of the reduction of hematite (Fe2O3) by methane (CH4) during chemical looping combustion: a global mechanism. Chem Eng J 232:478–487 Nguyen TDB, Seo M, Lim Y, Song B, Kim S (2012) CFD simulation with experiments in a dual circulating fluidized bed gasiﬁer. Comput Chem Eng 36:48–56 Parker J (2014) CFD model for the simulation of chemical looping combustion. Powder Technol 265:47–53 Patankar NA, Joseph DD (2001) Modeling and numerical simulation of particulate flows by the Eulerian-Lagrangian approach. Int J Multiph Flow 27(10):1659–1684 Peng Z, Doroodchi E, Alghamdi YA, Shah K, Lao C, Moghtaderi B (2015) CFD–DEM simulation of solid circulation rate in the cold flow model of chemical looping systems. Chem Eng Res Des 95:262–280 Seo M, Nguyen TDB, Lim Y, Kim S, Park S, Song B, Kim Y (2011) Solid circulation and loop-seal characteristics of a dual circulating fluidized bed: experiments and CFD simulation. Chem Eng J 2:803–811 Shadman F, Zhao Y (1991) Production of Oxygen from Lunar Ilmenite. In: NASA Space Engineering Research Center for Utilization of Local Planetary Resources Shuai W, Guodong L, Huilin L, Juhui C, Yurong H, Jiaxing W (2011) Fluid dynamic simulation in a chemical looping combustion with two interconnected fluidized beds. Fuel Proc Technol 92:385–393 Syamlal M, O’Brien TJ (1989) Computer simulation of bubbles in a fluidized bed. AIChE Symp Ser 85:22–31 Tsuji T, Yabumoto K, Tanaka T (2008) Spontaneous structures in three-dimensional bubbling gas-fluidized bed by parallel DEM–CFD coupling simulation. Powder Technol 184 (2):132–140 Wen CY, Yu HY (1966) Mechanics of fluidization. Chem Eng Prog Symp Ser 62:100–111 Zhang X, Li H, Hou F, Yang Y, Dong H, Liu N, Wang Y, Cui L (2017) Synthesis of highly efﬁcient Mn2O3 catalysts for CO oxidation derived from Mn-MIL-100. Appl Surf Sci 411:27–33

Chapter 12

Tribological Studies of an Internal Combustion Engine Vikram Kumar, Sujeet Kumar Sinha and Avinash Kumar Agarwal

Abstract Industrial lubricants are invariably used with organo-metallic additives [such as complex sulphates and phosphates Zinc dialkyldithiophosphate (ZDDP)] for tribological performance enhancement of machines such as internal combustion (IC) engines. However, these additives are environmentally harmful and they also have damaging effects on the steel components of the engine. Hence, there is an urgent need to ﬁnd alternative solutions for enhancing the tribological performance of lubricants and components without the application of such harmful additives. Epoxy-based composites are promising tribological coatings, which can provide low friction and high wear life under dry, as well as base-oil lubricated conditions. The present chapter focuses on reviewing the mechanisms of achieving improved mechanical properties with a low coefﬁcient of friction (CoF) in typical demanding applications, such as IC engines. The applications of epoxy composite coatings can be in extreme contact conditions such as engine piston rings and bearings. The best suitable coatings, namely epoxy/graphene/SN150 with DLC and WC as the intermediate hard coatings, were applied on the piston rings of a diesel engine and experiments were performed in order to permit wear analysis of coated rings. Keywords Wear, friction

12.1

Lubrication Coating Internal combustion engine

Introduction

An industrialized nation could save up to 1.3–1.6% of their gross national proﬁt (GNP) by giving proper attention to tribological aspects for reducing the wear of machines (Jost 1966). This is because friction leads to large wastage of power and V. Kumar A. K. Agarwal (&) Department of Mechanical Engineering, Indian Institute of Technology, Kanpur 208016, Uttar Pradesh, India e-mail: [email protected] S. K. Sinha Department of Mechanical Engineering, Indian Institute of Technology, Delhi, India © Springer Nature Singapore Pte Ltd. 2019 A. K. Agarwal et al. (eds.), Advanced Engine Diagnostics, Energy, Environment, and Sustainability, https://doi.org/10.1007/978-981-13-3275-3_12

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wear involves loss of material, which eventually leads to machine failure. Tribology influences almost all industrial ﬁelds involving motion such as transport (piston– cylinder interface in IC engines, cam–follower interface, bearings, gears, tyres, brakes, etc.), manufacturing (rolling, extrusion, cutting, machining, grinding and polishing, etc.), industrial processes (materials handling, conveying, etc.), information system (magnetic data storage), human body (bone joints, teeth, skin, eye, hair, surgical devices, etc.), micro-machines (micro-electro-mechanical systems), power production and transmission (thermal, hydro and wind) and everyday life (walking and running, cleaning, writing). Tremendous effort is underway in the twenty-ﬁrst century on development of compact and fuel-efﬁcient automotive engines with reducing their environment impact. The main drawbacks of IC engines are their low mechanical and low thermal efﬁciencies. From the tribological point of view, there is a requirement to develop lubrication techniques, which can lower the friction in the engine component such as piston assembly, valvetrain and bearings, and which are also successful under enhanced loading, high speed and high temperature conditions. Frictional losses in the engine alone are in the range of 4–15% of total energy input; hence, a large amount of fuel energy is wasted in the form of friction-generated heat. Overall 15% energy is lost due to friction from these components; therefore, 10% reduction in frictional losses would eventually lead to 1.5% lower fuel consumption. Piston ring assembly is one of the main contributors to frictional losses in an engine, which is roughly 50% of the overall frictional losses (Andersson 1991). Piston rings are exposed to very rapid and large variations in the speed, load, temperature and lubricant availability (Dowson 1993).

12.1.1 Tribological Issues in IC Engines IC engines are the heart of motor vehicles and other modes of transport on ground and sea. Cylinder and piston are the main components of an IC engine, which convert chemical energy of the fuel into thermal energy and then to mechanical energy. However, not all available energy is converted into useful work. Major mechanical losses include frictional losses between the piston rings and the cylinder liner assembly (Fig. 12.1). Piston-liner and ring-liner interactions contribute to frictional loss up to 1.6% of total input fuel energy (Richardson 2000). Typical proportions energy losses in various interfaces of an IC engine are shown in Fig. 12.1. Design and wear behaviour of the engine piston rings assembly have been investigated quite extensively, and it is reported in the open literature by numerous studies that the maximum wear takes place in the compression rings. However, there is no consensus on the exact mechanism to explain the wear of the engine piston rings.

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Fig. 12.1 Typical proportions energy losses in various interfaces of an IC engine

12.1.2 Piston Rings and Surface Treatment In order to eliminate frictional losses in IC engines, it is important to ﬁnd a way of reducing the coefﬁcient of friction (CoF) and wear at the interface of piston rings and the cylinder liner. Piston rings are split rings, which ﬁt into the grove on the outer surface at the top of piston of an engine. The gap in the ring is very small, which is helpful in assembly/disassembly of piston rings and it is enough to accommodate the thermal expansion of the rings, such that both ends of the ring never meet, even in the harshest engine conditions, thus, avoiding the disastrous situation called ‘Ring collapse’. Piston rings are generally classiﬁed into two types: (1) compression rings, which act as dynamic gas seals and, (2) oil rings, which control the oil flow on to the liner surface and scrapes the excessive oil back to the oil sump. Piston rings are dynamic metal seals having linear sliding motion, which functions well in thermally and chemically intense conditions such as the ones prevailing in the combustion chamber of an IC engine. Maintaining cleanliness of lubricant from external contaminants is essential in order to suppress the three-body abrasion wear of the mating components of the engine. It prevents flow of lubricating oil to the engine combustion chamber and provides uniform oil ﬁlm on the wall of the cylinder liner. Thus, the counter surface and rings operate under the same tribological laws as any sliding couples having the requirements of lower friction and wear and long service life under extreme conditions. Piston rings ideally should offer low friction, low wear for any type of cylinder liner (plain or textured), long service life, good sealing capability, and good resistance to mechano-thermal, chemical attacks and hot erosion.

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The piston ring materials are chosen depending upon the operating conditions (Federal 1998). The material should be highly elastic with high strength, high thermal resistance and high corrosion resistance. Surface modiﬁcations and treatments are required for better performance of rings with cylinder liner under lubricated conditions, see Table 12.1. Coatings are applied on the ring surface for obtaining superior performance of the rings. One of the main tasks of the rings is to transfer the heat away from the cylinder; hence, the material used for them should be thermally conducting. Grey cast iron is very good material from tribological aspects since graphite present in it acts as self-lubricant under dry conditions such as in the case of oil starvation. Rings are coated for wide variety of applications. Examples of such coatings for different applications include chrome plating, molybdenum, metal composites, metal-ceramic composites or ceramic composites using thermal (plasma) coating system (PVD or CVD method) (Mollenhauer 1997). Multi-layer coatings (Ti–TiN) have been deposited on ring base metal, which improves their tribological performance. Surface coatings or treatment of piston rings is carried out by employing phosphorous, nitrides, Ferro-oxides and tin (Rastegar and Richardson 1997; Priest and Taylor 2000). The main purpose of these coatings is to reduce friction and enhance wear resistance of piston ring and cylinder liner.

12.1.3 Wear and Friction Involving Piston Rings Cylinder and piston rings undergo variety of ﬁction mechanisms during an engine cycle. This difference is because of the sliding speed which changes from zero to a maximum value and back to zero in every stroke of the engine. Normal load also varies during expansion and compression strokes of a cycle. Load on the piston rings is exerted by the cylinder gas pressure and the ring pretension. Total tension in the piston ring pack essentially determines the frictional losses (Takiguchi et al. 1996). The contact between piston rings and cylinder liner can be explained by Stribeck curve, which is explained in the next section (Tayler 1998). The friction

Table 12.1 Typical composition of material used for piston rings

Element

Percentage (%)

Iron (Fe) Carbon (C) Silica (Si) Manganese (Mn) Phosphorus (P) Sulphur (S) Chromium (Cr) Vanadium (V) Copper (Cu)

90.2–93.4 2.8–3.8 2–3 0.5–0.9 0.3–0.7 0.15 (max) 0.15–0.6 0.2 (max) 0.2–0.8

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mechanism between the piston rings and liner interface is in boundary and mixed lubrication regimes in the vicinity of dead centres (TDC and BDC), whereas at mid-stroke, because of highest ring velocity, hydrodynamic lubrication mechanism is applicable (Wakuri et al. 1995; Arcoumanis et al. 1997; Durga et al. 1998; Coy 1998). During the boundary and mixed lubrication regimes, coefﬁcient of friction (CoF) is quite high; hence, it is necessary that friction is reduced in order to reduce the wear of piston ring and cylinder liner. Lubricating oil contains multifunctional additives such as ZDDP, which reacts with fresh steel surfaces to form layer of protective compounds. This forms a boundary layer. Surface roughness also plays an important role in determining the coefﬁcient of friction (CoF). Wear of piston rings is due to abrasive wear of cylinder liner and rings. The wear of piston rings and cylinder liner increases due to three-body abrasive wear caused by the presence of small amount of solid particles in the lubricating oil, which are added by component wear, ambient air dust particles and contaminants from the oil sump or combustion chamber. Strong adhesive forces between the piston rings and liner occur due to inadequate lubrication, leading to high frictional losses and metal scufﬁng, resulting in high wear. Apart from sliding wear, piston ring surfaces degrade due to blow-by of hot gases from the high temperature combustion chamber, which carry soot along with them.

12.1.4 Lubrication Lubrication mechanism is very important to explain the friction and wear properties of any machine component. It is explained by Stribeck curve, which presents a plot of the coefﬁcient of friction (CoF) versus the Sommerfeld number (Velocity Viscosity/Pressure). Figure 12.2 shows the Stribeck curve. The curve is divided into mainly three lubrication regimes. These lubrication regimes are: boundary lubrication (BL), mixed lubrication (ML) and hydrodynamic lubrication (HL) regimes. The fourth lubrication regime is the elasto-hydrodynamic lubrication (EHL) regime, which is often considered as part of HL (Dowson 1998). As shown in the Stribeck curve, the coefﬁcient of friction (CoF) is very high when the sliding speed is low or the normal load is high and this condition exists at low Sommerfeld number. Increasing sliding speed or lower normal load increases the Sommerfeld number and reduces the coefﬁcient of friction (CoF) drastically. In the HL regime, there is complete separation of the two surfaces by the fluid ﬁlm. It is important that the fluid ﬁlm thickness is maintained to a value greater than the roughness of the mating surfaces for effective HL. Very low coefﬁcient of friction (CoF) of the order of *10−4 to 10−3 can be achieved in this regime. In the ML regime, the fluid ﬁlm thickness is nearly equal to the surface roughness. In this lubrication regime, the asperities are just in contact or partially separated by very thin ﬁlm. The tribological characterization is, therefore, affected by both, the fluid properties and the surface roughness properties.

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Fig. 12.2 Stribeck curve

The BL regime comes into picture, when the lubricating oil ﬁlm thickness is zero due to rupture of oil ﬁlm and direct surface to surface contact occurs. In this regime, the coefﬁcient of friction (CoF) varies between 0.1 and *1. The tribological properties in the BL regime are affected by asperity–asperity contact, lubricating oil properties, contact between the frictional surfaces, and the interaction between friction surfaces and lubricating oil. In this regime, wear of both surfaces is quite high. However, suitable additives in the oil can reduce wear by forming effective and low friction boundary layers on the steel surfaces. Some of the hazardous substances contaminate the environment such as lubricant additives containing sulphur, chlorine, phosphorous, etc. These substances generally form due to polymerization of several organic compounds sticking to the engine components and they increase the viscosity of lubricant. Further, polymerization causes high viscous drag on the surface, which increases the friction and wear of components. Hence, there is a requirement of some other strategy of lubrication, which can replace or reduce the use of current set of hazardous additives (Bartz 1993). Therefore, there is need of ﬁnd alternatives to these harmful lubricating oil additives.

12.1.5 Tribological Coatings Apart from liquid lubrication of machine parts, they can also be coated by appropriate layer of materials such as solid lubricants to reduce the friction and hence, the

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wear. Coating materials may be metallic, non-metallic or polymers. Some protective coatings are diamond-like carbon (DLC) (Gahlin et al. 2001), titanium aluminium nitride (TiAlN), chromium aluminium nitride (CrAlN), zirconium nitride (ZrN), zirconium carbide (ZrC), tungsten carbide/carbon (WC/C) and tungsten carbon–hydrogen (W–C:H) (Gold and Loos 2002). In some cases, these coatings may react with lubricating oil additives to form triboﬁlms. For example, during the sliding test, triboﬁlm of MoS2 is formed when additives are used in lubricant (Morina and Neville 2007). However, the wear of surfaces cannot be completely prevented. In addition, there is a disadvantage of poor adhesion of coatings with the substrate. The other causes of coating failure include thermal stresses at the interface of coatings and substrate, incompatibility with the lubricant and sensitivity to the environment (Neville et al. 2007). Hard coatings tend to be brittle, which can lead to delamination if the coating/substrate adhesion is poor, or if there is mismatch in coefﬁcient of thermal expansion or if there is presence of hard wear debris. Polymers have self-lubricating properties. They are used for thin ﬁlm coatings for bearing applications and as composite ﬁllers (Lancaster 1984; Loomis 1985; Gresham 1994; Jamison 1994). In the form of ﬁllers and dispersants, polymers are used in coatings on surfaces for reduction of friction and wear rate (Booser 1997). Major advantages of polymers include low cost and weight, easy to coat on a variety of shapes, superior lubricity under dry and lubricated conditions, lower wear and corrosion resistance and tolerance to wide range of operating environmental conditions. Select polymers have excellent tribological properties and can also be used as solid lubricants. Polymers mostly used as lubricating material are polytetrafluoroethylene (PTFE). PTFE has the lowest coefﬁcient of friction (CoF) amongst all polymers and is used as solid lubricant for coatings and bulk. Polymer-based coatings have been applied on metal surfaces, which work well in both dry and base-oil lubricated conditions. Machine components can be protected by such tribological coatings (Podgoric et al. 2009). A suitable polymer coating provides low coefﬁcient of friction (CoF), low wear rate and very good adhesion properties with many substrates (Samad and Sinha 2010). There are many polymers such as epoxy, SU-8 (an epoxy-based polymer), PEEK, UHMWPE, PTFE, etc., which have been used as coatings to improve the tribological performance of solid surfaces (Singh et al. 2011; Loy and Sinha 2012). Thin polymeric coatings can work like an effective boundary lubricant, thus, protecting the steel surfaces, requiring very little liquid lubricant (Samad and Sinha 2010). Currently, polymer coatings have been used to protect the mating surfaces due to advantages such as cost-effectiveness, easy to deposit, very good bonding with many substrates, low friction and highly corrosion resistance. The polymer coatings also act as self-lubricating material, which makes them more useful for coating on metallic substrate such as steel. Bulk of engineering machine elements are made of steel. Many of these polymers can be easily coated on to a substrate in composite form; hence, their tribological properties can be tailored for the speciﬁc requirements. A combination of hard and soft coating on steel component, such as piston ring, has also been investigated, which provides low friction and wear durability to the

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piston rings. Thus, the base-oil lubricant does not require any additives/fewer additives to achieve low friction in all lubrication regimes, without affecting the durability. Polymer coatings are chosen as the soft layer because of polymers: • • • • •

require simple coating method, cost is low and easy to fabricate in any shape, tribological properties are excellent for select polymers and their composites, wear resistance is high and coefﬁcient of friction (CoF) is low, and they have good toughness property.

12.2

Wear and Friction Studies

12.2.1 Materials A set of piston rings of a single cylinder constant speed diesel engine (DM-10; Kirloskar Oil Engines, Pune, India) with an outer diameter of 102 mm, and width of 3 mm and D2 steel cylindrical shaft (Kumar et al. 2015; 2017) were PVD coated with two hard materials (DLC and WC). Each ring-set consisted of four rings. Top two rings were the compression rings. The third ring was the oil control ring, and the last ring was an oil ring (Fig. 12.3). Polymer composite coatings were applied over the surfaces of hard-coated piston rings and the cylindrical shaft. Lubricating oil in the engine was SAE 5W30. Base oil SN150 of Group-I with viscosity of 24.25 cP (at 40 °C) was used as liquid lubricant, and graphene nanoparticles of particle sizes

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