Sustainable Engineering: Process Intensification, Energy Analysis, and Artificial Intelligence [1 ed.] 1032042400, 9781032042404

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Sustainable Engineering Process Intensification, Energy Analysis, Artificial Intelligence

Yaşar Demirel Chemical and Biomolecular Engineering Department University of Nebraska Lincoln Lincoln, Nebraska, USA

Marc A. Rosen Faculty of Engineering and Applied Science Ontario Tech University Oshawa, Ontario, Canada

A SCIENCE PUBLISHERS BOOK

Cover image provided by the first author, Yaşar Demirel.

First edition published 2023 by CRC Press 6000 Broken Sound Parkway NW, Suite 300, Boca Raton, FL 33487-2742 and by CRC Press 4 Park Square, Milton Park, Abingdon, Oxon, OX14 4RN © 2023 Taylor & Francis Group, LLC CRC Press is an imprint of Taylor & Francis Group, LLC Reasonable efforts have been made to publish reliable data and information, but the author and publisher cannot assume responsibility for the validity of all materials or the consequences of their use. The authors and publishers have attempted to trace the copyright holders of all material reproduced in this publication and apologize to copyright holders if permission to publish in this form has not been obtained. If any copyright material has not been acknowledged please write and let us know so we may rectify in any future reprint. Except as permitted under U.S. Copyright Law, no part of this book may be reprinted, reproduced, transmitted, or utilized in any form by any electronic, mechanical, or other means, now known or hereafter invented, including photocopying, microfilming, and recording, or in any information storage or retrieval system, without written permission from the publishers. For permission to photocopy or use material electronically from this work, access www.copyright.com or contact the Copyright Clearance Center, Inc. (CCC), 222 Rosewood Drive, Danvers, MA 01923, 978-750-8400. For works that are not available on CCC please contact [email protected] Trademark notice: Product or corporate names may be trademarks or registered trademarks and are used only for identification and explanation without intent to infringe. Library of Congress Cataloging‑in‑Publication Data (applied for)

ISBN: 978-1-032-04240-4 (hbk) ISBN: 978-1-032-04250-3 (pbk) ISBN: 978-1-003-19112-4 (ebk) DOI: 10.1201/9781003191124 Typeset in Times New Roman by Radiant Productions

Dedication To Zuhal, Selçuk and Can Yaşar Demirel To my family, friends and colleagues, for their support and inspiration. Marc A. Rosen

Preface By providing a practical and useful resource on how to combine engineering with process intensification, energy analysis, and artificial intelligence to achieve sustainable engineering, Sustainable Engineering: Process Intensification, Energy Analysis, and Artificial Intelligence is particularly useful for senior and graduate students, engineers, scientists, and researchers. This book offers a new approach to sustainable engineering at the interface of three key dimensions: process intensification, energy analysis, and artificial intelligence. Process intensification can be achieved through directed equipment, methods, and design. Energy analysis can lead to reduction of the cost and improve the use of material, energy, and water. Artificial intelligence can deliver flexible and profitable engineering outcomes through with the help of data processing and automation. Sustainable engineering through process intensification, energy analysis, artificial intelligence can reduce costs of production, depletion of natural resources, enhance energy intensity, and reduce waste. Sustainable engineering can also enhance product quality, societal well-being, water quality, and economics, with enhanced resilience, flexibility, agility, and safety. Sustainable Engineering: Process Intensification, Energy Analysis, and Artificial Intelligence approaches guide the reader through nine chapters in a logical sequence. Chapter 1 on Sustainable Engineering provides an introduction and overview of sustainable engineering, which forms the focus of this book, through coverage of sustainability, natural earth cycles, climate change and global warming, sustainable engineering, sustainable energy management, and sustainable development and sustainable development goals. Chapter 2 on Environmental Sustainability covers environmental sustainability and its context, natural earth cycles and greenhouse gases, including carbon tracking. This chapter is very important given the focus on environmental issues in most if not all definitions of sustainability. Chapter 3 on Economic Sustainability aims to enhance understanding of economic sustainability, the circular economy and the bioeconomy through extensive coverage of these topics as well as the green economy. This chapter is necessary since sustainability goes beyond environmental issues to cover economics. Chapter 4 on Societal Sustainability covers such societal sustainability topics as societal well-being, social responsibility, advancing social sustainability and the human development index. This chapter is required since sustainability goes beyond environmental and economic topics to include social factors, even if these are sometimes overlooked. Chapter 5 on Sustainability Metrics discusses how to measure sustainability by using sustainability metrics and indices. This chapter is necessary since sustainability efforts, to be successful, most be measurable and monitorable. Chapter 6 on Process Intensification provides an understanding of and ability to apply significant techniques for process intensification, describes and illustrates process intensification in the industrial and energy sectors, and explains the relation of process intensification and sustainability. By focusing on process intensification, this chapter provides information on an important approach to engineering sustainability.

vi  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence Chapter 7 on Energy Analysis examines such technical topics as energy production and conversion, energy conservation and efficiency, energy storage, energy analysis and advanced tools like exergy analysis. The pervasiveness of energy in our lives makes this chapter essential. Chapter 8 on Artificial Intelligence provides an understanding of AI in general, through coverage of such topics as industrial Artificial Intelligence, information System Engineering, digital Industry Platforms and cybersecurity. By focusing on AI, this chapter ensures modern and advanced approaches are addressed. Chapter 9 on Workforce in Sustainable Engineering covers such topics as key competencies and skills in sustainability, as well as education and training for sustainable engineering, including such topics sustainable resources, sustainable processes, increased efficiency, reduced environmental and ecological impact among others. The relevance of this chapter may seem obvious, but we feel it important to emphasize the need for a well educated and trained workforce in the area of sustainable engineering. The coverage in this book, taken in entirety, emphasizes the need for balancing economics, environment, and societal elements of a system by implementing process intensification, energy analysis, and artificial intelligence to reduce production costs, improve the use of material and energy, enhance product quality, and improve process optimization and safety for sustainable engineering. The book discusses many techniques for process intensification and energy analysis, including engineering optimization, energy integration, carbon tracking, global warming potential, green engineering, pinch analysis, exergy analysis, feasibility analysis, life cycle assessment, circular economy, and bioeconomy. These techniques are discussed thoroughly and presented with case studies and examples. This book also discusses the artificial intelligence that can anticipate future conditions and act accordingly toward sustainable engineering. The book presents how to combine engineering fundamentals together with process intensification, energy analysis, and artificial intelligence toward sustainable engineering. All through this edition, the work of many people who contributed to “Sustainable Engineering” has been visited and revisited. We acknowledge and greatly appreciate the contributions of all these people and apologize for not being able to cite so many valuable published work on sustainable engineering. We particularly thank past and present colleagues and students for thoughtful discussions and debates, which helped influence aspects of this book. That assistance helped in enhancing the book’s coverage and content and in making it more useful as a textbook and resource on sustainable engineering through process intensification, energy analysis, artificial intelligence. We are also thankful to colleagues including Professor Turkan Kopac, Professor Mehmet Kopac, Kaylee Alles, and Boanerges Bamaca, students, and reviewers who have contributed with their comments and suggestions over the past years for the evolution of this edition. We are also thankful to companies and organizations that presented materials that helped inform the preparation of this book. The materials allowed for coverage of recent developments and provided noteworthy elements of practicality as well as real-world and industrial relevance. We hope that this book will inspire researchers, scientist, students, policy makers, stakeholders, and business investors toward achieving sustainable engineering through process intensification, energy analysis, and artificial intelligence. 2022

Yaşar Demirel Marc A. Rosen

Acknowledgments We acknowledge and greatly appreciate the contributions of all these people and apologize for not being able to cite so many valuable published work on sustainable engineering. We particularly thank past and present colleagues and students for thoughtful discussions and debates, which helped influence aspects of this book. That assistance helped in enhancing the book’s coverage and content and in making it more useful as a textbook and resource on sustainable engineering through process intensification, energy analysis, artificial intelligence. We are also thankful to colleagues including Professor Turkan Kopac, Professor Mehmet Kopac, Kaylee Alles, and Boanerges Bamaca, students, and reviewers who have contributed with their comments and suggestions over the past years for the evolution of this edition. We are also thankful to companies and organizations that presented materials that helped inform the preparation of this book.

Contents Dedication Preface Acknowledgments 1. Sustainable Engineering Introduction and Objectives 1.1 Sustainability 1.1.1 Sustainability Dimensions 1.1.2 Sustainability Science 1.1.3 Sustainability Strategy Equity, diversity, and inclusion Environmental, social and governance Socially responsible investing Impact investing 1.1.4 Environmental Impact Formulation 1.2 Resilience Sustainability and resilience 1.2.1 Stability, Robustness, and Resilience 1.3 Agility 1.4 Integrated Sustainability, Resilience, and Agility Management Stability, resilience, and agility Stability and resilience Building the strategically resilient and agile organization 1.5 Why Sustainability Matters? 1.6 Sustainable Engineering Sustainable engineering design and performance 1.6.1 Sustainable Engineering Principles 1.6.2 Sustainable Engineering Techniques 1.6.3 Environmental Sustainability Environmental security 1.6.4 Economic Sustainability Aligning with stakeholder’s priorities 1.6.5 Societal Sustainability Carrying capacity of Earth Food-energy-water nexus 1.6.6 Process Intensification and Sustainability 1.6.7 Energy Analysis and Sustainability Thermal efficiency and sustainability Thermodynamic optimum Efficient resource utilization

iii v vii 1 1 1 2 3 4 4 5 5 5 5 6 6 7 8 8 9 10 10 11 11 12 12 13 14 14 15 15 15 15 15 16 17 18 18 19

x  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence 1.6.8 Artificial Intelligence First-principles models Predictive analytics Remote operation Sustainability challenges 1.6.9 Views Regarding Sustainable Engineering 1.7 Sustainable Engineering: Energy Analysis, Artificial Intelligence, and Process Intensification 1.8 United Nation Sustainable Development Goals Bioeconomy strategy Sustainable development Resilient development Summary Nomenclature Problems Research Projects References 2. Environmental Sustainability Introduction and Objectives 2.1 Environmental Sustainability and its Context 2.2 Natural Earth Cycles Carbon cycle Nitrogen cycle Nitrogen and sulfur compounds 2.3 Greenhouse Gases Impact of greenhouse gas emissions 2.3.1 Carbon Tracking 2.4 Ecological Footprint 2.4.1 Climate Change Global warming and climate change Paleoclimatic data 2.4.2 Environmental Burden 2.4.3 Global Warming Potential 2.4.4 Acidification Atmospheric impact Atmospheric acidification Aquatic impact Aquatic acidification 2.4.5 Ozone Formation and Destruction Stratospheric ozone depletion 2.4.6 Smog Formation Photochemical ozone formation 2.4.7 Human Health 2.4.8 Toxicity Impact to land 2.4.9 Eutrophication Aquatic oxygen demand Stoichiometric oxygen demand (StOD) 2.4.10 Habitat Destruction

19 19 19 20 20 20 22 24 24 24 25 25 26 26 27 27 30 30 30 31 31 31 32 32 32 33 34 35 35 36 36 37 38 38 38 38 38 38 39 39 39 43 44 45 45 45 45 46

Contents xi

2.4.11 Resource Depletion 2.4.12 Particulate Matter 2.5 Carbon Capture Carbon capture and utilization Global warming potential Solvent technology for carbon capture 2.6 Decarbonization 2.7 Carbon Utilization 2.8 Environmental Cost of Carbon Emissions 2.8.1 Environmental Impact Assessment Summary Nomenclature Problems Research Projects References 3. Economic Sustainability Introduction and Objectives 3.1 Economic Sustainability Socioeconomic goals 3.1.1 Energy Return on Investment 3.1.2 Renewable Energy Cost 3.1.3 Levelized Cost of Electricity Value-adjusted levelized cost of electricity 3.2 Circular Economy 3.2.1 Circular Economy and Sustainability Equitable society and sustainability Customer feedback Energy transition 3.3 Bioeconomy 3.3.1 Important Aspects of Bioeconomy Bioeconomy and bioproducts 3.3.2 Waste Management Converting waste lignin into adipic acid Internal barriers to a bioeconomy 3.3.3 Economic Assessment of Biofuels Energy efficiency Bioenergy production 3.3.4 Bio Break Model Willingness to pay Willingness to accept Bio break model for algal feedstock 3.3.5 Bioeconomy and Circular Economy 3.4 Green Economy Natural capital Green economy: hydrogen and ammonia Green hydrogen 3.5 Hydrogen, Ammonia and Methanol Economy Hydrogen economy Green ammonia Methanol economy

46 48 49 49 49 50 50 51 52 52 52 53 54 54 55 56 56 56 57 58 59 59 60 61 62 62 63 65 65 66 66 66 67 69 69 69 70 70 71 71 72 72 73 74 74 74 75 75 75 76

xii  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence 3.5.1 Methanol and the Environment 3.5.2 Methanol Economy versus Hydrogen Economy 3.6 Economic Cost of GHG Emissions 3.6.1 Index of Ecological Cost 3.6.2 Ecological Cost 3.7 Thermoeconomics Exergoeconomics 3.7.1 Technoeconomic Analysis Stochastic analysis of economic performance Risk assessment Sensitivity analysis Summary Nomenclature Problems Research Projects References 4. Societal Sustainability Introduction and Objectives 4.1 Societal Sustainability 4.1.1 Societal Well-Being Human health 4.1.2 Social Responsibility 4.1.3 Advancing Social Sustainability 4.1.4 Human Development Index 4.2 Social Investment Equity, diversity, and inclusion Sustainability and poverty 4.2.1 Energy Return on Investment Society 4.3 Social Cost of Carbon Emissions Policy evolution of the SCC 4.3.1 Health Effect of Biofuels Summary Nomenclature Problems Research Projects References 5. Sustainability Metrics Introduction and Objectives 5.1 Sustainability Impact and Indicators Sustainability assessment 5.1.1 Ecological Indicators 5.1.2 Sociological Indicators 5.1.3 Technological Indicators 5.2 Sustainability Indices 5.3 Sustainability Metrics Measuring sustainability 5.4 Human Development Index 5.5 Sustainability Assessment Tools EcoCalculator International frameworks and assessment tools

76 76 76 77 77 77 78 78 79 79 79 79 80 80 81 81 84 84 84 87 87 88 89 90 90 91 91 91 91 93 93 94 94 95 95 95 97 97 97 97 99 100 101 102 104 105 106 107 107 107

Contents  xiii

5.5.1 Energy Assessments Renewable energy technologies Green energy 5.6 Case Study: Sustainability Assessment of Hydrogen Production from Solid Fuels Summary Nomenclature Problems Research Projects References 6. Process Intensification Introduction and Objectives 6.1 Process Intensification 6.1.1 Process Intensification Fundamentals Process intensification vision Key approaches to achieve the PI vision Expected outcomes for the PI vision Process intensification aspects Outstanding challenges in PI 6.1.2 Process Intensification Principles 6.1.3 Process Intensification Domains 6.1.4 Process Intensification Strategies 6.1.5 Process Intensification Techniques 6.1.6 Implementation of Process Intensification 6.1.7 Operability of Intensified Processes Process flexibility analysis Multi-level reactor design Scheduling and control 6.1.8 Intensification Factor 6.2 Intensification Methods and Modeling Vision Key approaches Product development Modeling and simulation 6.2.1 Thermodynamic Method Rate-based separation Flash drum simulation and Henry’s law 6.2.2 Thermodynamic Analysis Thermodynamic cost Distillation columns Column targeting tools Exergy loss profiles Equipartition principle Heat exchanger operation modes 6.2.3 Industry I4.0 6.2.4 Six-Sigma Analysis Probability density function and defects Capacity lost in manufacturing due to defects Procedures to improve performance Design team and six sigma Definitive screening design

108 109 109 110 112 112 113 113 114 116 116 116 117 118 118 118 119 119 119 120 121 122 123 124 124 124 124 125 126 127 127 127 127 128 128 128 129 130 130 131 131 132 132 133 133 133 134 134 136 136

xiv  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence

6.3

6.4

6.5

6.6 6.7

6.8

Lean six sigma analysis and Industry 4.0 Six sigma and sustainable manufacturing Intensification in Units 6.3.1 Advanced Separation Systems Distillation columns Reactive distillation column Petlyuk column Reactive Petlyuk columns Intensified carbon capture Adsorptive separations 6.3.2 Advanced Reactors Structured catalytic reactors Microreactors Oscillatory flow reactors Reverse flow reactors 6.3.3 Reactor and Separators Membrane-assisted reactive separation 6.3.4 HiGee Technology 6.3.5 Crystallization Equipment Intensification in Plants 6.4.1 Modular Manufacturing Streamlining design with a modular approach 6.4.2 Heat Integration 6.4.3 Optimization 6.4.4 Process Synthesis Municipal wastewater treatment Integration of wastewater treatment with algal cultivation Sewage sludge-to-hydrogen Degradation of microplastics in wastewater Decomposing plastics to monomers Biochemical Processes and Bioproducts 6.5.1 Biopharmaceutical Processes 6.5.2 Biotechnology 6.5.3 Bioproducts Chemical Processes 6.6.1 Fischer-Tropsch Synthesis 6.6.2 Methanol, Ammonia and Hydrogen Production Thermochemical Processes with Chemical Looping Systems 6.7.1 Thermochemical Processes 6.7.2 Chemical Looping Systems 6.7.3 Hydrothermal Conversion Carbon dioxide to formic acid Formic acid to methanol Combination of chemical looping with hydrothermal conversion Capturing and using CO2 as feedstock with chemical looping and hydrothermal technologies Chemical looping gasification with Fischer−Tropsch synthesis Chemical looping gasification for ammonia production Green Engineering Processes Ascribing economic value to natural processes

136 137 137 138 138 139 140 141 141 141 142 142 142 142 143 143 143 144 144 145 145 147 147 148 148 149 149 150 150 150 151 152 152 153 153 155 155 157 157 158 160 161 161 161 162 163 163 163 164

Contents xv

Understanding biodiversity net gain Decarbonization 6.8.1 Biorefinery Biomass conversion processes Microwave processing Supercritical fluid extraction Green steam crackers 6.8.2 Fermentation Ethanol fermentation Lactic acid fermentation 6.8.3 Anaerobic Digestion Natural gas pyrolysis Propylene carbonate and polypropylene carbonate production Carboxylation of glycerol production Ionic liquids Production of caprolactam Separation of xylene isomers Switchable solvents Oxidative dehydrogenation of propane Oxidative dehydrogenation of ethane Better methane reforming 6.9 Energy Technology and Management 6.9.1 Energy Technology Cement production Ammonia as fuel Advanced biofuels Carbon capture and utilization systems simulator Precision alignment to reduce energy costs Cost estimation and risk assessment 6.9.2 Energy Management Internet of things (IoT)-based energy management 6.10 Process Safety Process hazards Process safety in industrial and manufacturing sectors Hydrogen fires Hydrogen safety Improved safety 6.11 Process Intensification and Sustainable Engineering Energy and industrial/manufacturing sectors Some barriers to sustainability Safety aspect Digitalization and I4.0 Process intensification in carbon dioxide capture and conversion Plasma conversion of carbon dioxide Summary Nomenclature Problems Research Projects References

164 165 165 167 168 168 168 169 169 169 169 170 171 171 171 172 172 172 172 172 173 173 174 174 174 174 175 176 176 176 176 177 177 177 178 178 179 179 181 181 182 182 182 183 183 184 186 187 187

xvi  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence 7. Energy Analysis Introduction and Objectives 7.1 Energy Production 7.1.1 Nonrenewable Energy Production Steam power generation Possible improvements in steam-power generation Brayton cycle Heat recovery steam generators Nuclear energy Blue hydrogen 7.1.2 Renewable Energy Production Hydropower Wind energy Solar energy Hydrogen energy Green energy Geothermal energy Bio energy Biogas Biomass-to-liquid fuels Fischer-Tropsch diesel Ethanol production Biodiesel production Green diesel production Butanol Methanol Dimethyl ether Energy from solid waste 7.1.3 Implications of Energy Use 7.1.4 Energy Production Assessments Issues with biofuel production Possible improvements in renewable energy Bioenergy systems assessment 7.2 Energy Conservation Energy conservation of compression or expansion work Example for energy conservation with a throttle valve and a turbine Energy conservation in the manufacturing sector 7.3 Energy Conversion Green building Rebound effect Fuel cells 7.3.1 Energy Efficiency Energy efficiencies of biofuels 7.3.2 Energy Efficiency Standards Comparison of energy-efficiency standards 7.4 Energy Storage 7.4.1 Energy Storage Types Thermal energy storage Thermochemical energy storage

194 194 194 195 195 196 196 197 197 197 198 198 199 199 200 200 200 201 201 202 202 202 203 204 205 205 206 206 207 208 209 209 211 211 212 213 213 214 216 216 216 217 217 218 219 219 219 219 220

Contents xvii

Flywheel energy storage Compressed air energy storage Pumped energy storage Magnetic energy storage Chemical and hydrogen energy storage 7.4.2 Energy Storage Applications 7.5 Energy Economics 7.6 Energy Analysis Minimum approach temperature 7.6.1 Energy Targets Utility load allocation method Pinch analysis Heat exchanger network system Energy targets for toluene hydrodealkylation process 7.6.2 Energy Integration Heat integration in a biodiesel plant 7.6.3 Exergy Analysis Comparison of electrical heating technology based on exergy: resistance vs heat pump Column targeting tool with exergy analysis Thermodynamic efficiency Retrofitting by column targeting tool in a biodiesel plant Crude oil refinery operation Column grand composite curve Exergy loss profiles Heat exchanger network system Biorefinery and petrochemical industry integration 7.7 Food, Energy, and Water System Water-energy nexus Water-food systems Energy-food systems 7.8 Life Cycle Analysis of Energy Systems 7.9 Energy Analysis and Sustainability 7.9.1 Energy Analysis and Process Intensification Energy transition Electrification Decarbonization Intensified process extracting sugars 7.9.2 Energy Analysis and Artificial Intelligence 7.10 Case Studies Natural gas upgrading Blue ammonia transition Hydrogen versus ammonia Power-to-X Summary Nomenclature Problems Research Projects References

220 220 220 220 221 221 222 222 222 223 223 223 224 225 229 229 230 235 237 238 238 240 241 242 243 244 244 245 246 246 247 249 250 251 252 252 254 254 255 255 255 256 256 256 257 258 259 260

xviii  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence 8. Artificial Intelligence Introduction and Objectives 8.1 Industrial Artificial Intelligence 8.1.1 The Constellation of Artificial Intelligence Machine learning 8.1.2 Artificial Intelligence and Analytics 8.1.3 Opportunities and Challenges of Digitization 8.1.4 The Industrial AI Readiness Checklist 8.1.5 Execution of Industrial AI Black box models 8.1.6 Agility and Digitalization 8.1.7 Challenges with Industrial Artificial Intelligence Ethical and social issues 8.2 Information System Engineering Performance management 8.2.1 Information Quality 8.2.2 Information and Data Management Information processing and sustainability A self-correct system Bifurcations for information processing Biocomputing Information processing in living and non-living systems Machine learning for molecular thermodynamics 8.3 Digital Industry Platforms Maintaining sustainability efforts 8.3.1 Smart Sensors Digital transformation Product development in the digital age Collaborative communication with electronic lab notebooks Efficient data analysis workflows Maintenance and AI Prescriptive analytics 8.3.2 Hybrid Artificial Intelligence Systems Hybrid models Sustainability indicators via industrial internet of things 8.3.3 Digital Twins State of the art of digital twin development Types of digital twin models Plant digital twin process models Operational excellence and integrity of digital twin Digital twins and business model Digital twin models in refining Streamlining with a modular approach Cost-competitive manufacturing Digitalization and knowledge creation Enterprise reliability Asset life cycle Asset optimization and digital twins Stable operations and reliability Operational digital twin models Scale of digital twins

265 265 265 266 267 268 268 269 270 270 272 272 273 273 274 274 275 275 275 276 276 277 277 278 278 279 279 279 280 280 280 281 281 282 284 284 286 287 287 287 287 287 288 288 290 290 291 291 292 293 294

Contents xix

8.4

8.5

8.6

8.3.4 Industry 4.0 Green lean six sigma and I4.0 8.3.5 Industrial Internet of Things Impact of IoT on artificial intelligence Data integration and mobility 8.3.6 Multi-Dimensional Optimization Plant-wide modeling and optimization 8.3.7 Event Agent Event agent of a compressor Performance engineering 8.3.8 Self-Optimizing Plants Cybersecurity Cybersecurity domains 8.4.1 Cyber Threats Key cybersecurity technologies and best practices 8.4.2 Cybersecurity Response Data integrity 8.4.3 Cybersecurity Training Artificial Intelligence and Process Intensification 8.5.1 Industrial PI4.0 Implementation of PI4.0 8.5.2 Challenges of Application of Industry PI4.0 Information processing in living system and process intensification Potential machine learning methods for PI4.0 Cases of process intensification with PI4.0 Self-driving laboratory for accelerated discovery Artificial Intelligence and Sustainable Engineering Triple bottom line Artificial intelligence and environmental challenges 8.6.1 Sustainability from Knowledge Creation Building on the existing technology foundation 8.6.2 Data sources Data quality Maturity assessments 8.6.3 Turning Data into Action 8.6.4 Workflow Process 8.6.5 Sustainability Focus Aligning environmental sustainability with artificial intelligence Artificial intelligence and societal outcomes Artificial intelligence and economic outcomes Artificial intelligence and environmental outcomes Research on artificial intelligence in sustainable development Domains of artificial intelligence for environmental sustainability Challenges in applying AI for environmental sustainability IoT and energy management Better design Risk priority 8.6.6 Sustainable Artificial Intelligence Artificial intelligence and development of materials

294 295 296 296 297 298 298 299 299 299 300 300 301 301 302 303 304 304 304 306 306 307 307 308 308 308 308 309 310 312 312 313 314 314 315 315 315 318 319 320 320 320 321 321 322 322 322 323 324

xx  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence 8.6.7 Process Intensification and Artificial Intelligence Applications Safety and human factor aspects Indicator evaluation and impact assessment Artificial intelligence and machine learning for maximum efficiency Autonomous tuning works Artificial intelligence, machine learning and energy transition 8.7 Case Studies Pump monitoring Preventive maintenance Predictive analysis Wildfires Materials discovery with artificial intelligence Digital manufacturing sector Energy industry and digital transformation Circular economy with artificial intelligence Food and beverage industry with AI Multivariate analytics Safety and reliability Decarbonization and artificial intelligence Energy policy with targets Retrosynthesis and artificial intelligence Digitalization, industrial artificial intelligence and sustainability Digital solutions Continuous flow chemistry Sustainable catalysis Biocatalysis Intensification using membrane separators and reactors Intensification of the CO2 stripper by reducing the energy penalty Advancements in coating technologies Developing hydrogen technology Digital solutions for hydrogen economy Hydrogen challenges Industrial AI-powered workflow Integrated supply chain Beyond hydrogen Summary Nomenclature Problems Research Projects References 9. Workforce in Sustainable Engineering Introduction and Objectives 9.1 Key Competencies and Skills in Sustainability 9.1.1 Equity, Diversity, and Inclusion Equity, diversity and inclusion and sustainable engineering Incorporating equity into sustainability assessments of biofuels 9.1.2 Environmental, Social and Governance 9.1.3 Equity, Diversity, and Inclusion in the Industrial Sector 9.1.4 Work Ethics

324 325 326 327 327 328 328 328 329 329 330 330 330 331 331 332 333 333 334 335 335 336 336 336 337 337 337 337 338 338 338 339 340 340 340 341 341 342 343 343 349 349 349 350 351 351 352 352 352

Contents xxi

9.1.5 Skills for Sustainability 9.1.6 Workforce with Sustainability Strategy 9.2 Education for Sustainable Engineering Plan of study Modular teaching technique Workbook strategy Group work for cooperative learning Assessments of the workbook strategy Sustainability and education Engineering sustainability Industry impact on education 9.2.1 Required Topic Areas for Engineering Sustainability Education 9.2.2 Sustainable Resources 9.2.3 Sustainable Processes 9.2.4 Increased Efficiency 9.2.5 Reduced Environmental and Ecological Impact 9.2.6 Addressing Other Sustainability Facets 9.3 Training in Manufacturing Event agent 9.3.1 Mentoring in Industry 9.3.2 Diversity Training 9.3.3 Training in Energy Management 9.3.4 Cybersecurity Training 9.3.5 Training in Energy Analysis Sustainability jobs 9.3.6 Training in Process Intensification 9.3.7 Training in Artificial Intelligence 9.4 Workforce Soft Skills 9.4.1 Soft Skills in Energy Analysis 9.4.2 Soft Skills in Process Intensification 9.4.3 Soft Skills in Artificial Intelligence 9.5 Workforce and Digital Transformation 9.5.1 Connected Workforce 9.6 Sustainable Engineering Curriculum with Process Intensification and Artificial Intelligence Problem-based learning Summary Nomenclature Problems References

353 354 354 355 355 356 357 357 357 358 359 360 360 361 361 362 363 364 364 365 367 367 367 368 369 369 369 370 371 371 372 373 374 376 377 377 378 378 379

Index

383

About the Authors

401

Chapter 1

Sustainable Engineering INTRODUCTION and OBJECTIVES Sustainability is maintaining or improving the material and social conditions for human health and the environment over time without exceeding the ecological capabilities that support them. Energy is one of the main drivers of technology and development, and hence the demand for energy continues to grow world-wide. The ways we produce, convert, store, and use energy are changing Earth’s climate and hence the ways humans live now as well as the ways future generations will live. Therefore, energy management is a global challenge. Nearly two-thirds of global greenhouse gas (GHG) emissions are associated with the production and consumption of energy. This makes the sustainability of energy technology critical to mitigating the adverse effects of global warming and climate change. The objectives of this chapter are to provide an introduction and overview of sustainable engineering, which forms the focus of this book, by providing coverage of: • Sustainability • Natural Earth cycles • Climate change and global warming • Sustainable engineering • Sustainable energy management • Sustainable development and sustainable development goals

1.1 Sustainability Sustainability is the capacity toward maintaining or improving the material and social conditions for human health and the environment over time without exceeding the ecological capabilities that support them. For humans, sustainability is the long-term maintenance of well-being, which has environmental, economic, and social dimensions, and encompasses the concept of stewardship and the responsible management of resource use. Healthy ecosystems and environments provide goods and services to humans and other organisms. Environmental management is based on information from earth science, environmental science, and biology (especially conservation biology). Management of consumption of resources, however, is based on information from economics (Kates 2011, Demirel 2021). Numerous interpretations and understandings of sustainability and sustainable development exist (Rosen 2018, Baleta et al. 2019, Hengst et al. 2020, Pauliuk 2020, Dragicevic 2020, Chen et al. 2020, Rezaie and Rosen 2020). In most if not all of these, sustainability is taken to imply temporally lasting, although the time frame to be considered is subjective. Too short a period for evaluating sustainability is not useful since most activities are sustainable for years, but too long

2  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence a period is intractable. A period of 50–100 years is sometimes viewed as reasonable for many sustainability considerations (Graedel and Allenby 2010), although this time frame can be disputed, especially for energy issues that span centuries. For example, excluding pollution and climate change contributions from coal combustion, coal could be viewed as sustainable for the next century based on the present lifetime in terms of reserve base for coal, which is presently around 120 years (BP 2016). However, coal reserves could be consumed then, clearly implying coal is not sustainable. This contrasts with many types of renewable energy, which have no practical consumption time frame. Also, sustainability can be thought of in terms of limitations by considering carrying capacity, i.e., the maximum supportable population, given the planet’s capability for receiving wastes and providing resources. Resource demand and supply link to carrying capacity significantly, as shown by Park et al. (2020) for Jeju Island, South Korea. More generally, sustainability is often viewed as multidimensional given its economic, social, environmental, and other facets (Jose and Ramakrishna 2021), but a key challenge is that these facets are often in conflict, e.g., social sustainability may require a sacrifice in economic or environmental sustainability. Various sustainability principles have been proposed, based on differing rationales. One useful set of sustainability principles are as follows: (1) reduce dependence on fossil fuels, and underground metals and minerals, (2) reduce dependence on synthetic chemicals and other unnatural substances, (3) reduce encroachment upon nature, and (4) satisfy human needs fairly and efficiently.

1.1.1  Sustainability Dimensions The main dimensions of sustainability are often taken to be economic, environmental, and societal, as seen in Figure 1.1a (Gagnon et al. 2008). Each dimension has a different focus: • Society: Offer individuals and communities the opportunity to increase their capabilities. • Economy: Maintain a positive genuine long-term investment considering all types of capital. • Environment: Preserve biodiversity, respect all life forms, and stay within the ecosystem’s carrying capacity for resource development and waste assimilation. Two-dimensional sustainability (2D) is represented by the overlaps of two of the dimensions of sustainability, as seen in Figure 1.1a. There are several types 2D sustainability: Socio‑Ecological (2D): • Preserve access to ecosystems services essential to health and well-being. • Look beyond one’s locality and immediate future. • Ensure that all material and energy inputs and outputs are inherently safe and benign. Socio‑Economic (2D): • Understand needs. Focus on achieving needs of larger numbers of individuals. • Allocate in a fair manner benefits and costs related to economic activity and public policies. Eco‑Economic (2D): • Develop closed cycles of operation and consumption to minimize waste. • Reduce the use of non-renewable resources by investments in renewable substitutes. • Stimulate innovation to facilitate the adaptation of more efficient and greener technologies.

Sustainable Engineering  3 Environmental

Social 1D Socio-Economic

Socio-Ecologial 2D

Environment 1D •

Societal

3D Sustainable Eco-Economic 2D

(a)

2D

Economic

Economic 1D

(b)

Figure 1.1.  (a) Sustainability at the confluence of its three dimensions, (b) sustainability as systems, in which both the 2 by environmental limits. economy and society are constrained

True sustainability is indicated by the intersection of all three dimensions (3D): Sustainability (3D): • Internalize all costs within the value of goods and services, by accounting for with costs of emissions. • Design processes holistically, use system analysis, and integrate environmental impact assessment tools. • Seek stakeholders’ involvement with consideration of local cultures and subsidiaries. Another interpretation of the interrelations among the dimensions of sustainability shows that economic, and societal indicators may be constrained with in the environment, as seen in Figure 1.1b. In this figure, economic sustainability is considered to be constrained by societal sustainability.

1.1.2  Sustainability Science Sustainability science, as described by Kates (2011), is a research field involving interactions between natural, technological, and social systems. Sustainable science deals with how those interactions affect the challenge of sustainability, that is meeting the needs of present and future generations while substantially reducing poverty and conserving the planet’s life support systems. There many questions for research in sustainability science specified by the World Academies of Sciences, the Initiative for Science and Technology for Sustainability, the International Council for Science, the Third World Academy of Sciences and others (Rosen 2012, Pauliuk 2020, NASEM 2021, Jose and Ramakrishna 2021). Seven major questions for research in sustainability science follow: • What shapes the long-term trends and transitions that provide the major directions for this century? • What determines the adaptability, vulnerability, and resilience of human–environment systems? • How can models be formulated to account for the variation in human–environment interactions? • What are the main tradeoffs between human well-being and the natural environment? • Can scientific “limits” be defined to provide effective warnings for human–environment systems?

4  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence • How can society most effectively manage human ecology systems for a sustainability transition? • How can the “sustainability” of possible pathways of environment management and societal development be evaluated? Natural disasters, climate change, deforestation, loss of biodiversity, and resource depletion are just some of the concerns which helped accelerate national and international movements for sustainability (EIA 2019). A major driver of human impact on Earth systems is the destruction of biophysical resources and, particularly, Earth’s ecosystems.

1.1.3  Sustainability Strategy A sustainability strategy seeks to better understand and to align the elements of sustainability that are most important to external stakeholders. These priorities shape new sustainability commitments. To prioritize a list based on importance, it is necessary to include social progress, economic prosperity, environmental stewardship and good governance with a comprehensive set of ethics and a compliance management program, including training, engagement, and enforcement. The materiality assessment underlines both the importance of these ongoing efforts and the success of such programs to date. Project sustainability and related learning and development are invested in what they do, and how they do it. Engineering and constructing critical human infrastructure is crucial for contributing to sustainable outcomes. There is a need to cultivate leadership and knowledge of sustainability throughout all levels of organizations, reflecting a culture of continuous improvement and innovation. Upskilling professionals and enabling more sustainable solutions can multiply the value shared with clients and the communities they serve. Environmental stewardship cares about reducing and possibly minimizing pollution and waste in all forms and involves a commitment to be stewards of the natural resources. Environmental stewardship is a key priority in materiality assessments (Gagnon et al. 2012, Rosen 2013, Hengst et al. 2020). Equity, diversity, and inclusion Equity is about fairness, justice, and taking deliberate actions to remove systemic, group, and individual barriers and obstacles that hinder opportunities and disrupt well-being. Diversity refers to the similarities and differences among people, often called diversity dimensions. Inclusion is a dynamic state of feeling, belonging, and operating in which diversity is leveraged and valued to create a fair, healthy, and high-performing organization, or community. An inclusive culture and environment leads to equitable access to resources and opportunities for all. It also enables individuals and groups to feel safe and respected (Strawn 2021). Sustainable engineering requires sustainable resources, sustainable processes, and sustainable technology with increased efficiency and reduced environmental impact, as well as equity, diversity, and inclusion (EDI). As engineering activities affect the society in various ways, including living standards and technological developments, EDI should be a central component of strategies for creating a more just and sustainable world. Equity itself is often underestimated but it is important since a truly sustainable future involves active participation of all members of a community. EDI aligns with sustainability as it deals with human rights. As a rule, implementing sustainable business practices includes incorporating the principles of sustainability into every business decision, offering greener alternatives to products or services, and committing to environmentally friendly business operations and principles (Rosen 2012, 2013, Sundström et al. 2019). Sustainable business should commit to sustaining its social resources, which include employees, customers, and reputation. Sustainable business practices offer social, environmental, and financial benefits (Sundström et al. 2019). For example, creating a progressive and inclusive workplace

Sustainable Engineering  5

environment allows employers to adopt sustainable business practices. Three key benefits EDI can offer to sustainability efforts follow: • Knowledge sharing. Implementing a sustainable business model requires a globalized workforce with diverse backgrounds that shares knowledge and information. This can allow companies to remain competitive in a globalized world. • Collaboration. Implementing strategies, policies, and processes for business sustainability needs to be a collaborative effort considering the views of customers, employees, and suppliers. This includes understanding the workforce and both utilizing its strengths and asking for opinions. • Diversity. Industry sector seeks to create innovative, agile, and sustainable companies that care about the world and employees and includes teams that are diverse and can outperform competitors. Environmental, social and governance Environmental, social, and governance (ESG) refers to the practices of an investment that may have a material impact on the performance of that investment. Environmental criteria consider how a company performs in terms of nature. Social criteria examine how it manages relationships with employees, suppliers, customers, and the communities where it operates. Governance deals with a company’s leadership, executive pay, audit, internal control, and shareholder rights. While there is an overlay of social consciousness, the main objective of ESG valuation remains financial performance. Socially responsible investing Socially responsible investing (SRI) goes a step further than ESG by actively eliminating or selecting investments according to specific ethical guidelines. Unlike ESG analysis which shapes valuations, SRI uses ESG factors to apply negative or positive screens on the investment universe. SRI is sometimes referred to as sustainable or socially conscious investing, when focused on environmental causes. Asset management companies sometimes offer funds tailored to socially responsible investors (D’Apice et al. 2021, Rosen 2013). Impact investing Impact investing aims to generate measurable social or environmental benefits in addition to financial gains. Impact investments span several industries including healthcare, education, energy (especially clean and renewable energy), and agriculture. ESG or SRI or impact investing scores can be used to measure a company’s sustainable footprint (Saad et al. 2019, D’Apice et al. 2021).

1.1.4  Environmental Impact Formulation The IPAT formulation attempts to explain environmental impact in terms of three components: population numbers (P), levels of consumption (“affluence”) (A), and impact per unit of resource use (“technology”) (T). The formulation can be expressed as follows: I = P × A × T

(1.1)

where I is the impacts of a given course of action on the environment. The IPAT formulation expresses a balance among interacting factors (Magee and Devezas 2018). This is not a rigorous analysis but is a way of organizing information for a first-order analysis. Impact per unit of consumption is interpreted in its broadest sense as any human-created invention system, or organization that serves to uncouple consumption from impact. If Q is the quantity of goods and services delivered to people, and R is the quantity of resources consumed, then Eq. (1.1) can be modified as follows: I = P × (GDP/P) × (Q/GDP) × (R/Q) × (I/R)

(1.2)

where GDP is the gross domestic product, (R/Q) is the resource intensity, and (I/R) is the impact created per unit of resources consumed. Rearrangement indicates that R = Q × (R/Q), which

6  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence shows that resources consumed are equal to the quantity of goods and services delivered times the resource intensity. The inverse of resource intensity (Q/R) is called the resource use efficiency, or eco-efficiency, which seeks to minimize environmental impacts by maximizing material and energy efficiencies of production. Thus, we can write: R = Q × 1/(Eco-efficiency)

(1.3)

This shows that resources consumed are equal to goods and services delivered divided by eco-efficiency. Genuine savings in resources depend on societal consumption of a given product or service (the relative efficiency gain, ∆e/e) and the relative gain in the quantity of goods and services delivered ∆Q/Q. If ∆Q/Q ≥ ∆e/e then the system is experiencing some adverse effects. Population growth in the developing world and unsustainable consumption levels in the developed world pose a challenge to sustainability. Healthy ecosystems provide vital goods and services to humans and other organisms. Two major ways of reducing negative human impact and enhancing ecosystem services are environmental management and demand management (Curran 2015, Marchese et al. 2018). This is based on information gained from Earth science, environmental science, and biology (especially conservation biology). Environmental management considers the oceans, freshwater systems, land, and atmosphere. Understanding the impacts of consumption is directly related to resource use, resource intensity, and resource productivity.

1.2 Resilience Resilience is the capacity of a system to respond to a change and continue to develop. Sustainability mainly focuses on increasing the quality of life both in the present and for future generations with respect to environmental, social, and economic considerations. Resilience focuses on the response of systems (including environmental, social, and economic systems) to both disturbances and stresses, especially extreme ones. Both concepts represent large, complex, structural, and systemic issues, and joint implementation efforts (Marchese et al. 2018). For example, water utilities face resilience challenges from climate change, as more frequent droughts and periods of intense precipitation affect their ability to rely on reservoirs. One resilience strategy for both electric and water utilities is the pursuit of expanded and more integrated planning that includes supply and demand across central and distributed assets. Electric utilities are exploring alternatives to increase resilience. In addition, green hydrogen is in some cases being added into expanded utility planning. Sustainability and resilience Sustainability and resilience refer to both the state and survivability of a system under normal conditions, as well as in response to a disturbance. Therefore, sustainability and resilience share common research methodologies, such as life-cycle analysis, structural analysis, technoeconomic analysis, and socioeconomic analysis. However, sustainability efforts focus on larger spatial scales and longer temporal scales than resilience. In the environmental protection context, sustainability focuses on preserving traditional methods of resource use, livelihoods, environmental knowledge, and environmental resources. But resilience initiatives focus on adapting to new conditions/ solutions and creating new and innovative uses of traditional knowledge. Further, resilience tends to prioritize processes of systems or features, whereas sustainability prioritizes outcomes of processes of systems (Mamaghami and Medini 2021). A transition toward sustainability needs action priorities. Numerous such priorities have been proposed, including the following: (i) challenges of measuring progress toward sustainability, (ii) promoting equity and justice in sustainability efforts, (iii) adapting to change and moving beyond incremental change to transformational change, and (iv) effectively linking these priorities. Figure 1.2 outlines several possible resilience efforts. Resilience goes beyond the well-known ability to absorb or adapt to adversity, to also include a strategic attribute that could help companies capture change-related opportunities to design

Sustainable Engineering  7

Management and Governance

Persistence in System Structure

• Analysis • Policy of sustainability goals

• Managing resilience • Regime changes/adjustment

Transformation in System Dynamics • Navigating • Transformations • Assessing resilience

Figure 1.2.  Possible resilience efforts (Marchese et al. 2018).

new ways of doing business under stress. A key set of strategically agile processes, enabled by digitalization, creates strategic resilience that also includes a proactive, opportunity-focused attitude in the face of change. Strategic resilience aimed at organizational sustainability must be understood Sustainability as a multi-domain concept similar to the holistic view of sustainability encompassing environment, • Resource economy and society dimensions (Miceli et al. 2021).

1.2.1  Stability, Robustness, and

efficiency • Impact reduction Resilience • Transparency

Strategic About fifty years ago, the Turing instability demonstrated that even simple reaction-diffusion Multi-domain resilence systems might lead to spatial order and differentiation, whilestrategic the Rayleigh-Bénard instability showed resilience • Absorption that the maintenance of nonequilibrium state might be the source of order in fluids subjected to an • Adaptation external disturbance above a critical value. Therefore, distance from global equilibrium in the form • Environment • Flexibility • Economy of magnitude of• Short-term an external disturbance emerges as another constraint of stability; some systems outlook • Society may enhance perturbations and evolve to highly organized states called the dissipative structures after a critical distance from a stable equilibrium. Although the kinetics and transport coefficients represent shortrange interactions, chemical instabilities may lead to long-range order and coherent time behavior, such as a Strategic chemicalagility clock, known as Hopf bifurcation. Stability analyses of linear and Digitalization nonlinear modes for stationary homogeneous systems are useful in understanding the formation • Artificial •Transformation intelligence of organized structures •Renewal as the response to a disturbance. Such a treatment presents the stability • Industry and 4.0 rate processes and underlines the of equilibrium and nonequilibrium •Responsivenesssystems of transport • Data processing •Long-term outlook and stability relationships between complex behavior of systems using classical and nonequilibrium thermodynamics approaches (Demirel 2005, Demirel and Gerbaud 2019). A system’s resilience is described by its buffering capacity, whether it adapts flexibly or stiffly, what happens at the performance margins, and its tolerance. Additionally, increased process Mamaghami andinMedini 2021).systems. Steen and Aven (2011) developed a framework to better complexity results less resilient describe the connections between resilience and risk that is described by the framework of (A,C,U), where events A occur, resulting in the consequence C and all associated uncertainties U of whether A will occur and at what intensity. Uncertainties may be described subjectively by a knowledge Inputs System base to generate probabilities that an event with a certain3Dconsequence will occur. In contrast, Technoeconomic Metrics Material Boundary Multicriteria resilience relies on system vulnerability expressed by (C, U/A) describing the consequences C with Estimations Estimations Energy Defined uncertaintiesWater U given an initiating event A. System robustness and resilience are similar, but the difference 2D and 1Doccurs on the initiating event Maximum Minimum Estimations A. Robustness and vulnerability tolerate a fixed A where resilience can react to any A. Resilience Useful in a system is high if there is aHarmful low probability of operational disruption from normal expected Energy Output events and any unforeseen events (equilibrium state). While the resiliency of a complex system can be improved through correctly identified vulnerabilities. A process with a lesser complexity is Fig. 1.6.toFeasibility technoeconomic analysis and sustainability metrics. assumed be the bestanalysis approachwith for identifying a risk-averse technology in a comparative analysis. Complexity refers to having more “parts” that would undergo analysis in a more focused risk study on a single technology where more the probabilistic approaches would be involved (Steen and Ayen 2011, Fiksel et al. 2015).

8  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence

1.3 Agility Unpredicted events may prevent a system from obtaining targets on time. Agility is likely to help companies deal with unpredicted events and reach time-to-production volumes without exceeding allocated budgets. The interrelated concepts of resilience, agility and risk management increase the ability of the system to handle changes effectively. Furthermore, agility can provide the right product at the right time. Early and reliable information on product and process maturity has an important impact on production. Data system implementation is one of the approaches applied in electronics manufacturing systems, creating reliable interconnections for supply-chain and production objectives. For agile product development an adaptive engineering change management (ECM) approach based on data structures is necessary. Collecting real-time data via approaches of industry 4.0 can support the learning and decision-making process to improve the agility in continuous and reliable production. Two challenges in agile production are the identification of adjustment levers to manage the uncertainties in production ramp-up and to develop a concept to avoid unnecessarily a predetermined design in a contract (Worley et al. 2014, Mamaghami and Medini 2021). A reliable supply chain helps decision-makers react adaptively toward agile and sustainable operation and be able to manage negative disturbances. An optimum supply chain also helps survive and recover from short-term disruptions as well as long term, global shocks with societal and economical transformations in a resilient and sustainable manner (Altay et al. 2018).

1.4  Integrated Sustainability, Resilience, and Agility Management One resilience strategy plans supply and demand across central and distributed assets. For example, utility sectors recognize the need to be more resilient to be reliable. The growing mismatch between customers’ expectations and services can lead to negative publicity and create conflict between reliability and sustainability. Disregarding the similarities and differences between sustainability and resilience can lead to implementation problems by decision makers. Therefore, an integrated sustainability and resilience management approach based on the following three frameworks may be helpful (Marchese et al. 2018, Amindoust 2018): 1. adopting resilience as a component of sustainability in public policy, urban planning, and natural resource planning, 2. adopting sustainability as a component of resilience in business management, public policy, and supply chain management, and 3. utilizing sustainability and resilience as separate conceptual objectives in civil infrastructure, public policy, economics, urban planning, and community resilience. Environmental Economic Disturbance Disturbance

Disturbance

Nonresilient system

Recover

Adapt

Social Disturbance Sustainable system

Critical indicator

Sustainability

Resilient system

Unsustainable system

Absorb

Time

Time

(a) (b) Figure 1.3. (a) Sustainability and resilience as separate conceptual objectives, indicator Figure 1.3.  (a) Sustainability and resilience as separate conceptual objectives, (b) critical indicators(b) forcritical sustainable systems. sustainable systems.

Sustainable Engineering  9

To develop sustainability practices that are consistent with resilience methods, one may frame sustainability as a critical factor, which is to be maintained during and after a disturbance. This factor may be a combination of environmental, social, and economic indicators, or a single holistic indicator such as the Human Development Index (HDI), which includes metrics for income, health, and education (HDI 2021). Figure 1.3 illustrates how these indicators might be tailored to national sustainability objectives. Here, the emphasis is on evaluating and building the resilience of the sustainable components of a system. For example, by focusing investments on building resilience into low-impact energy systems like renewables, the fossil-fuel generated energy would be more likely to fail in response to a disturbance. Later, the response can be focused on sustainable objects. Stability, resilience, and agility There are two dominant interpretations of resilience: absorption and the system’s ability to bounce back to original state. The associated challenges for businesses are the solutions to deal with unexpected or unpredictable change, testing the stability of equilibrium. As a response to external threats, employee-level resilience and strengths, as well as business models or supply chain levels, may create adaptability or design principles that reduce vulnerabilities and disruptions. This requires a proper setting of boundaries, because of the many factors and interdependencies, the levels of systems should be considered when discussing resilience and stability (Miceli et al. 2021). Figure 1.4 displays some of the characteristic features of resilience and agility, which include adaptive capability, flexibility, competitiveness, and scenario predictions. Figure 1.5 shows the interrelations among sustainability, strategic resilience, strategic agility, and digitalization. The multi-faceted domain of resilience is aligned with dimensions of sustainability. Agility builds strategic dimension of resilience, such as the capability of an organization to manage change proactively, effectively and efficiently with a view to transformation and renewal. Agility includes the speed of the organization’s response to an unexpected change (Miceli et al. 2021). Resilience may induce positive outcomes in one of the sustainability domains (ecological, economic, social), but it may not automatically induce the same positive effects in the other domains. Sustainability may improve resilience, but the positive outcome experienced in one resilience domain does not automatically lead to a positive outcome in another one. If sustainability is understood as a multi-domain concept and the ultimate meta objective of the organization, resilience should also be defined correspondingly to include economic, environmental, and social resilience. Establishing a multi-domain concept of resilience needs to account interconnections and trade-offs/synergies about multi-domain sustainability. This implies that resilience must include a normative element regarding the desirable outcome and/or desirability of system conditions (Miceli et al. 2021). •Agility

•Resilience

•Timely response •Proactive attitude •New opportunities

•Va l ue cons erva tion

•Survi va l •Ada ptation •Rea ctive a ttitude

Adaptive Capability

Flexibility

Scenario Predictions

Competitiveness •Ra pidity

•Value creation •Transformation

Figure 1.4.  Characteristic features of resilience and agility (Miceli et al. 2021).

10  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence

Sustainability • Resource efficiency • Impact reduction • Transparency

Strategic resilence • Absorption • Adaptation • Flexibility • Short-term outlook

Multi-domain strategic resilience • Environment • Economy • Society

Strategic agility

Digitalization

•Transformation •Renewal •Responsiveness •Long-term outlook

• Artificial intelligence • Industry 4.0 • Data processing

Figure 1.5.  Sustainability management with resilience, agility, and digitalization (Worley et al. 2014, Mamaghami and Medini 2021).

Stability and resilience To maintain organizations and society towards a state of equilibrium ensuring stability is a challenging task. Stability of a system is continuously tested by internal and external fluctuations Inputs System or changes. When a stable system is subject toTechnoeconomic external or internal3D effects which create fluctuations Metrics Material Boundary Multicriteria or changes, theEnergy stability of the system may change. Depending onEstimations the characteristics of fluctuations Estimations Defined Feasibility or changes, theWater system responds to eliminate the effects of fluctuations or changes and return to Analysis the original stable state if these fluctuations or changes decay in2D time. This may be the case for and 1D resilient systems under moderate system can resist and recover Maximum fluctuations Estimations Minimum or changes. A resilient from impacts and disruptions. if fluctuations or changes are severe and continuous then Useful However, Harmful the system may move toEnergy a semi stable state or unstable state depending on the structural integrity. Output For agile systems, a meta stable system may move to a stable state quickly. Otherwise, an unstable system may evolve to a new state or move to an oscillating structure (Demirel 2005).

.

Building the strategically resilient and agile organization Organizations face complexities and interactions when dealing with resilience and sustainability. Organizations need to understand the dynamics between the concepts, cascading effects, potential trade-offs, and synergies. This may lead to building strategic resilience effectively and efficiently and, in turn, sustainability. Designing and implementing strategically agile processes may create strategic resilience, and this often can be done through the integration of research insights from others. However, strategically agile processes potentially allow the dilemma of efficiency versus efficacy in the organization’s response to change to be addressed. Communities, networks, and ecosystems can influence an organization’s resilience. Setting boundaries, therefore, on the one hand helps to manage complexity but on the other hand brings the disadvantage of omitting important relations. The strategically resilient organization continually leverages and strengthens its competences in change management to establish a virtuous circle (Miceli et al. 2021).

Sustainable Engineering  11

1.5  Why Sustainability Matters? Use of renewable energy technologies and nuclear fuels are growing, but the growth is constrained by cost, infrastructure, and public acceptance. Sustainable designs should satisfy the product specifications while using renewable material and energy. This can lead to optimized, safer, and more efficient startups and operations. The following can comprise energy policies for sustainability (Gagnon et al. 2012, Dragicevic 2020, Chen et al. 2020, Rosen 2021):

• • • •

Increase energy efficiency with energy integration. Increase the usage of renewable energy. Include technologies for carbon capture, storage, and utilization. Design auto thermal processes with heat recovery steam generation (HRSG).

There are major questions for research in human-environment interactions: • What theory and models can determine the adaptability, vulnerability, and resilience of human–environment systems? • What are the principal tradeoffs between human well being and the natural environment? • Can scientifically meaningful “limits” be defined that would provide effective warnings for human–environment systems? • How can society most effectively guide and manage human-environment systems toward a sustainability transition? • How can the sustainability of alternative pathways of environment and development be evaluated?

1.6  Sustainable Engineering Sustainable engineering principles fall within the triangle with environmental, social, and economic values as cornerstones, with the overarching goal of pursuing a balanced solution to any engineering problem. Solutions benefiting only one of these three aspects may create instability in the long run. Engineering is at the interface between design, implementation, and production. Therefore, sustainable engineering incorporates development and implementation of technologically and economically viable products, processes and systems that promote human welfare and health, while protecting of the biosphere. The environmental element focuses on the impact a process design has on ecology, including the effects of carbon emissions, energy use, water usage, and waste production. The economic element focuses on the implementation a bioeconomy and a circular economy. The societal element focuses on welfare and quality of life, health, and education (Gagnon et al. 2008, 2012, Rosen 2012). Many organizations provide their own visions on sustainable engineering. Sustainability engineers’ goals include accountability for the overall sustainability of the engineering system. Doing so requires a deep understanding of how to operate and maintain a system with the Pareto optimal solution or an equally beneficial condition for all three elements. Recommendations and guidance are backed by the principles of fundamentals of engineering. In the Pareto optimality, no individual criterion can be better off without making at least one other individual criterion worse off. An optimization can be based on economic efficiency and income distribution. The Pareto efficiency is based on multi-objective optimization and is considered as a minimal notion of efficiency that does not necessarily result in a socially desirable distribution of resources. Besides economics, the Pareto efficiency can be applied to the selection of alternatives in engineering under multiple criteria and identify an option with properties that no other option can outperform.

12  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence Sustainable engineering design and performance Sustainable design processes may be achieved along six dimensions:

• • • • • •

Attaining a clear understanding of the structure of the design process, covering a defined scope of sustainability elements of environment, economy, and society, considering the relevance of sustainability indicators, using reliable tools for evaluation, assessing alternatives options for potential design improvements, and decision-making (Gagnon et al. 2008, 2012).

Process analytical technology (PAT) provides fundamental understanding of chemical reactions and their performance including continuous flow chemistry, catalysis, and biocatalysis. This may lead to developing sustainable reactor design, which requires the adoption of real-time measurement in manufacturing. In research and development, understanding the reaction kinetics, mechanisms, and the effect of variables on syntheses are enhanced by PAT. In manufacturing, PATs ensure that processes are stable and operate in the prescribed design space to ensure sustainability (Hebrault et al. 2022). Sustainable engineering encourages consideration of the performance of the complete product and process lifecycle during the process design effort. This can minimize environmental impacts and maximize the benefits to social and economic stakeholders in various ways: • Awareness of issues in areas of sustainability including global warming, ozone layer depletion, deforestation, pollution, ethical issues, fair trade, and gender equity. • Understanding of the roles of design, technology, and engineering decisions, and their impacts on environmental, societal, and economic problems with the emphasis on the potential trade-offs between them. • A capacity to distinguish professional and ethical responsibilities associated with the practice of engineering. • Minimization of the effects a product has on the environment from conception, development, and prototyping to commercialization, recycling, and disposal. • Internalization of all costs within the value of goods and services with fees of emissions. • Holistic design processes that use systems analysis and integrate environmental impact assessment tools. • Seeking of stakeholders’ involvement with consideration of local cultures and subsidiaries. A working knowledge of sustainable engineering helps one minimize the effects a product has on the environment at every stage of its lifecycle, from conception, development, and prototyping to commercialization, recycling, and disposal.

1.6.1  Sustainable Engineering Principles There have been multiple attempts by academic and industrial institutions to formulate sustainable engineering principles within the triangle with environmental, social, and economic values as cornerstones. If an engineering project benefits one of these three aspects but ignores the others, this may create tension, instability, and new problems in the long run. Table 1.1 shows the aspects that differentiate the traditional and sustainable approaches in engineering. A framework for sustainable engineering principles includes the following (Gagnon et al. 2008, Rosen 2012, 2013): 1. Engineer processes and products holistically. Targeted durability and resilience should be a design goal that minimizes energy consumption and materials use. 2. Conserve and improve natural ecosystems while protecting human health and well-being.

Sustainable Engineering  13 Table 1.1.  Selected aspects that differentiate traditional and sustainable approaches in engineering (Gagnon et al. 2012). Traditional Engineering

Sustainable Engineering

Considers the object or process

Considers the whole system in which the designs will be used

Focuses on technical issues

Considers both technical and non-technical issues synergistically

Solves the immediate problem

Strives to solve the problem for the present and the future

Considers the local context

Considers the local through global context

Assumes others will deal with political, ethical, and societal issues

Acknowledges the need to interact with experts in other disciplines related to the problem

3. Incorporate life cycle approach in all engineering activities. 4. Ensure that all material and energy inputs and outputs are as inherently safe and benign as possible. 5. Minimize depletion of natural resources. Material and energy inputs should be renewable rather than depleting. 6. Strive to prevent waste. 7. Develop and apply engineering solutions compatible with local geography, aspirations, and cultures. 8. Create engineering solutions beyond current or dominant technologies; improve, innovate, and invent technologies to achieve sustainability. 9. Actively engage communities and stakeholders in development of engineering solutions. The sustainable engineering principles should be considered in decision making for both research and industrial projects as well as in policy making and decisions regarding funding of technological research. Those principles should be contemplated and applied early to ensure that technology development and scale-up follow the environmentally benign and sustainable route. Sustainable systems include complex systems at the molecular scale, at the product scale and at the process scale. A multiscale approach uses process intensification and green products/ green processes to produce molecules and products. A process based on green chemistry principles would lead to the development of process intensification with advanced catalysts, adsorbents, solvents, complex feedstocks, and multiphase processes (Charpentier 2016, Bilge et al. 2016). Many companies have begun viewing sustainability as a key to improve the operational efficiency, reducing waste, and lowering costs.

1.6.2  Sustainable Engineering Techniques Sustainable engineering techniques transform existing engineering disciplines to promote sustainability. Using a methodical structure for solving problems and implementing solutions, firstly the problem is identified and analyzed, and a plan is developed. Then, steps are taken to implement the planned actions. A series of verifications and analyses follows during the check stage, and potential improvements are identified and implemented. For example, green chemistry is a paradigm shift in how chemistry can be used in a sustainable way to redress damage done by improving environmental health and safety. Some important factors in the sustainability of energy system design are: (i) energy use per unit of economic value-added through the product, (ii) type of energy used (renewable or non-renewable), (iii) resource depletion, (iv) freshwater use, (v) waste and pollutants production, and (vi) assessment of overall risk to human health and the environment. To fully implement sustainable engineering solutions, engineers can use the following techniques: • Integrate environmental impact assessment tools in activities. • Conserve and improve natural ecosystems while protecting human health and well-being.

14  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence Inputs Material Energy Water

System Boundary Defined Maximum Useful Energy

Minimum Harmful Output

Technoeconomic Estimations

3D Metrics Estimations 2D and 1D Estimations

Multicriteria Feasibility Analysis

Figure 1.6.  Feasibility analysis with technoeconomic analysis and sustainability metrics.

.

• Ensure that all material and energy inputs and outputs are as inherently safe and benign as possible. • Minimize depletion of natural resources and waste. • Develop and apply engineering options in line with local geography, aspirations, and cultures. • Actively engage with communities and stakeholders. • Use material and energy inputs that are renewable. Figure 1.6 displays the major steps toward a comprehensive feasibility analysis incorporating technoeconomic analysis and sustainability metrics.

1.6.3  Environmental Sustainability Environmental sustainability focuses on GHG and other pollutant emissions and optimizing energy usage effectiveness, utilization factor, and water usage. Healthy ecosystems provide vital goods and services to humans and other organisms with environmental management and demand management of human resource use. At the global scale, environmental management involves oceans, freshwater systems, land, and the atmosphere. Feeding the world’s human population consumes the Earth’s resources; industrial agriculture and agribusiness are now being enhanced through sustainable agriculture, farming and business practices (Ahmad et al. 2020). Sustainability measurement is the quantitative basis for the informed management of sustainability. Some sustainability measures include corporate sustainability reporting, Triple Bottom Line accounting, an using the Environmental Sustainability Index and the Environmental Performance Index. These can be used to estimate the quality of sustainability governance for countries. The Intergovernmental Panel on Climate Change (IPCC), founded in 1988 as a United Nations body, agreed and ratified by 195 states the Paris Climate Agreement to reduce carbon pollution so that the global temperature increase stabilizes to a 1.5°C increase by 2100. The IPCC assesses research on climate change and prepares assessment reports every 5–7 years. Working Group One examines scientific evidence for climate change and the extent to which human activity is the cause. Working Group Two focuses on the impacts of climate change, and how plants, animals and humans can adapt. Working Group Three focuses on mitigating the adverse effects on climate. Environmental security Environmental security necessitates global environmental agreements to manage aquifers and protect shared global systems including the environment. Self-reliant communities are often based on a bioregional economy. New urbanism is successfully reducing environmental impacts by creating and preserving sustainable cities. An eco‑municipality is one that has adopted a particular set of sustainability principles as guiding municipal policy.

Sustainable Engineering  15

1.6.4  Economic Sustainability Sustainability economics represents a broad interpretation of ecological economics where environmental and ecological variables and issues are fundamental but treated as part of a multidimensional perspective. A United Nations Environment Programme (UNEP) environmental protection report (https://www.unep.org/explore-topics/green-economy) proposes a green economy that improves human well-being and social equity, while significantly reducing environmental risks and ecological scarcities. Economic analysis and reform take greater account of the social and environmental consequences of market behavior. Decoupling environmental degradation and economic growth refers to economic production and environmental quality. Ecosystem services emphasize the market relevance to the natural world. Sustainable business opportunities can contribute to a green-collar workforce (Ahmad et al. 2020). Aligning with stakeholder’s priorities The priorities of stakeholders on sustainability programs relate to environmental stewardship across food, energy, water, and waste, as well as climate change and habitat loss. Environmental stewardship reflects the responsibility of balancing the impacts of infrastructure, community development, health and safety, and ethics and compliance. Infrastructure assets improve people’s quality of life and provide critical resources (Mateus 2015). The transition to a sustainable future promotes social progress, economic prosperity, environmental stewardship, and good governance in line with the United Nations Sustainable Development Goals (https://sustainabledevelopment.un.org/sdgs).

1.6.5  Societal Sustainability Societal sustainability requires humans to establish and maintain capacity planning, effective post-incident review practices, and enhanced quality of life through optimization of a social welfare function and welfare economics. Diversity, equity, and inclusion are forms of social responsibilities leading to social sustainability. For example, social equity supports environmental protection and sustainability. Corporations inform the public about their social responsibility activities. There is a link between diversity and social responsibility (Sundström et al. 2019). It has been widely acknowledged that poverty is one source of environmental degradation. Social sustainability challenges include international and national law, urban planning and transport, local and individual lifestyles, as well as consuming habits. The increase of population in the developing world and unsustainable consumption levels in developed countries pose a challenge to sustainability (Singer 2010, Rosen 2012, Pelletier et al. 2018). Carrying capacity of Earth The scientific data (IRENA 2015, EIA 2019) indicate that humans are living beyond the capacity of the Earth. This scientific evidence comes from many sources but is presented in detail in the Millennium Ecosystem Assessment and the planetary boundaries framework (MEA 2003). Consuming less by making the full cycle of production and use sustainable through careful recycling would reduce the impact. Food, energy, water, and materials all need to be considered. Resource intensity and productivity are important tools for understanding the impacts of consumption (EIA 2019, Park et al. 2020). Food‑energy‑water nexus Food, energy, materials, and water are key resources for human needs. The Sun’s energy is stored by plants and powers all living processes. Water efficiency is being improved by enhancing demand management and infrastructure, minimizing the water intensity of goods and services, and concentrating food production in areas of high productivity. The environmental impact of agribusiness is addressed through sustainable agriculture and organic farming (Rezaie and Rosen 2020).

16  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence Water efficiency can be improved by increased demand management, better infrastructure, and lower intensity of goods and services. Sustainable food systems provide food to meet current needs while maintaining healthy ecosystems that can also meet future generations’ need. The environmental impact of agribusiness is being addressed through sustainable agriculture and organic farming (Chen et al. 2020, Rosen 2021). The food-energy-water nexus indicates that the world will need 80% more energy, 55% more water, and 60% more food in 2050 (IRENA 2015). This projection emphasizes the role of food, energy, and water in sustainability for global welfare and protection of the ecology. The projection suggests the following actions, among others: • Combine first- and second-generation biomass to produce ethanol with less water and fertilizers and increased yield, • Use waste lignin for producing valuable chemicals to permit lignocellulosic–based biofuels to be more feasible, • Develop algal technology that uses less water and nutrients in biodiesel/bioproduct production, and • Decouple food production from fossil fuel usage, to help attain more secure water and food supplies.

1.6.6  Process Intensification and Sustainability Process intensification (PI) focuses on substantial improvements in manufacturing and processing by remodeling of existing operation schemes into ones that are both more precise and more efficient. PI aims at safer and more sustainable technological developments. Its tools are reductions in the number of devices (integration of several functionalities in one apparatus), improving heat and mass transfer by advanced mixing technologies and shorter diffusion pathways, miniaturization, novel energy techniques, new separation approaches, integrated optimization, and advanced control strategies (Keil 2018). PI deployment seeks to improve energy efficiency and lower capital investment requirements. Companies have increasingly begun to view sustainability as a key to improve operational efficiency, while reducing waste and lowering costs. PI represents an important enabler of sustainability by its potential to address several of these steps toward sustainability (Anantasarna et al. 2017, Keil 2018): • Water supply • Food production • Housing and shelter • Sanitation and waste management • Energy development • Transportation • Industrial processing • Development of natural resources • Cleaning of polluted waste sites • Planning projects to reduce environmental and social impacts • Restoring natural environments such as forests, lakes, streams, and wetlands • Providing medical care to those in need • Minimizing and responsibly disposing of waste to benefit all • Improving industrial processes to eliminate waste and reduce consumption • Recommending appropriate and innovative use of technology

Sustainable Engineering  17

Industry has considered near- and long-term actions toward sustainability through various steps such as education, energy efficiency, use of renewable feedstocks and energy, green chemistry, and life cycle assessment (Matzen and Demirel 2016). This industrial sustainability requires accountability for the overall sustainability of the system, enforcement of sustainable engineering principles and best practices, and the provision of data to engineers (Stankiewicz et al. 2019). Sustainable engineering employs science and data to enlarge the impacts of all the three dimensions for a truly sustainable system, as shown in Figure 1.7. The reach of sustainability dimensions and improvements can be enhanced by process intensification toward sustainable engineering. For example, reducing the reliance of industry on fossil-based energy, by using renewable energy and feedstocks, can help achieve enhanced environmental, societal, and economic sustainability. Process intensification can be applied to achieve sustainable process design by a systematic, three-stage synthesis-intensification framework. In stage 1, an objective function and design constraints are defined, and a base case is synthesized. In stage 2, the base case is analyzed using economic and environmental analyses to identify process bottlenecks that are translated into design targets. In stage 3, phenomena-based process intensification is performed to generate flowsheet alternatives that satisfy the design targets, thereby minimizing and/or eliminating the process bottlenecks. The computer-aided workflow for the process synthesis-intensification framework operates at the unit operations scale, task scale, and phenomena scale, and it has been shown that novel, more sustainable flowsheet designs are generated by performing process intensification at the lowest (phenomena) scale. The framework generates more sustainable flowsheet alternatives that include hybrid/intensified unit operations that provide reductions in operation cost, carbon footprints and environmental impacts. The next step expands the framework to include a control step for the verification of the synthesis-intensification stages and design decisions (Anantasarna et al. 2017).

Social

2D

3D Sustainable

Environment

2D

2D

Economic

Figure 1.7.  Enlarging the reach of sustainability dimensions by process intensification toward sustainable engineering.

engineering. 1.6.7  Energy Analysis and Sustainability The ways people produce, convert,for store, Earth’s climate and hence The computer-aided workflow theand use energy are changing the ways humans live now as well as in the future. Therefore, energy management is a global , challenge since nearly two-thirds of global GHG emissions are associated with the production and consumption of nonrenewable energy. This makes the sustainability of energy technology critical to mitigating adverse effects of global warming. The use of renewable energy technologies (including

18  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence solar and wind) and alternative ways of using traditional fossil and nuclear fuels are growing but constrained by various factors including cost, infrastructure, and public acceptance. Means of addressing sustainable engineering include (Rosen 2012, 2013, 2017, Demirel 2021):

• • • • •

Increased energy efficiency. Increased usage of renewable energy. Technologies for carbon capture and storage (CCS). Effective energy storage technologies. A leadership role in transforming the global energy sector.

For example, green building efforts use resources more efficiently and reduce a building’s negative impact on the environment. Zero energy buildings may have an even lower ecological impact. ‘Green building certification’ programs require reducing the use of nonrenewable energy considerably. Energy companies and manufacturing sectors are increasingly focusing on sustainability goals with continuous recovery, agility, and flexibility. For example, refineries worldwide produce and/or operate with chemicals to maximize economies of scale and integration opportunities. Beyond feedstocks, chemicals are also large energy consumers, and the transition transforms how chemicals are made so industry jointly develops the technology. Thermal efficiency and sustainability Higher energy efficiency and the use of renewable energy are the twin pillars in moving toward sustainable energy. The IEA defines three generations of renewables technologies. The first generation includes hydropower, biomass combustion, and geothermal power and heat. The second generation includes solar heating and cooling, wind power, modern forms of bioenergy, and solar photovoltaics. The third generation includes advanced biomass gasification, biorefinery technologies, concentrating solar thermal power, hot dry rock geothermal energy, and ocean energy. Customers can drive sustainability and sustainable operations in the era of climate change, addressing more frequent extreme weather events, and natural disasters. Utilities are adapting decarbonization methods and these have emerged as primary initiatives for large corporate customers and other stakeholders, intensifying market drivers for decarbonization. Both oil and gas interests and electric utilities have focused on hydrogen as a unique opportunity to leverage their expertise and capital to fill gaps in the existing array of decarbonization options. Sustainability considerations in energy systems have been considered in a wide range of fields, e.g., biomass gasification (Shahbeig et al. 2022), desalination for freshwater production (Farsi and Rosen 2022, Rosen and Farsi 2022), electrical power generation (Abanades et al. 2022), hydrogen production via electrolysis (Jalili et al. 2021), building cities (Alvarez-Risco et al. 2020), and many others (Dincer and Rosen 2021). Thermodynamic optimum Every open system exchanges mass and energy between the surroundings and the system, during which energy dissipation occurs to some extent, depending on the level of irreversibility. The Gouy-Stodola theorem states that the lost available work rate is directly proportional to the rate of entropy production due to irreversibility in open systems. This statement can be made more explicit for thermal systems by indicating that the irreversibility is directly proportional to the rate of entropy production. So, the rate of entropy production becomes an important parameter in the design of thermal systems. The design questions that follow from the Gouy-Stodola theorem are: • What is the total rate of entropy production? • How is the entropy production distributed through the system? • How can a thermal system be designed to produce the least possible entropy production?

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The thermodynamic optimum is related to improving the thermodynamic imperfections by reducing the irreversibility through sustainable design and sustainable operation so that the available energy delivered would be a maximum. Exergy is the maximum amount of work theoretically available on bringing a resource into equilibrium with its surroundings, and it occurs through a reversible process. Therefore, exergy is a function of both the properties of a resource and those of its environment (Demirel 2004, 2013). Efficient resource utilization A thermodynamic optimum links efficient resource utilization, optimum process design and optimum operating conditions to efficiency, and pollutant emissions, affecting the economy, environment, and society, and hence sustainability. The thermodynamic optimum can indicate natural limits on the attainment of sustainability. Entropy production can be minimized through a set of modifications in design and operating conditions This would come from the optimum compatibility between design parameters and operating conditions so that the thermodynamic driving forces are manageable and evenly distributed in size and time (Demirel 2004, 2013).

1.6.8  Artificial Intelligence An artificial Intelligence (AI) model-based approach can integrate process designs coupled with assessments across all sustainability dimensions. Digitalization can support this effort, for instance, in the manufacturing sector, where AI can be employed to improve safety, protect the environment, and maintain continuity. For example, digital twin models help optimizing asset performance and planning by monitoring the status of equipment and units with respect to process safety, process integrity, emissions, energy use, and yields. Digitalization can make a refinery, for example, more agile and flexible. The digital transformation may change the nature of the energy and manufacturing sectors, particularly in how they interact with intelligent systems and virtual models (Nishant et al. 2020). By increasing resilience and agility with AI, the industrial sector can become more sustainable for various reasons (Nishant et al. 2020): • Digitalization, through data and technologies, promotes agility because it increases the flexibility and responsiveness of the organization’s business processes, for example by identifying changes early, and by enabling efficient and effective connection and coordination of business processes and partners. • Digitalization enables efficient use of resources, thus contributing positively to sustainability. • Digitalization triggers change and rebound effects may occur. Thus, it may also have a negative influence, so caution is required. • The strategically resilient organization continually leverages and strengthens its competences in change management to establish a virtuous circle of resilience, change, and management. First‑principles models AI incorporates first-principles models (FPMs) that include mass and energy balances, and physics and/or chemistry-based descriptions of some or all of the terms (e.g., flux or source terms) in these balances. This can be done with fundamental thermodynamics, kinetics, fluid mechanics, and transport phenomena (Pantelides and Renfro 2013). FPM starts directly at the level of established science and does not make assumptions such as empirical modeling and parameter fitting. Predictive analytics Predictive analytics involves various statistical methods, e.g., predictive modeling, data mining, machine learning, that analyze current and historical data to make predictions about unknown or future events. Predictive analytics can greatly assist engineering activities like operations and maintenance.

20  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence For instance, predictive analytics substantially reduces unplanned “flaring.” According to the World Bank, flaring contributes more than 350 million tons of CO2 emissions globally every year, the equivalent of approximately 90 coal-fired power plants. These emissions could be reduced considerably by increasing equipment reliability to eliminate unplanned shutdowns and the flaring that comes with them. Predictive maintenance can dramatically improve safety. The Chemical Safety Board (CSB) asserts that unplanned startups and shutdowns account for 50% of safety incidents in the refining industry. Pressures for greater sustainability, along with an evolving workforce, will continue to impact the industries. Asset optimization can help reduce GHG emissions and increase safety by improving reliability and eliminating inefficiencies in operations, hence lowering their energy consumption, and reducing the use of natural resources. New levels of agility can also support these industries. AI-driven predictive maintenance can warn operators of potential equipment failures days, weeks, or months in advance, reduce the number of unplanned shutdowns, and keep production within safe operating limits. When combined with dynamic modeling technology, AI methods can rehearse critical situations and remove uncertainty by collecting, aggregating, and conditioning data from across an enterprise. Remote operation In a remote operation center, challenges of a mixed generation portfolio are managed by shared automation and an asset management infrastructure optimizes grid efficiency and lower cost. There are no standards among renewable power supplies regarding the type of automation to accommodate various technologies and variation of power outputs. This makes the integration of such automation systems important, and even essential. Each renewable power site can benefit from real-time monitoring and internet of things (IoT) capabilities with various sensors to determine electric current and voltage, flow rates, pressures, temperatures, and vibration readings. A new automation component is the edge controller, which can be a main component of an IoT-based energy management system. An edge controller can integrate with original equipment manufacturer automation technologies supporting various means of digital communication and hence provide the functionality required to control the local equipment with a remote operating center. Sustainability challenges Sustainability (including green process engineering) will face new challenges to include complex systems at the molecular scale, at the product scale and at the process scale. A multiscale approach combines both market dynamics and technology initiatives oriented to process intensification and coupled green products/green processes for achieving more and better with using less. Focusing simultaneously on process development and scale-up, and on developing techniques, and simulation and modeling tools may reduce scale-up risks. A process designed based on green chemistry principles will commercially be “green” only if scaled up correctly. This may lead to the development of sustainable processes, including process intensification. Significant progress made on catalysts, adsorbents, solvents, complex feedstocks, and multiphase flows, both experimentally and computationally, has had an impact on process design and modeling, and process performance, with sustainability in mind (Singer 2010, Nguyen and Demirel 2011, Charpentier 2016, Sloan et al. 2020).

1.6.9  Views Regarding Sustainable Engineering Views of people, engineers, and companies and towards sustainability have been examined in many constituencies, including business (Reid et al. 2009), the public (Mbeng et al. 2009) and students (Reid et al. 2009, Alvarez-Risco et al. 2021). In this section, the views of engineers and companies and towards sustainability are examined, to better understand how attitudes and actions towards

Sustainable Engineering  21

sustainability are shifting and the relevant barriers. The topics covered are widely varying, and include energy sustainability (Rosen 2013, 2017, 2021). The data used is based on data from a 2009 online survey of mechanical engineers (ASME 2012, Winters 2010, Rosen 2013), but appears to be applicable to other types of engineering and still valid today. The survey was sent to members of the ASME (www.asme.org) in the U.S. and had 3029 responses from practicing engineers and 1354 from engineering students. The principal job function of the practicing engineer respondents is broad (23% in design/development engineering, 23% in consulting/professional services, 19% in engineering or other management). The industries are represented by the respondents are also broad (24% energy, petroleum, and related equipment; 15% consulting, design and professional services; 15% manufacturing, materials and machinery; 13% transportation and defense; 13% others). The views of engineers towards sustainability appear to be for the most part positive, as evidenced by the following: • For practicing engineers, professional views and attitudes are generally positive towards sustainability. Most practicing engineers believe that sustainable and/or green design principles to more product innovation (66%) and is of growing interest to colleagues (60%). • Personal attitudes of practicing engineers are quite positive towards sustainability, as over 80% claim they are personally involved in sustainable information and causes outside of work. • For engineering students, personal attitudes are even more positive towards sustainability than practicing engineers, with 89% claiming to be involved in green and sustainable information and causes. Most engineering students feels that the use of sustainable and/or green design principles in the design, production, and operation of manufactured products is of increasing interest to fellow students (74%) and results in more product innovation (86%). • More than half of engineering students (56%) believe that their school has a sustainable design class, program, or assignment. Nonetheless, some practicing engineers and engineering students have reservations about the benefits of sustainability. Example comments include feeling sustainability is based on unsound science, or a temporary fad, or wasteful of company time and money, or motivated by public relations, or driven by politics. The views of engineering companies about sustainability are largely positive, as supported by the data in Table 1.2 and the following key points: • About 67% of practicing engineers claim to be involved with sustainability or sustainable technologies. Most (66%) claim that at least some of their projects over the past year included sustainable and/or green design principles, over and beyond regulatory requirements. Yet only 14% of respondents felt that more than half of their projects included specifications based on sustainable and/or green design principles beyond regulatory requirements. • In the coming year, most practicing mechanical engineers (63%) expect their organization to become more involved in sustainable and/or green design. • Practicing engineers consider the most important sustainable technologies to be those that use less energy or reduce emissions (64%), followed by manufacturing processes that use less energy and natural resources (27%). • The most common sustainable technologies with which organizations are currently involved include those that use less energy and reduce emissions (71% of organizations) and that comply with environmental standards and regulations (71%). By comparison, sustainable technologies worked on by practicing engineers the most over the year are those that use less energy or reduce emissions (64%) and that comply with environmental standards and regulations (54%).

22  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence Table 1.2.  Engineers use of and views about sustainability measures (Rosen 2013). Sustainability measure

Used in past year (%)

Organization currently using (%)

Reduce energy use or emissions

64

71

Comply with environmental standards and regulations

54

71

Use renewable/recyclable/recycled materials

27

43

Use non-toxic materials

20

37

Attain low carbon footprint

21

36

Reduce manufacturing material waste

22

40

Reduce manufacturing energy and natural resource use

21

33

Reduce manufacturing pollution

15

31

The views of engineers on factors that affect sustainable design and practices are particularly interesting and noteworthy (Reid et al. 2009, Singer 2010, Rosen 2013, 2018). • Practicing engineers feel that organizations must compromise or balance factors that influence the use of sustainable design and practices with other priorities (economics, production). It was found that 34% of organizations invest in sustainable technologies for new products if they lower costs, 27% if they increase throughput and reduce costs of existing products/processes, 24% if they do not affect throughput or costs of existing products, and 9% to make a statement with some flagship products. Also, 19% will spend extra to incorporate sustainable technologies in most new products, while 15% do not invest in sustainable technologies. • Practicing engineers indicate that sustainability is most addressed in the conceptual design phase of a project (76%), materials selection (50%), process selection (44%) and reporting (21%). This suggests that engineers feel that sustainability is usually better worked into a project as early as possible, and throughout all project stages. • The factors most cited by practicing engineers as likely to affect application of green design practices by engineering organizations follow regulatory requirements (42%), client demand (19%), rising energy costs (16%), gaining market advantage (6%), long-term return on investment (5%), personal sense of environmental responsibility (5%) and government/industry incentives (3%). Clearly, regulatory requirements, client demand and rising energy costs most affect organizational use of green design practices and procedures. Despite the positive views of engineers towards sustainability, barriers exist to utilizing sustainability measures by engineers in engineering work (Singer 2010). These include economics, short-term focus, competitiveness, confidentiality, market forces, customer demand and inertia to change. In addition, there is a lack of corporate culture and commitment, incentives, sharing of best practices, commonly accepted and consistent procedures, and measures for assessing sustainable engineering, and relevant codes, standards, regulations, and laws for sustainable engineering. These points provide some indication of the needs for improved education about sustainability and sustainable engineering. Nonetheless, there appears to be a growing focus on the implementation of sustainability concepts, actions, and measures in engineering.

1.7  Sustainable Engineering: Energy Analysis, Artificial Intelligence, and Process Intensification Truly sustainable engineering has essential contributions from at least three elements: energy analysis, process intensification, and artificial intelligence, as seen in Figure 1.8. These elements can be complementary and lead to considerable improvements in environmental, economic, and societal dimensions of sustainability. Energy analysis can contribute to ecological and environmental

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Energy analysis • Energy integration • Energy efficiency • Energy recovery • Energy management • Increaded EROI • Thermodynamic optimum (via exergy analysis, less irreversibility, reduced lost work)

SUSTAINABLE ENGINEERING Environment, Economy, Society Process intensification • Increased efficiency • Reduced size and time • Safer operation • Reduced CAPEX • Reduced OPEX • Less irreversible • Reduced waste • Reduced emissions

Atificial intelligence/ machine learning • Predictive maintenance • Asset management • Increased productivity • Self optimizing plants • Reduced downtime • Data processing • Viable supply chain

Figure 1.8.  Interrelations of energy analysis, process intensification, and artificial intelligence for sustainable engineering.

protection as itFihelps reduce GHG and other emissions and depletion of natural resources by sustainable increased energy recoveryengineering. and energy efficiency. This also increases energy return on investment to allow more useful energy for consumers leading to a positive impact on society. Exergy analysis can also help reduce irreversibility, thereby avoiding the unnecessary energy dissipation in process operations with heat and material flows, as well as with energy conversions (Demirel 2013). Process intensification introduces considerable improvements in process (plant) operations, unit (equipment) design, and methodology. This naturally leads to safer and more economical designs, implementations, and operations. Process intensification also leads to efficient use of resources with less waste material and energy, and pollutant emissions. Therefore, process intensification brings considerable improvements for economics and society, as well as the environment (Amindoust 2018). Investments in artificial intelligence with machine learning can provide companies with efficient operation and maintenance as well as increased productivity and lower operational costs. These gains are often reflected in consumer benefits and in effective asset management. Artificial intelligence helps manufacturing and industrial sectors to process and share operation data, which leads to the creation of data based on new knowledge in operation and maintenance. Artificial intelligence can facilitate highly effective predictive maintenance to reduce operation downtime considerably and hence to increase production with lower operational expenses. This normally has positive societal and economic effects. Artificial intelligence with data management supports effective coordination between scientist, engineer and designer and produces new processes and products with global benefits. In addition, with improved and effectively managed supply chains, production reliability and quality increases (Pantelides and Renfro 2013, Amindoust 2018, Visser 2020, Nishant et al. 2020, Rosen 2021).

24  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence

1.8  United Nation Sustainable Development Goals In 2015, the UN General Assembly formally adopted the universal, integrated and transformative 2030 Agenda for Sustainable Development Goals (SDGs) consisting of a set of 17 SDGs and 169 associated targets (https://sustainabledevelopment.un.org/sdgs). The SDGs are: (1) no poverty, (2) zero hunger, (3) good health and well-being, (4) quality education, (5) gender equality, (6) clean water and sanitation, (7) affordable and clean energy, (8) decent work and economic growth, (9) industry, innovation, and infrastructure, (10) reduced inequalities, (11) sustainable cities and communities, (12) responsible consumption and production, (13) climate action, (14) life below water, (15) life on land, (16) peace justice and strong institutions, and (17) partnerships for the goals. The SDGs were adopted by 193 countries as the world’s shared plan to protect the planet, end extreme poverty, and reduce inequality. Achieving the SDGs requires coherent and global efforts that need to be backed by practical commitments and actions. Sustainable development was presented by the United Nations Commission on Environment and Development in 1987 as an approach to reconcile global economic development with environmental protection and social equity. The World Engineering Partnership for Sustainable Development WEPSD was formed, and it is responsible for the following areas: redesign engineering responsibilities and ethics to sustainable development, analyze and develop a long-term plan, find solution by exchanging information with partners and using new technologies, and solve critical global environment problems. Effective global collaboration is important for achieving the SDGs and an understanding of the needs of individual countries (Sullivan et al. 2018). Bioeconomy strategy Three conflicting goals in any future bioeconomy strategy are non-food uses of arable land, use of crop land to produce feedstock for meat, milk, and egg production and, finally, the conversion of forests into agricultural land. Bioeconomy expertise and know-how should be shared in close cooperation between developed and developing economies to attain or shift toward the UN SDGs. Zero waste and zero emissions are ambitious goals for a more sustainable bioeconomy. An accessible and sustainable bioeconomy needs to be developed for meeting the UN sustainable development goals tracked and documented at local, regional, and international levels. The elimination of fossil-fuel subsidies and the adoption of a global taxation strategy for the carbon footprint should be an integral part of any bioeconomy evaluation process. Elimination of poverty and reaching zero hunger, rural development, and promoting quality education need to be priority targets for a future bioeconomy. Sustainable green city concepts and a low-carbon circular economy should be further promoted in any future global bioeconomy. Finally, research could help to determine the success stories of bioeconomy in achieving the stated UN SDGs (Nguyen and Demirel 2013). Sustainable development Sustainable development helps maintain the delicate balance between today and next generation, without endangering the resources of tomorrow. It, therefore, refers to an approach that calls for the adoption of development strategies that consider both the observable short-term effects (sustainability) and the long-term effects (sustainable development) (Miceli et al. 2021). Other concepts have since emerged from this foundation, such as definitions of: • Environmental sustainability, which emphasizes the preservation of biodiversity without sacrificing economic and social progress, • Economic sustainability, which ensures that activities that seek environmental and social sustainability are profitable, • Social sustainability, which seeks population cohesion and stability.

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Thus, sustainability and sustainable development work on the principle that available resources cannot be used indiscriminately, that natural resources must be protected, and that all people must have access to the same opportunities. Environmental value can be achieved using renewable resources and the reduction in waste and emissions. Sustainability and sustainable development have become a central issue and companies are called on to seek a balance between economic, social, and environmental benefits with the call for the triple P (people, profit, and planet) bottom line, which defines sustainability as the intersection of environmental, social, and economic value. Social value derives from social development and the well-being generated by the organization; economic value commonly is seen in terms of survival, growth or long-term performance of the firm. Frequently, economic value is seen as a consequence of organizational resilience (Sullivan et al. 2018). Resilient development If sustainability is understood as a multi-domain concept and ultimate meta objective of the organization, resilience should also be defined correspondingly to include economic, environmental, and social resilience. Establishing a multi-domain concept of resilience allows us to account for interconnections and trade-offs/synergies about multi-domain sustainability. A resilient development is one that adapts to changing conditions and can recover from extreme and adverse circumstances. Resilience and sustainability share the same goal, that is to achieve sustainable development. In times of uncertainty, commitment to sustainability is essential for companies that also want to be resilient. Therefore, the resilience of organizations helps to deal with the complexity of change fluctuations, while preserving the capacity for development. The governance of these organizations in such complex scenarios should focus on achieving organizational resilience based on stable sustainability (economic, social, and environmental) that ensures the achievement of their sustainable development goals. Resilience may induce positive outcomes in one of the sustainability domains (ecological, economic, social), but it may not automatically induce the same positive effects in the other ones. Sustainability may improve resilience, but the positive outcome experienced in one resilience domain does not automatically lead to a positive out come in another one (Worley et al. 2014, Miceli et al. 2021). Summary Sustainability is maintaining or improving the material and social conditions for human health and the environment over time without exceeding the ecological capabilities that support them. Energy is one of the main drivers of technology and development, and energy demand is continually growing world-wide. Energy and other processes are changing Earth’s climate and hence the ways humans live now and in the future. Sustainable engineering in all areas, including energy technology, is important for mitigating climate change and other environmental impacts, creating wealth and good living standards, and fostering social harmony. This chapter provides an introduction and overview of sustainable engineering, which forms the focus of this book, through coverage of sustainability, natural earth cycles, climate change and global warming, sustainable engineering, sustainable energy management, and sustainable development and sustainable development goals. Featured throughout are the three key dimensions of sustainability: economic, environmental, and societal. The discussion of sustainable engineering is broad, covering sustainable engineering principles and techniques, each of the dimensions of sustainability, the ties of sustainability to process intensification, energy and artificial intelligence, and views regarding sustainable engineering. The bottom line of this chapter is that sustainable engineering is a key requirement of sustainable development, as it helps reduce or minimize the effects a product or process has on the environment at every stage of its life cycle, from conception, development, and prototyping to commercialization, recycling, and disposal, while maintaining both economic sustainability and societal or social sustainability. Sustainable engineering is encountered in the cornerstone

26  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence dimensions of sustainability: environmental, social, and economic. The aim of pursuing a balanced solution to any engineering problem thus requires a sustainable engineering approach. Outcomes of sustainable engineering that benefit only one or two of these three dimensions of sustainability may not be sustainable in the long term and may lead to instabilities of other challenges. A framework for sustainable engineering is required, since traditional and sustainable approaches in engineering are different, and the former usually do not support well sustainable development relative to the latter.

Nomenclature AI Artificial intelligence ASME American Society of Mechanical Engineers (www.asme.org) CAPEX Capital expenses CCS Carbon capture and storage CSB Chemical Safety Board ECM Engineering change management EDI Equity, diversity, and inclusion EROI Energy return on investment ESG Environmental, social, and governance Ex Exergy FPM First-principles model GDP Gross domestic product GHG Greenhouse gas HDI Human development index HRSG Heat recovery steam generation IoT Internet of things IPCC Intergovernmental Panel on Climate Change I4.0 Industry 4.0 OPEX Operational expenses Q Quantity of goods and services delivered to people R Quantity of resources consumed SDG Sustainable development goal SRI Socially responsible investing UNEP United Nations Environment Programme WEPSD World Engineering Partnership for Sustainable Development

Problems 1.1 Is the definition of sustainable development, as laid out by The Brundtland Commission, practical and useful? Describe pluses and minuses of this definition. 1.2 What are the three common dimensions of sustainability? Discuss each and explain if they are sufficient to cover all facets of sustainability? If not, describe what other dimensions of sustainability may be needed to be more comprehensive. 1.3 In the context of sustainability, explain what resilience is and how it relates (or does not relate) to sustainable development? 1.4 From a sustainability perspective, describe and compare the advantages and disadvantages for automobiles that use (a) internal combustion engines, (b) electric propulsion, (c) hybrid power systems, and (d) fuel cells. 1.5 Identify which of the Sustainable Development Goals relate to engineering, and describe the relations between those Goals and engineering. 1.6 Explain how engineering methods can be used to facilitate movement towards achieving each of the Sustainable Development Goals.

Sustainable Engineering  27

1.7 Search for a paper on the application of engineering sustainability methods to a specific industry. List and describe the ways in which the engineering sustainability methods help identify potential improvements in that industry. 1.8 Select a device or system for a specific task (of your choice) and compare it with alternative devices or systems for the same task in terms of sustainability and/or environmental impact and/or cost. Other relevant factors can also be considered, e.g., performance, efficiency, and resource use. Utilize appropriate and reliable information sources, possibly including books, journals, conference proceeding, reports, direct communications with relevant experts, and the internet. 1.9 Reflect on Figure 1.3 and provide your comments for making it more focused for our future consideration of ‘sustainable engineering.’ 1.10 Resilience in one domain does not automatically lead to resilience in another domain. Explain this with examples. 1.11 Sustainability in one domain does not automatically lead to sustainability in another domain. Explain this with examples.

Research Projects 1. Sustainability and resilience can be viewed as two interdependent concepts. a. Clarify how resilience affects sustainability (understood in a holistic sense). b. Clarify how sustainability affects resilience. 2. Agility builds a strategic dimension of resilience, for example, the capability of an organization to manage change proactively, more effectively and more efficiently with a view to transformation and renewal. It includes the notion of the speed of the organization’s response to change. Discuss how to make a resilient system an agile system.

References Abanades, S., Abbaspour, H., Ahmadi, A., Das, B., Ehyaei, M.A., Esmaeilion, F., El Haj Assad, M., Hajilounezhad, T., Hmida, A., Rosen, M.A., Safari, S., Shabi, M.A. and Silveira, J.L. 2022. A conceptual review of sustainable electrical power generation from biogas. Energy Science & Engineering 10(2): 630–655. Ahmad, T., Zhang, H. and Yan, B. 2020. A review on renewable energy and electricity requirement forecasting models for smart grid and buildings. Sustainable Cities and Society 55: 102052. doi: 10.1016/j.scs.2020.102052. Altay, N., Gunasekaran, A., Dubey, R. and Childe, S.J. 2018. Agility and Resilience as antecedents of supply chain performance under moderating effects of organizational culture within humanitarian setting: A dynamic capability view. Production Planning and Control 29(14): 1158–1174. Alvarez-Risco, A., Del-Aguila-Arcentales, S., Rosen, M.A., García-Ibarra, V., Maycotte-Felkel, S. and MartínezToro, G.M. 2021. Expectations and interests of University students in COVID-19 times about sustainable development goals: evidence from Colombia, Ecuador, Mexico, and Peru. Sustainability 13(6): 3306. Alvarez-Risco, A., Rosen, M.A., Del-Aguila-Arcentales, S. and Marinova, D. (eds.). 2020. Building Sustainable Cities: Social, Economic and Environmental Factors. Springer Nature, Cham, Switzerland. Amindoust, A. 2018. A resilient-sustainable based supplier selection model using a hybrid intelligent method. Computers and Industrial Engineering 126: 122–135. Anantasarna, N., Suriyapraphadiloka, U. and Babi, D.K. 2017. A computer-aided approach for achieving sustainable process design by process intensification. Computers & Chemical Engineering 105: 56–73. ASME. 2012. Sustainable Design Trend Watch Survey Results. Viewed 2012 July 12, at http://memagazine.asme.org/ Web/Sustainable_Design_Trend.cfm. Baleta, J., Mikulčić, H., Klemeš, J.J., Urbaniec, K. and Duić, N. 2019. Integration of energy, water and environmental systems for a sustainable development. Journal of Cleaner Production 215: 1424–1436. Bilge, P., Seliger, G., Badurdeen, F. and Jawahir, I. 2016. A novel framework for achieving sustainable value creation through industrial engineering principles. Procedia CIRP 2016 40: 516–523. BP. 2016. BP Statistical Review of World Energy. Charpentier, J-C. 2016. What kind of modern “green” chemical engineering is required for the design of the “factory of future”? Procedia Engineering 138: 445–458.

28  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence Chen, S., Kharrazi, A., Liang, S., Fath, B.D., Lenzen, M. and Yan, J. 2020. Advanced approaches and applications of energy footprints toward the promotion of global sustainability. Applied Energy 261: 114415. Curran, M.A. 2015. Life Cycle Assessment: A Systems Approach to Environmental Management and Sustainability. CEP October. D'Apice, V., Ferri, G. and Intonti, M. 2021. Sustainable disclosure versus ESG intensity: Is there a cross effect between holding and SRI funds? Corp. Soc. Responsib. Environ. Manag. 28: 1496–1510. Demirel, Y. 2004. Thermodynamic analysis of separation systems. Separation Science and Technology 39: 3897–3942. Demirel, Y. 2005. Stability of transport and rate processes. Int. Journal of Thermodynamics 8: 1–14. Demirel, Y. 2013. Thermodynamics analysis. Arabian Journal Science Engineering 38: 221–249. Demirel, Y. 2021. Energy: Production, Conversion, Storage, Conservation, and Coupling, 3rd ed. Springer, London. Demirel, Y. and Gerbaud, V. 2019. Nonequilibrium Thermodynamics: Transport and Rate Processes in Physical, Chemical and Biological Systems, 4th ed., Elsevier, Amsterdam. Dincer, I. and Rosen, M.A. 2021. Exergy: Energy, Environment and Sustainable Development, 3d ed. Elsevier, Oxford, UK. Dragicevic, A.Z. 2020. Concentric framework for sustainability assessment. Journal of Cleaner Production 248: 119268. EIA (Energy Information Administration). 2019. International Energy Outlook 2019 with projections to 2050 September 2019. https://www.eia.gov/outlooks/ieo/pdf/ieo2019.pdf. Farsi, A. and Rosen, M.A. 2022. Multi-objective optimization of a geothermal steam turbine combined with reverse osmosis and multi-effect desalination for sustainable freshwater production. Journal of Energy Resources Technology 144(5): 052102. Fiksel, J., Polyviou, M., Croxton, K.L. and Pettit, T.J. 2015. Learning to deal with disruption. MIT Sloan Management Review 56(2): 79–86. Gagnon, B., Leduc, R. and Savardi, L. 2008. Sustainable development in engineering: a review of principles and definition of a conceptual framework. Environmental Engineering Science 26: 1459–1472. Gagnon, B., Leduc, R. and Savardi, L. 2012. From a conventional to a sustainable engineering design process: different shades of sustainability. Journal of Engineering Design 23(1): 49–74. Graedel, T.E. and Allenby, B.R. 2010. Industrial Ecology and Sustainable Engineering. Upper Saddle River, NJ: Prentice Hall. HDI (Human Development Index). 2021. United Nations Development Programme. https://hdr.undp.org/en/content/ human-development-index-hdi. Hebrault, D., Rein, A.J. and Wittkamp, B. 2022. Chemical knowledge via in situ analytics: advancing quality and sustainability. ACS Sustainable Chem. Eng. 10: 5072–5077. Hengst, I.-A., Jarzabkowski, P., Hoegl, M. and Muethel, M. 2020. Toward a process theory of making sustainability strategies legitimate in action. Academy of Management Journal 63: 246–271. https://www.sustainability.com/ globalassets/sustainability.com/thinking/pdfs/2021/ermsi-corporate-progress-and-action-on-dei-september21.pdf. IRENA (International Renewable Energy Agency). 2015. Renewable energy in the water, energy & food nexus, www. irena.org/publications. Jalili, M., Chitsaz, A., Holagh, S.G., Ziyaei, M. and Rosen, M.A. 2021. Syngas-fed membrane-based and steam and water-fed electrolysis-based hydrogen production systems: renewability, sustainability, environmental and economic analysis and optimization. Journal of Cleaner Production 326: 129424. Jose, R. and Ramakrishna, S. 2021. Comprehensiveness in the research on sustainability. Materials Circular Economy 3: 1. Kates, R.W. 2011. What kind of a science is sustainability science? Sustainability Science 108(49): 19449–19450. Keil, F.J. 2018. Process intensification. Rev. Chem. Eng. 34(2): 135–200. Magee, C.L. and Devezas, T.C. 2018. Specifying technology and rebound in the IPAT identity. Procedia Manufacturing 21: 476–485. Mamaghami, E.J. and Medini, K. 2021. Resilience, agility and risk management in production ramp-up. Procedia CIRP 103: 37–41. Marchese, D., Reynolds, E., Bates, M.E., Morgan, H., Clark, S.S. and Linkov, L. 2018. Resilience and sustainability: Similarities and differences in environmental management applications. Science of the Total Environment 613-614: 1275–1283. Mateus, A. 2015. Digital Economy and Society Index (DESI) Methodological note. EU Comm. Bruxelles. 1: 8–9. Matzen, M. and Demirel, Y. 2016. Methanol and dimethyl ether from renewable hydrogen and carbon dioxide: Alternative fuels production and life-cycle assessment. Journal of Cleaner Production 139: 1068–1077. Mbeng, L.O., Probert, J., Phillips, P.S. and Fairweather, R. 2009. Assessing public attitudes and behaviour to household waste management in cameroon to drive strategy development: A Q methodological approach. Sustainability 1: 556–572.

Sustainable Engineering  29 MEA. 2003. Millennium Ecosystem Assessment and the planetary boundaries framework. Island Press. https:// millenniumassessment.org/en/index.html. Miceli, A., Hagen, B., Riccardi, M.P., Sotti, F. and Settembre-Blundo, D. 2021. Thriving, not just surviving in changing times: how sustainability, agility and digitalization intertwine with organizational resilience. Sustainability 13: 2052. NASEM (National Academies of Sciences, Engineering, and Medicine). 2021. Progress, Challenges, and Opportunities for Sustainability Science: Proceedings of a Workshop in Brief. Washington, DC: The National Academies Press. https://doi.org/10.17226/26104. Nguyen, N. and Demirel, Y. 2011. A novel biodiesel and glycerol carbonate production plant. International Journal of Chemical Reactor Engineering 9: 1–25. Nguyen, N. and Demirel, Y. 2013. Economic analysis of Biodiesel and glycerol carbonate production plant by glycerolysis. Journal of Sustainable Bioenergy Systems 3: 209–216. Nishant, R., Kennedy, M. and Corbett, J. 2020. Artificial intelligence for sustainability: Challenges, opportunities, and a research agenda. International Journal of Information Management 53: 102104. Pantelides, C.C. and Renfro, J.G. 2013. The online use of first-principles models in process operations: Review, current status and future needs. Computers & Chemical Engineering 51: 136–148. Park, J., Kim, S., Kim, Y., Hong, S. and Suh, K. 2020. Evaluation of carrying capacity and sustainability of jeju island using onishi model. Journal of Korean Society of Rural Planning 26(02): 95–106. Pauliuk, S. 2020. Making sustainability science a cumulative effort. Nat. Sustain. 3: 2–4. Pelletier, N., Ustaoglu, E., Benoit, C., Norris, G. et al. 2018. Social sustainability in trade and development policy. Int. J. Life Cycle Assess. 23: 629–639. Reid, A., Petocz, P. and Taylor, P. 2009. Business students’ conceptions of sustainability. Sustainability 1: 662–673. Rezaie, B. and Rosen, M.A. 2020. The energy-water-food nexus: a framework for sustainable development modeling. Energy Equipment and Systems 8: 179–201. Rosen, M.A. 2012. Engineering sustainability: A technical approach to sustainability. Sustainability 4(9): 2270–2292. Rosen, M.A. 2013. Engineering and sustainability: Attitudes and actions. Sustainability 5(1): 372–386. Rosen, M.A. 2017. Bioenergy and energy sustainability. J. Fundamentals of Renewable Energy and Applications 7(7): 25. Rosen, M.A. 2018. Issues, concepts and applications for sustainability. Glocalism: Journal of Culture, Politics and Innovation 3: 1–21. Rosen, M.A. 2021. Energy sustainability with a focus on environmental perspectives. Earth Systems and Environment 5(2): 217–230. Rosen, M.A. and Farsi, A. 2022. Sustainable Energy Technologies for Seawater Desalination. Academic Press/ Elsevier, New York. Saad, M.H., Nazzal, M.A. and Darras, B.M. 2019. A general framework for sustainability assessment of manufacturing processes. Ecological Indicators 97: 211–224. Shahbeig, H., Shafizadeh, A., Rosen, M.A. and Sels, B.F. 2022. Exergy sustainability analysis of biomass gasification: a critical review. Biofuel Research Journal 9(1): 1592–1607. Singer, M. 2010. Economics: Are the planet-unfriendly features of capitalism barriers to sustainability? Sustainability 2: 127–144. Sloan, M., Matolyak, L.E. and Chen, R. 2020. Building a sustainable coatings infrastructure. Chem. Eng. August: 26–30. Stankiewicz, A., Gerven, T.V. and Stafenidis, G. 2019. The Fundamentals of Process Intensification. Wiley, New York. Steen, R. and Aven, T. 2011. A risk perspective suitable for resilience engineering. Safety Science 49(2): 292–297. https://doi.org/10.1016/j.ssci.2010.09.003. Strawn, T. 2021. Corporate progress and action on diversity, equity, and inclusion. The Sustainability Institute by ERM, September: 1–46. Sullivan, K., Thomas, S. and Rosano, M. 2018. Using industrial ecology and strategic management concepts to pursue the Sustainable Development Goals. J. Clean. Product. 174: 237–246. Sundström, A., Ahmadi, Z. and Mickelsson, K. 2019. Implementing social sustainability for innovative industrial work environments. Sustainability 11: 3402. Visser, W. 2020. Integrated innovation: applying systems thinking to sustainable innovation and transformation. Sustainability 12: 5247, https://doi.org/10.3390/su12135247. Winters, J. 2010. Compelled to be green. Mechanical Engineering 132(2): 42–45. Worley, C.G., Williams, T.D. and Lawler, E.E. 2014. The Agility Factor: Building Adaptable Organizations for Superior Performance. John Wiley & Sons: Hoboken, NJ, USA.

Chapter 2

Environmental Sustainability INTRODUCTION and OBJECTIVES This chapter covers environmental sustainability and its context, natural earth cycles and greenhouse gases, including carbon tracking decarbonization, and cost of carbon emissions. The ecological footprint is covered in depth, through such topics as climate change, environmental burden, global warming potential, acidification, ozone formation and destruction, smog formation, human health, toxicity, eutrophication, habitat destruction, resource depletion and particulate matter. The objectives of this chapter are an introduction and overview of environmental sustainability by providing coverage of:

• • • • • •

Environmental burden Greenhouse gases Decarbonization and carbon utilization Environmental cost of carbon emissions Sustainable development and sustainable development goals Environmental impact assessments

2.1  Environmental Sustainability and its Context Environmental sustainability focuses on emissions and impacts of greenhouse gases (GHGs) and other pollutants and on optimizing energy and resource usage utilization factors, water usage, waste management, and use of renewable fuels and feedstocks. A framework for sustainable engineering falls within the environment dimension of sustainable development (Gagnon et al. 2008), and encompasses at least the following areas (see Figure 2.1): Environment: Preserve biodiversity and respect all life forms regardless of how useful they are to humankind. Stay within ecosystem’s carrying capacity in terms of resource development and waste assimilation. Two-dimensional sustainability (2D) is represented by the overlaps of two of the dimensions of sustainability in Figure 2.1. Two types 2D sustainability are of relevance regarding the environment dimension of sustainable development: Socio‑Ecological (2D): • Preserve access to ecosystems services essential to health and wellbeing. • Look beyond one’s locality and immediate future. • Ensure that all material and energy inputs and outputs are inherently safe and benign.

2

Environmental Sustainability  31

Social 1D Socio-Economic

Socio-Ecologial 2D

Environment 1D

3D Sustainable Eco-Economic 2D

2D

Economic 1D

Figure 2.1.  Sustainability at the confluence of its three dimensions, reflected as a triple-bottom-line.

Figure 2.1

Eco‑Economic (2D): There arecycles variousofnatural cycles Earth that affect • Develop closed operation andonconsumption to minimize waste. these are now described. • Reduce the use of non-renewable resources by investments in renewable substitutes. Carbon cycle to facilitate the adaptation of more efficient and greener technologies. • Stimulate innovation

2.2  Natural Earth Cycles 6CO2 +natural 6H2O ↔ C6H12on O6Earth + 6O2that affect the environment and entities living within it. There are various cycles Some of these are now described.

Carbon cycle Plants use carbon dioxide, water, and sunlight to form carbohydrates via photosynthesis, which cycle involves theNitrogen following representative chemical reaction: the fixation a 6CO2 + 6H22020). O ↔ CIn6H (2.1) 12O 6 + 6O2step : Carbohydrates, in turn, produce water and carbon dioxide when they are oxidized. Atmospheric carbon dioxide considerably since the industrial revolution. N2 +levels 3H2 →have 2NHincreased 3

Nitrogen cycle The nitrogen cycle mainly consists of nitrogen fixation, nitrification, and denitrification steps (Zhang et al. 2020). In the fixation step atmospheric nitrogen is converted to ammonia (NH3) by nitrogen-fixing bacteria that live in legume root nodules and in soil: N2 + 3H2 → 2NH3 (2.2) In the nitrification step, ammonia is converted to nitrate in an aerobic process: NH3 + 1.5O2 → NO2– + H+ + H2O (2.3) step This is followed by conversion of nitrite to. nitrate: 19). 2NO– + O → 2NO– (2.4) 2

2

3

Nitrogen and step, sulfurcertain compounds In the denitrification bacteria convert nitrate to nitrogen in the following anaerobic conversion: NO3– → NO2– → NO → N2 → N2. The nitrogen is released to atmosphere. After plants 2 take up nitrogen from the soil in the form of nitrate ions, the nitrogen is passed along the food chain (Takai 2019).

(

32  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence Nitrogen and sulfur compounds Nitrogen oxides include nitrogen dioxide NO and nitric oxides NOx. Molecules of nitrogen and atmospheric oxygen combine at very high temperatures to form nitric oxides. The combustion chambers of engines often cause this conversion of nitrogen to NOx compounds. Once in the atmosphere, NOx compounds may react with additional oxygen to form nitrogen dioxide, which is a red-brown toxic gas that causes irritation to the eyes and respiratory systems. Selective catalytic reduction (SCR) system removes NOx compounds, which are reduced with ammonia at 700°F (271.1°C) with catalysts of TiO2 and V2O5 (Naqvi et al. 2021). Nitrogen oxides from burning fuels at very high temperatures (> 2200°F) (> 1093°C) react with the oxygen in the air and produce ground-level ozone, which has very negative effects on the respiratory system of people and many other living beings and on agricultural production. It is important to measure the total NOx in mitigating it. Nitric acid, formed when NOx reacts with water, can cause acid rain and the deterioration of habitat: NO2 + H2O → H2NO3 (2.5) Sulfur dioxide, SO2, makes up about 95% of all the sulfur oxides released during combustion and is a main cause of acid rain when it reacts with water vapor. Most sulfur dioxide is produced through electricity generation and industrial processes. Sulfur trioxide is a source of corrosion in cold areas of boilers. Hydrogen sulfide, H2S, is a flammable and toxic gas that can form during combustion. Desulfurization involves removing some of the sulfur from the fuel before it is burned. Flue gas desulfurization uses scrubbers that chemically react with the SO2 to form other compounds reducing sulfur oxide emissions by up to 90%.

2.3  Greenhouse Gases Some important GHGs are carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), and fluorinated gases. Carbon dioxide is emitted via burning fossil fuels, solid waste, biological systems, and certain chemical reactions. Production of coal, natural gas, and oil emits methane. Livestock, agricultural processes, and decomposition of organic waste also emit methane. Many agricultural activities, industrial processes, fossil fuel combustion and solid waste, as well as wastewater treatment emit nitrous oxides. Some industrial processes emit fluorinated gases like hydrofluorocarbons, perfluorocarbons, sulfur hexafluoride, and nitrogen trifluoride. These gases are powerful GHGs on a molecule basis, although they are emitted in small quantities. GHGs in the upper atmosphere and small particles in the form of smoke in the lower atmosphere can trap heat escaping from Earth’s surface, while also allowing sunlight to pass (IRENA 2020, Demirel 2021). Impact of greenhouse gas emissions The impact of GHGs on climate depends on the concentration (typically measured in parts per million, ppm) and how long the carbon remains in the atmosphere (typically measured in years). Accumulated moisture in the atmosphere may form into heavy rains and energy-intensive storms. In addition, a warmer atmosphere increases melting of glaciers and polar ice caps, leading to rising sea levels (Kutscher 2007, Demirel 2021). Some specific GHG impacts are described below: • Ozone layer: The ozone layer acts as a filter in the stratosphere, protecting the Earth from harmful ultraviolet radiation from the sun. Chlorofluorocarbons (CFCs) destroy stratospheric ozone and contribute to the greenhouse effect. • Tropical deforestation: Forests act as absorbers of carbon dioxide. Deforestation harms species, including wide variety of animal and plant life, disrupts local climates, causes loss of habitat, and contributes to atmospheric emissions of several chemical species. • Waste: Waste usually ends up in landfill sites, or is incinerated, although it sometimes be simply dumped at sea. Most of this waste does not simply biodegrade into harmless substances.

Environmental Sustainability  33

Incineration can generate energy but also produce toxic gases such as dioxins. Reuse, recycling, and re-manufacturing are essential for reducing waste. • Water pollution: The growth of population and the increasing use of water for industrial purposes are stressing fresh water resources, leading to insufficient supplies of clean water. • Carbon: Engineering designs that are carbon efficient may avoid the increase in the temperature on the surface of the Earth due to GHG emissions. The most common GHG is carbon dioxide (CO2). Other GHGs are often normalized to carbon dioxide equivalent (CO2eq). For example, 1 ton of methane has the same warming effect as about 80 tons of CO2, so we normalize it to 80 tons CO2eq. The Intergovernmental Panel on Climate Change (IPCC) is a United Nations body, founded in 1988, which assesses research on climate change and synthesizes it into major ‘assessment’ reports every 5–7 years. Working Group One of the IPCC assesses scientific evidence for climate change and the extent to which human activity is the cause. Working Group Two focuses on the impacts of climate change, and how plants, animals and humans can adapt. Working Group Three focuses on climate mitigation. The Lancet Countdown is an independent, global monitoring system for the social cost of carbon. It presents various indicators across five areas (Watts et al. 2021):

• • • • •

climate change impacts, exposures, and vulnerabilities adaptation, planning, and resilience for health mitigation actions and health co-benefits economics and finance public and political engagement

Additional harmful effects of climate change from carbon-intensive practices include poor air quality, poor food quality, and poor housing quality, which in turn harm human health. Global food security is threatened by rising temperatures and increases in the frequency of extreme events; global yield potential for major crops declined by 1.8–5.6% between 1981 and 2019. Climate change tends to increase infectious disease transmission. Between 145 million people and 565 million people face potential harm from rising sea levels. A concerted public and political engagement is essential to control fossil fuel consumption and limit the global temperature rise. The health dimensions of climate change are increasingly recognized worldwide (Watts et al. 2021).

2.3.1  Carbon Tracking The standard unit of carbon intensity (CI) is gCO2eq/kWhr. CI changes over time as renewable sources increase or decrease. Understanding the CI helps decide where an effort should be prioritized. Carbon tracking allows the calculation of CO2 equivalent emissions after specifying a ‘CO2 emission factor data source’ and ‘ultimate fuel source.’ CO2 emission factor data source include the European Commission decision ‘2007/589/EC’ or the United States Environmental Protection Agency (EPA) Rule of E9-5711. Table 2.1 shows the emission rates from some fuel sources (ECD 2007, EPA 2009, Demirel 2018, 2021). Table 2.1.  Emission rates (lb/MMBtu*) for various CO2 emission factor data sources and fuel sources (Demirel 2018). Fuel source Natural gas Coal (bituminous) Coal (anthracite) Crude oil Biogas * MMBtu = one million Btu

US-EPA-Rule-E9-5711 130.00 229.02 253.88 182.66 127.67

EU-2007/589/EC 130.49 219.81 228.41 170.49 0

34  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence Table 2.2.  Carbon dioxide emissions from the combustion of various fuels (Demirel 2018, 2021). Fuel

Specific carbon (kgc/kgfuel)

Specific energy (kWh/kgfuel)

Specific CO2 Emission (mass basis) (kgCO2/kgfuel)

Specific CO2 Emission (energy basis) (kgCO2/kWh)

Coal (bituminous/anthracite)

0.75

7.50

2.3

0.37

Gasoline

0.90

12.5

3.3

0.27

Diesel

0.86

11.8

3.2

0.24

Liquid petroleum gas (LPG)

0.82

12.3

3.0

0.24

Natural gas, methane

0.75

12.0

2.8

0.23

Crude oil

0.26

Kerosene

0.26

Wood

0.39

Lignite

0.36

Global CO2 emissions due to fossil fuel use can be calculated as eCO2 = (Cf /Ef )(44/12)

(2.6)

where eCO2 is the CO2 emission in kgCO2/kWh, Cf is the carbon content in the fuel (kgC/kgfuel) and Ef is the energy content of the fuel (kWh/kgfuel). One tonne of carbon is equivalent to: MWCO2/MWC = 44/12 = 3.7 tonnes of carbon dioxide. Table 2.2 shows typical emissions of carbon dioxide from the combustion of various fuels.

2.4  Ecological Footprint GHGs affect the environment and health adversely, through such effects as climate change, and respiratory disease from smog and air pollution. Climate change also causes extreme weather behavior, flooding, and wildfires. The ways we produce, convert, store, and use energy may change Earth’s climate and hence the ways of human’s live as well as the lives of future generations. Therefore, energy management is a global challenge, and the sustainability of energy technology is critical to mitigating adverse effects of global warming. Some major actions needed in reducing atmospheric GHG emissions follow:

• • • • •

Increase energy efficiency. Increase the usage of renewable energy. Develop effective technologies for carbon capture and storage (CCS). Develop effective energy storage technologies. Recognize a leadership role in transforming the global energy sector.

Zero energy buildings and green buildings are intended to use renewable resources more and may have a much lower ecological impact compared with other ‘green’ buildings that require imported energy and/or fossil fuels. ‘Green building certification’ programs require reducing the use of nonrenewable energy considerably. Conventional fossil-fuel based electricity production (~ 40%) and transportation fuels (~ 30%) accounts for a large part of total GHG emissions. Therefore, targeting renewable energy-based electricity generation and transportation fuels will address a large portion of the problems associated with GHG emissions. Some possible renewable energy sources are concentrating solar power, geothermal electric plants, wind power, distributed rooftop photovoltaics, and solar hot water heaters. Electric vehicles represent an important improvement, depending on the source of electricity, as they can be plugged into the grid to be recharged and reduce the amount of gasoline use. In addition,

Environmental Sustainability  35

using E85 (85%–15% blend of ethanol and gasoline) may help reduce carbon dioxide emission. The major challenges with the use of renewable energy forms are cost, intermittency of supply, and remote production sites (Auffhammer 2018, Howard and Sterner 2017).

2.4.1  Climate Change The impacts of climate change include melting of glaciers in polar regions and consequent rising sea levels as well as increased flooding and erosion of coastal areas. Additional impacts include, more frequent heat waves, droughts and forest fires, and shifting rainfall patterns. The latter can lead to reduced availability of water resources and water quality. Climate change is also predicted to cause health impacts, such as increased heat-related illnesses and deaths, as well as water-borne illnesses and diseases. The costs of climate change to societies and their economies could be large, and include costs due to harm to infrastructure, productivity in forestry and agriculture, energy, tourism and other sectors. The impacts on developing and poor countries are likely to be disproportionately large, since they have the least resources to mitigate the adverse effects of climate change and to adapt (Kutscher 2007, Howard and Sylvan 2020). Global warming and climate change Global warming is the primary driver of climate change and is associated with the disruption of the Earth-sun-space energy balance due to anthropogenic activity. According to this energy balance, almost all the energy entering Earth’s atmosphere as short-wave solar radiation ultimately exits back to space as long-wave thermal radiation (see Figure 2.2). Atmospheric “greenhouse gases” generally absorb radiation in the 8 to 20 micrometer regions. Their increased concentration in the atmosphere disrupts the Earth-sun-space energy balance by reducing the energy output from the Earth and its atmosphere. Since the energy entering remains constant, the average temperature of the Earth increases. Subsequently, when concentrations of greenhouse gases in the atmosphere become constant at new values, the energy balance is re-established but at some higher average temperature of the Earth. Addressing climate change will involve both mitigation efforts and adaptation efforts aimed at living with the effects. The main greenhouse gas, carbon dioxide, is a direct product of the combustion of carbon containing fuels. Thus, renewable energy options that avoid fossil fuel use are needed to mitigate climate change, as they generally have much lower emissions of greenhouse gases, permitting non-fossil energy to provide sustainable energy services and thereby to contribute to energy sustainability and sustainable development (Rosen et al. 2008, Rosen 2017, 2020, 2021a). Advanced tools like exergy analysis can also be used for addressing climate change (Rosen 2021b). Since the early 1800s, it is known that various atmospheric gases, acting like the glass in a greenhouse, transmit incoming sunlight but absorb or reflect outgoing infrared radiation, thus raising the average air temperature Chp. 2 at the Earth’s surface. Carbon dioxide is clearly the most influential greenhouse gas because of its high level of emissions. The most compelling evidence we have for Solar energy

Thermal energy Figure 2.2.  Earth-sun-space energy balance with the two primary energy flows. Solar energy is input to the system containing the Earth and its atmosphere. Thermal energy is emitted to space at longer wavelengths than the incoming solar radiation. Figure 2.2. Earth-sun-space

36  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence

Figure 2.3.  Paleoclimatic data from ice cores shows recent increases in carbon dioxide and methane. The temperature, though increasing, has not yet reached record levels but will likely do so by midcentury (Kutscher 2007).

climate change lies in the so-called paleoclimatic data obtained from ancient ice core samples in Greenland and Antarctica. By analyzing air bubbles that were trapped in the ice when it formed, scientists can determine the content of greenhouse gases and even the average temperature at each point in time. Figure 2.3 shows that over the past 420,000 years, the CO2 content in the atmosphere of about global is measured by tonne has varied cyclically (EB) between 180warming ppm and 290 ppm by volume with a period of about 100,000 years in conjunction with variations in Earth’s orbit. Earth’s temperature has closely followed the greenhouse gas concentration. Paleoclimatic data

(tonnes) × potency n Around 1850, when the

factor (pf)i,n atmospheric CO2 level was about 280 ppm, the level began to increase and in 2022 reached the value of 420 ppm, which indicates a considerable increase over the prevalue. This increase in temperature CO2 from the ground and m seawater so the is the ith environmental burden, ncan is release the substance index and i industrial two effects reinforce each other. The possible consequences of these increases include ice melts, sea level rises, and severe storms because of the additional energy in the atmosphere. The resulting water and ground absorb more sunlight, thus exacerbating global warming and land erosion. 2.4.2  Environmental Burden The environmental burden (EB) of global warming is measured by tonne/year carbon dioxide 7 of substances is estimated by adding their equivalent. The EB caused by the emission of a range masses multiplied by a weighted factor known as the “potency factor,” as follows (IChemE 2002): EBi = ∑mn (tonnes) × potency factor (pf )i,n (2.7) where EBi is the ith environmental burden, n is the substance index and m is the mass amount of that substance. Each substance has different potency factors for different environmental burdens, such

Environmental Sustainability  37

as global warming burden, atmospheric acidification burden, human health burden, ozone depletion burden, and photochemical ozone burden.

2.4.3  Global Warming Potential Global warming potential (GWP) is estimated based on how long GHGs remain in the atmosphere and how effectively they trap heat. GWP measures how much energy the emissions of 1 ton of gas would trap over a given period, relative to the emissions of 1 ton of carbon dioxide. The period is usually 100 years. GWP compares emission levels of various GHGs. Carbon dioxide remains for a long time and has a GWP of 1 as a reference substance. Methane has a GWP of 28–36 over 100 years, indicating that methane can trap more energy than carbon dioxide for equivalent masses. Nitrous oxides have a GWP of 265–298 for a 100-year period. Chlorofluorocarbons, hydrochlorofluorocarbons, hydrofluorocarbons, perfluorocarbons, and sulfur hexafluoride gases have very high values of GWP. Specifically, chlorofluorocarbon-12 has a global warming potential of 8500, while chlorofluorocarbon-11 has a GWP of 5000. Hydrochlorofluorocarbons and hydrofluorocarbons have the values of GWPs ranging from 93 to 12,100 (IChemE 2002). GWP can be determined based on data from the three standards: the IPCC’s 2nd (SAR) and 4th (AR4) Assessment Reports, and the U.S. EPA’s proposed rules (CO2E-US) from 2009 (Demirel 2021). Potency factors for global warming are listed in Table 2.3. Table 2.3.  Potency factors for global warming (IChemE 2002). Substance Carbon dioxide Carbon monoxide

Potency factor, pf 1 3

Carbon tetrachloride

1,400

Chlorodifluoromethane, R22

1,700

Chloroform

4

Chloropentafluoroethane, R115

9,300

Dichlorodifluoromethane, R12

8,500

Dichlorotetrafluoroethane, R114

9,300

Difluoroethane Hexafluoroethane

140 9,200

Methane

21

Methylene chloride

9

Nitrous oxide

310

Nitrogen oxides (NOx)

40

Tetrafluoroethane, R125

2,800

Perfluoro methane

6,500

Tetrafluoroethene

1,300

Trichloroethane (1,1,1)

140

Trichlorofluoromethane, R11

4,000

Trichloro trifluoroethane, R113

5,000

Trifluoroethane, R143a

3,800

Trifluoroethane, R23

11700

Volatile organic compounds, VOCs

11

38  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence

2.4.4 Acidification Acidification potential refers to the potential of pollutants to acidify natural or artificial substances. These substances include building materials and structures as well as ecosystems with surface and ground waters, soil, and biological organisms. Acidification effects can move through the atmosphere and deteriorate buildings, metal structures and fabrics. Acidifying pollutants typically are anthropogenic in origin. Acidic gases derived from of NOx emissions are strongly related to road transport while SO2 emissions mainly are derived from electric power generation, residential heating, and industrial energy utilization. Some ecosystems are vulnerable to varying degrees to damage from excessive acidity. Atmospheric impact Atmospheric acidification The environmental burden of atmospheric acidification is measured in tonne/year sulfur dioxide equivalent (Table 2.4). The potential of certain gaseous emissions to form acid precipitation and acids is the potency factor for atmospheric acidification. The potential factors are based on a 100-year integrated time. Table 2.4.  Potency factors for atmospheric acidification (IChemE 2002). Substance

Potency factor, pf

Sulfur dioxide, SO2

1

Ammonia, NH3

1.88

Hydrochloric acid, HCl

0.88

Hydrofluoric acid, HF

1.66

Nitrogen dioxide, NO2

0.7

Sulfuric acid mist, H2SO4

0.65

Aquatic impact Aquatic acidification Environmental burdens for emissions to water addresses aquatic acidification. The potency factor is the mass of hydrogen ions released by unit mass of acid. The unit of EB is ton/year of H+ ions released (see Table 2.5). Table 2.5.  Potency factors for aquatic acidification (IChemE 2002). Substance

Potency factor

Sulfuric acid

0.02

Hydrochloric acid

0.027

Hydrogen fluoride

0.05

Acetic acid

0.02

2.4.5  Ozone Formation and Destruction High-energy ultraviolet (UV) photons can react with oxygen, splitting it into highly reactive oxygen atoms. These are free radicals in the stratosphere and can combine with oxygen molecules to form ozone. Each ozone molecule can absorb a UV photon with a wavelength of less than 320 nm and prevents potentially harmful UV rays from reaching Earth’s surface. High-energy UV photons in the stratosphere split chlorine radicals from chlorofluorocarbons (CFCs) by breaking their C-Cl bond. The freed chlorine radicals are very reactive and can destroy ozone by converting it to diatomic oxygen. Some ozone depleting substances are hydrochlorofluorocarbons, chlorofluorocarbons, and halons.

Environmental Sustainability  39

Ground-based ozone is formed by chemical reactions involving atmospheric oxygen (O2) with emissions of non-methane hydrocarbons such as ethylene and butane and/or nitrogen oxides (NOx) (primarily NO and NO2). These emissions stem mainly from automotive transport and transport via other fossil fuel-fired devices and electricity generation based on fossil fuel combustion. Ground-based ozone formation can be controlled by constraining atmospheric emissions of non-methane hydrocarbons and/or nitrogen oxides in general and especially from fossil fuel-fired transportation and electricity generation. Stratospheric ozone depletion The potency factor is based on the ozone depletion potential in the upper atmosphere relative to the chlorofluorocarbon CFC-11. The unit of EB is tonne/year CFC-11 equivalent (CFC-11 trichlorofluoromethane) (see Table 2.6).

2.4.6  Smog Formation Smog is a significant concern as it can cause health and crop damage. Ozone (O3) is the main constituent of photochemical smog, which is produced at low levels in the atmosphere and near the ground. An assessment of smog formation is beneficial for reducing its adverse effects. Volatile organic compounds (VOCs) form radicals that can convert NO to NO2 without ozone depletion leading to an increase in the ratio of concentrations [NO2]/[NO] and an increase in ozone. The main relevant chemical reactions follow: VOC + OH → RO2 + others

(2.8)

RO2 + NO → NO2 + radicals

(2.9)

radicals → OH + others

(2.10)

The influence of VOCs on ozone levels depends on their hydroxyl radical (OH) reaction kinetics. Simple smog formation potential indexes may be based on volatile organic hydroxyl radical rate constants. The smog formation potential (SFP) may be based the maximum incremental reactivity (MIR) [gO3/g VOC]: SFPi = MIR/MIRROG

(2.11)

where MIRROG is the average value for background reactive organic gases. The total smog formation potential is the sum of the MIRs and emission rates of smog-forming chemicals. Thus, the smog formation index can be written as = I SF

∑ (SFP × m ) (2.12) i

i

i

Photochemical ozone formation Photodissociation of NO2 causes ozone formation in the lower atmosphere, according to the following reactions: NO2 + hν → O(3P) + NO

(2.13)

O( P) + O2 + X → O3 + X

(2.14)

O3 + NO → NO2 + O2

(2.15)

3

where X is nitrogen or molecular oxygen. This ozone formation cycle in a photo stationary state is controlled by the NO2 photolysis rate and ratio of concentrations of [NO2] to [NO] (Arnold and Comes 1979). The potency factors for photochemical ozone formation are obtained from the potential of substances to create ozone photochemically. The unit of environmental burden is tonne/year ethylene equivalent (see Table 2.7).

40  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence Table 2.6.  Potency factors for ozone depletion (IChemE 2002). Compound

Potency factor

CFC-115

0.6

CFC 111, CFC 112

1.0

CFC 112, CFC 213; CFC 214

1.0

CFC–215; CFC 216; CFC 217

1.0

Halon-1211

3.0

Halon-1301

10.0

Halon-2402

6.0

Carbon tetrachloride

1.1

1,1,1-Trichloroethane

0.1

Methyl bromide

0.7

HCFC-21

0.04

HCFC-22

0.055

HCFC-31

0.02

HCFC-121

0.04

HCFC-122

0.08

HCFC-123

0.02

HCFC-124

0.022

HCFC-131

0.05

HCFC-132

0.05

HCFC-133

0.06

HCFC-141

0.07

HCFC-141b

0.11

HCFC-142

0.07

HCFC-142b

0.065

HCFC-151

0.005

HCFC-221

0.07

HCFC-222

0.09

HCFC-223

0.08

HCFC-224

0.09

HCFC-225

0.07

HCFC-226

0.1

HCFC-231

0.09

HCFC-232

0.1

HCFC-233

0.23

HCFC-234

0.28

HCFC-235

0.52

HCFC-241

0.09

HCFC-242

0.13

HCFC-243

0.12

HCFC-244

0.14

HCFC-252

0.04

HCFC-253

0.03

Environmental Sustainability  41 Table 2.7.  Potency factors for petrochemical ozone depletion (IChemE 2002). Substance Methane

Potency factor 0.034

Ethane

0.14

Propane

0.411

n-Butane

0.6

i-Butane

0.426

n-Pentane

0.624

i-Pentane

0.598

n-Hexane

0.648

2-Methylpentane

0.778

3-Methylpentane

0.661

2,2-Dimethylbutane

0.321

2,3-Dimethylbutane

0.943

n-Heptane

0.77

2-Methylhexane

0.719

3-Methylhexane

0.73

n-Octane

0.682

2-Methylheptane

0.694

n-Nonane

0.693

2-Methyloctane

0.706

n-Decane

0.680

2-Methylnonane

0.657

n-Undecane

0.616

n-Dodecane

0.577

Cyclohexane

0.595

Methyl cyclohexane

0.732

Ethylene

1.0

Propylene

1.08

1-Butene

1.13

2-Butene

0.99

2-Pentene

0.95

1-Pentene

1.04

2-Methylbut-1-ene

0.83

3-Methylbut-1-ene

1.18

2-Methylbut-2-ene

0.77

Butylene

0.703

Isoprene

1.18

Styrene

0.077

Acetylene

0.28

Benzene

0.334

Toluene

0.771

o-Xylene

0.831

m-Xylene

0.08

p-Xylene

0.948

Ethylbenzene

0.808

n-Propyl benzene

0.713 Table 2.7 contd. ...

42  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence ...Table 2.7 contd. Substance

Potency factor

i-Propyl benzene

0.744

1,2,3-Trimethylbenzene

1.245

o-Ethyl toluene

0.846

m-Ethyl toluene

0.985

p-Ethyl toluene

0.935

3,5-Dimethylethylbenzene

1.242

3,5-Diethyltoluene

1.195

Formaldehyde

0.554

Acetaldehyde

0.65

Propionaldehyde

0.755

Butyraldehyde

0.77

i-Butyraldehyde

0.855

Valeraldehyde

0.887

Methylethylketone

0.511

Methyl-i-butyl ketone

0.843

Cyclohexanone

0.529

Methyl alcohol

0.205

Ethyl alcohol

0.446

i-Propanol

0.216

n-Butanol

0.628

i-Butanol

0.591

Diacetone alcohol

0.617

Cyclohexanol

0.622

Methyl acetate

0.046

Ethyl acetate

0.328

n-Propyl acetate

0.481

i-Propyl acetate

0.291

n-Butyl acetate

0.291

Formic acid

0.003

Acetic acid

0.156

Propionic acid

0.035

Butyl glycol

0.629

Propylene glycol methyl ether

0.518

Dimethyl ether

0.263

Methyl-t-butyl ether

0.268

Methyl chloride

0.035

Methylene chloride

0.031

Methyl chloroform

0.002

Tetrachloroethylene

0.035

Trichloroethylene

0.075

Vinyl chloride

0.272

1,1-Dichloroethylene

0.232

cis 1,2-Dichloroethylene

0.172

trans 1,2-Dichloroethylene

0.101

Nitrogen dioxide

0.028

Sulphur dioxide

0.048

Carbon monoxide

0.027

Environmental Sustainability  43

2.4.7  Human Health Carcinogenic effects can be used for human health assessments. Potency factor values for this category are listed in Table 2.8 and are derived from the reciprocal of values of Occupational Exposure Limit (OEL). The OEL for benzene has been chosen as the normalizing factor for this category, so: Potency factor of substance = (OEL benzene/OEL substance)

(2.16)

Chemicals with an OEL greater than 500 mg/m3 have a minimal impact on the total weighted impact. The unit of environmental burden is tonne/y benzene equivalent. Table 2.8.  Human health potency factors (IChemE 2002). Compound

Potency factor

Acrylamide

53.3

Acrylonitrile

3.6

Arsenic & compounds except arsine, as As

160

Azodicarbonate

16

Benzene

1

Bis (chloromethyl) ether

3,200

Buta-1,3-diene

0.73

Cadmium oxide fume

640

Carbon disulphide

0.5

1 Chloro-2,3-epoxypropane

8.4

1,2-Dibromoethane

4.1

1,2-Dichloroethane

0.76

Dichloromethane

0.05

2-2’-Dichloro-4,4’-methylene dianiline (MbOCA)

3,200

Diethyl sulphate

50

Dimethyl sulphate

3.8

2-Ethoxyethanol

0.43

2-Ethoxyethyl acetate

0.3

Ethylene oxide

1.7

Formaldehyde Hydrazine

6.4 533.3

Iodomethane

1.3

Maleic anhydride

16

2-Methoxyethanol

1

2-Methoxyethyl acetate

0.64

4-4’-methylenedianiline

200

2-Nitropropane

0.8

Phthalic anhydride

4

Polychlorinated biphenyls (PCB)

160

Propylene oxide

1.33

Styrene

0.04

o-Toluidine

18

Triglycidyl isocyanurate (TGIC)

160

Trimellite anhydride 400

400

Vinylidene chloride

0.4

44  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence

2.4.8 Toxicity To address ecotoxicity to aquatic life, the potency factor of metals is used as the reciprocal of the Environmental Quality Standard (EQS) divided by the reciprocal of the EQS of copper. The unit of EB is ton/year copper equivalent (see Table 2.9). Potency factors of other substances (see Table 2.10) are equal to the reciprocal of the Environment Quality Standard (EQS) divided by the reciprocal of the EQS of formaldehyde. The unit of Environmental Burden is ton/year formaldehyde equivalent. Table 2.9.  Potency factor for ecotoxicity to aquatic life (values for sea water conditions) (IChemE 2002). Substance

Potency factor

Arsenic

0.2

Cadmium

2.0

Chromium

0.33

Copper

1

Iron

0.005

Lead

0.2

Manganese

0.1

Mercury

16.67

Nickel

0.17

Vanadium

0.05

Table 2.10.  Potency factors for ecotoxicity to aquatic life for other substances (IChemE 2002). Substance

Potency factor

Ammonia

0.24

Benzene

0.17

Carbon tetrachloride

0.42

Chloride

0.5

Chlorobenzene

1

Chloroform

0.42

Cyanide

1.0

1,2-Dichloroethane

0.5

Formaldehyde

1

Hexachlorobenzene 1

166.67

Hexachlorobutadiene

50

Methylene chloride

0.5

Nitrobenzene

0.25

Nitrophenol Toluene Tetrachloroethylene

0.5 0.125 0.5

Trichloroethylene (TRI)

0.5

Xylenes

0.17

Environmental Sustainability  45

Impact to land Two major impacts to land are the effects of hazardous waste and of nonhazardous waste. A modular catalyst-impregnated ceramic filter can remove particulate matter, nitrogen oxides (NOx) and sulfur oxides (SOx) that can cause health problems, and acid rain, which can negatively impact land and water quality. Common pollution-control equipment mainly consists of baghouse fabric filters and electrostatic precipitators. NOx includes nitrogen dioxide and nitric oxide. The following reactions show the conversion of nitrogen oxides (NOx) to nitrogen: NO + NO2 + 2NH3 → 2N2 + 3H2O (fast)

(2.17)

4NO + 4NH3 + O2 → 4N2 + 6H2O (slow)

(2.18)

2NO2 + 4NH3 + O2 → 3N2 + 6H2O (slow)

(2.19)

Wet flue gas desulfurization uses hydrated lime to remove SOx, according to the following reaction: Ca(OH)2 + SO2 + 1/2O2 → CaSO4 + H2O (2.20)

2.4.9 Eutrophication Eutrophication is the potential impact of over fertilization of water and soil, which can result in increased growth of biomass. The species considered are those responsible for eutrophication. The unit of EB is ton/year phosphate equivalent (see Table 2.11). Table 2.11.  Potency factors for eutrophication (IChemE 2002). Substance

Potency factor

NO2

0.2

NO

0.13

NOx

0.13

Ammonia

0.33

Nitrogen

42

PO4 (III-) Phosphorus

1 3.06

Aquatic oxygen demand The Stoichiometric Oxygen Demand (StOD) provides a potency factor representing the potential of emissions to remove dissolved oxygen that would otherwise support aquatic life. An alternative potency factor is the Chemical Oxygen Demand (COD). StOD is expressed as tonnes of oxygen required per tonne of substance. The unit of EB is tonne/year oxygen (see Table 2.12). Stoichiometric oxygen demand (StOD) From the knowledge of the chemical structure of a molecule, we can calculate the empirical formula as follows: CcHhNnClCINaNaOoPpSs. Then the StOD can be evaluated in tonne O2 per tonne of substance as follows: StOD = 16(2c + 0.5(h – Cl) + 2.5n + 3s + 2.5p + 0.5Na – o)/Molecular Weight

(2.21)

Here it is assumed that nitrogen is converted to a nitrate ion (NO3–) and carbon is converted to CO2, hydrogen (H) is converted to H2O, phosphorus (P) is converted to P2O, sodium (Na) is converted to Na2O, sulfur (S) is converted to SO2 and halides (represented by Cl) are converted to their respective acids. The compounds described after oxidation are those specified by the international convention for calculating oxygen demand (IChemE 2002).

46  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence Table 2.12.  Potency factor for aquatic oxygen demand (IChemE 2002). Substance

Potency factor

Acetic acid

1.07

Acetone

2.09

Ammonium nitrate in solution

0.8

Chlorotrifluoroethane

0.54

1,2–Dichloroethane (EDC)

0.81

Ethylene

1

Ethylene glycol

1.29

Ferrous ion

0.14

Methanol

1.5

Methyl methacrylate

1.5

Methylene chloride

0.47

Phenol

0.238

Vinyl chloride

1.28

Example: Calculation of the stoichiometric oxygen demand Estimate the values of SrtOD for acetic acid, phenol, and ammonium ion. Solution: Using Equation (2.18) for acetic acid CH3COOH with a molecular weight of 60, we have StOD = 16(2×2 + 0.5×4 – 2)/60 = 1.07 ton equivalent O2 per ton equivalent acetic acid Similarly, for phenol (C6H5OH) with a molecular weight of 94 StOD = 16(2×6 + 0.5×6 – 1)/94 = 2.38 tonne equivalent (te) O2 per tonne equivalent of phenol For ionic species, the charge of the ionic unit is considered. For the ammonium ion (NH4+), for example, we remove an H+ ion and calculate for NH3, so that the ionic balance is not disturbed, and then: StOD = 16(0.5×3 + 2.5×1)/17 = 3.76 te O2 per tonne equivalent of ammonia = 3.56 te O2 per tonne equivalent of ammonium ion

2.4.10  Habitat Destruction Habitat destruction involves the loss of natural habitats, either by disrupting them, or by diverting them to other uses. A damaged habitat may be no longer useful and productive. Examples of causes of habitat destruction include deforestation as well as expansions in agricultural an urban land use. Habitat destruction often results in reductions in biodiversity, sometimes due to the loss of the large and continuous land tracts required for some species to thrive and be vibrant. The reduction or loss of flora and fauna species due to anthropogenic activity can raise numerous concerns, such as reductions in the genetic pool and disruptions of food cycles (Watts et al. 2020).

2.4.11  Resource Depletion The indicators of resource depletion assess the environmental impact of resource usage, as well as emissions, effluents, and wastes. There are various metrics for resource depletion, some of which for major depletions are described below (Demirel 2021, Rosen 2021a): • Energy: Some metrics in energy sources are: a) total net primary energy usage rate = imports – exports (GJ/year) b) total net primary energy sourced from renewables, as a percentage of total primary energy use (%) c) total net primary energy usage per kg product (kJ/kg)

Environmental Sustainability  47

• Material (excluding fuel and water): Some metrics for material sources are: a) total raw materials used, including packaging (ton/year) b) raw material recycled from other company operations (ton/year) c) raw material recycled from consumers (tonne/year) d) raw material used which poses health, safety, or environmental hazard (ton/year) e) total raw materials used per kg product (kg/kg) f) fraction of raw materials recycled within a company (kg/kg) g) fraction of raw materials recycled from consumers (kg/kg) f) hazardous raw material per kg product (kg/kg) • Water: Some metrics for water sources are: a) water used in cooling (tonne/year) b) water used in processes (tonne/year) c) other water used (tonne/year) d) water recycled internally (tonne/year) e) net water consumed = total water used – recycled water (tonne/year) f) net water consumed per unit mass of product (kg/kg) • Land: Some metrics for land sources are: a) land occupied by an operating unit in m2 (including land needed for all activities), b) other land affected by the unit’s activities in m2 (land used in mining raw material or in dumping waste product) to total land in m2 c) land restored to original condition (m2/year) d) total land occupied (m2/$/year) e) rate of land restoration (restored per year/total) (m2/year)/m2. The areas of land occupied and affected are those at the start of the reporting period, and the land restored is that area restored during the reporting period. We may measure resource depletion by the Depletion number Dp, which is a nondimensional indicator of ExDp per unit consumption ExC. On a rate base, we can write (Demirel 2021):    Ex  (1 − Ω RU )  Ex  (1 − ΩVU )  Ex Dp =1 + Dsl + ψ  − 1 + TV  − 1 (2.22) Dp =    Ex Ex Ex η ηVU C C RU C    

. where ExDsl is the exergy dissipation rate, ΩRU and ηRU are the renewable . exergy fraction and transfer efficiency for the recovered resource upgrade process, respectively, ExTV is the exergy transfer rate to the nonrenewable source, and ΩVU and ηVU are the renewed exergy fraction and transfer efficiency, respectively, for the nonrenewable resource upgrade process. The depletion number provides a measure of system progress and is a function of the following indicators: • The exergy cycling fraction ψ, which is a measure of recycling that accounts for both the throughput and quality change aspects of resource consumption and upgrading. • The exergy efficiency. • The renewable exergy fraction Ω. Boundary conditions determine which resources and processes constitute an industrial system. • Boundary conditions. Spatial boundary conditions are mainly geographical and resource-specific, while temporal boundary conditions define the scope of time for the exergy transfer and loss in processes.

48  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence

2.4.12  Particulate Matter Particulate matter contains a mixture of solid particles, liquid droplets, dust, dirt, soot, and smoke, ranging from 2.5 to 10 microns in size, as well as dioxins and heavy metals. Toxic and combustible dust endangers air quality and can create fire and explosion hazards. For example, a dust explosion can occur when a confined and concentrated combustible dust cloud meets an ignition source such as a welding flame. The primary explosion is the first point where an explosion occurs and is usually an isolated incident. A following secondary explosion after disturbing the dust collected may create a far more dangerous explosion. Installing a dust collection system can prevent airborne dust from building up in the work environment, and on electrical and other equipment. PM is commonly removed using a baghouse fabric filter, although scrubbers and electrostatic precipitators can also be used. Catalyst ceramic filters (CCFs) can remove these pollutants efficiently in many sectors including hazardous and industrial waste incineration. Baghouses and CCFs can capture over 99.9% of particulate matter (Figure 2.4). A CCF has a more compact layout compared with other pollution control techniques, such as electrostatic precipitators and baghouses with fabric filters (Naqvi et al. 2021). Catalyst ceramic filters are resistant to high temperature and corrosion, and they typically have low cost with less maintenance and utilities. Dry calcium or sodium-based sorbents are injected into the flue gas upstream of a CCF for SO2 removal. For mercury removal active carbon is injected. CCF consists mainly of micrometer-sized catalyst particles of Al2O3, SiO3, TiO2, V2O5, and WO3 distributed across the entire wall thickness. The typical life of a CCF is 5 to 10 years (Naqvi et al. 2021). Particulate matter HCL SOx NOx Dioxins

Less than 2mg/Nm3 outlet particulate matter Up to 97% HCL removal Up to 95% SOx removal Up to 95% NOx removal

Catalyst ceramic filter

Alkali sorbent: Ammonia Urea Lime Air Sodium bicarbonate Particulate matter HCL SOx NOx Dioxins ~650 oF

Particulate matter HCL SOx NOx Dioxins ~400 oF

Scrubber

Electrostatic precipitator

Alkali sorbent: Lime Sodium bicarbonate

SOx Dust

Baghouse Fabric filter fabric filter Alkali sorbent: SOx Dust Lime Sodium bicarbonate

Reheating

SCR

Ammonia Urea Air

NOx

SCR

~650-850 oF

Ammonia Urea Air

NOx

Figure 2.4.  Flow diagram for the operation of selected pollution control technologies: catalyst ceramic filter (top), a scrubber with electrostatic precipitator (middle), and baghouse with fabric filter (bottom) (Naqvi et al. 2021). 4.

Environmental Sustainability  49

2.5  Carbon Capture Fossil fuel combustion is the primary source of carbon dioxide emissions. There are many processes for carbon capture and storage/sequestration (CCS) and carbon capture and utilization (CCU), as shown in Figure 2.4. Power plants, oil refineries, biogas sweetening plants as well as production facilities for ammonia, ethylene oxide, cement and iron and steel, are the main industrial sources of CO2 (see Figure 2.4). Various categories of CO2 capture options exist: post-conversion, pre-conversion, and oxy-fuel combustion. Another option involves biomass fixation of CO2 like cultivation of microalgae. Microalgae cultivation involves the use of CO2, light, nutrients, and water for converting algae to proteins, biooils, fatty acids, carbohydrates, and chemicals. Post-conversion capture using the solvent monoethanolamine (MEA) is widely used in the power generation sector. For example, mineral carbonation to produce MgCO3 can reduce the GHG emission by up to 48% (Demirel 2018, 2021). Carbon capture and utilization To increase the potential of carbon capture and utilization (CCU) research is needed on the development of materials and processes to capture and convert CO2. For example, life cycle assessments (LCAs) have shown that CCU has potential to reduce environmental impacts of CO2-based methanol and formic acid production (Cuellar-Franca and Azapagic 2015). LCA is a technique which identifies holistically environmental impacts of systems and processes, and it can quantify the environmental benefits of CCU. Using rotating packed beds instead of static columns can effectively distribute solvents and enhance the carbon capture process (by up to 75%), allowing for a smaller footprint and lower capital expenditures (Sancheza et al. 2018). The oil and gas sectors are seeking to make changes that improve efficiency, reduce GHG emissions, comply with environmental regulations, and move toward sustainable operations. Companies in these sectors also are exploring how renewable technologies and energy storage can be leveraged to support decarbonization initiatives, while maintaining plant availability and uptime, low maintenance, acceptable operating expenses (OPEX) and capital expenses (CAPEX), and regulatory compliance. For instance, a power plant may burn palm kernel shells as its primary fuel source for biomass energy generation and be equipped with large-scale carbon capture and storage capabilities. Unique fuel gas desulfurization technology as a pretreatment device can be used for the separation and recovery of CO2. Significant potentials for emission reductions are provided by carbon capture and sequestration/ storage, enhanced hydrocarbon recovery (EHR), and carbon dioxide utilization (CDU). While CCS can impact GHG emissions, CDU can have a comparable effect whilst generating income. EHR ultimately increases net emissions. Ideally, a CDU plant would be situated close to the capture plant to reduce costs of hydrogenation of CO2 with green hydrogen to hydrocarbons. This can be accomplished with a catalytic Fischer-Tropsch-type reduction. The average GWP for pulverized coal (PC) power plants without CCS is 876 kg CO2eq./MWh while for the same plant with post-conversion capture via methyl ethylamine (MEA) the average value is 203 kg CO2eq and for oxy-fuel combustion it is 154 kg CO2eq. Therefore, the greatest GWP reductions (up to 82%) can be achieved by oxy-fuel combustion in PC and integrated gasification combined cycle (IGCC) plants. CO2 can be used as a feedstock in a wide variety of products including urea, organic chemicals, diesel and aviation fuel, methane (synthetic natural gas), and some polymers. The mineralization of waste, such as fly ash, bauxite, and steel slag, provides long-term CO2 sequestration and construction materials may reverse the adverse effects of the mining of olivine or serpentine (Armstrong and Styring 2015, Demirel 2018). Global warming potential The global warming potential (GWP) of emissions can be reduced by 63–82% by oxy-fuel combustion for pulverized coal and IGCC power plants. Mineral carbonation can reduce the GWP by

50  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence 4–48%. Utilizing CO2 for production of the chemical dimethyl carbonate (DMC) reduces the GWP by 4.3 times and ozone layer depletion by 13 times compared to the conventional DMC process. Enhanced oil recovery has a GWP 2.3 times lower compared to discharging CO2 to the atmosphere, but acidification is three times higher. Capturing CO2 by microalgae to produce biodiesel fuel has a GWP 2.5 times higher than that for diesel fuel production from fossils (Cuellar-Franca and Azapagic 2015, Rosen 2021b). Solvent technology for carbon capture CO2 capture processes can be improved by using rate-based absorber-stripper systems with solvents. For this, solvent screening and techno-economic analyses for decarbonization should consider cost optimization after screening the following CO2 capture processes using rate-based absorber-stripper systems with solvents: 1. AMP (2-amino-2-methyl-1-propanol) from a gas mixture of nitrogen and CO2. 2. Diethanolamine (DEA) from a gas mixture of methane, ethane, propane, N2, CO2 and H2S. 3. Mixed DEA and methyl-diethanolamine (MDEA) aqueous solution from a gas mixture of CO2, methane, ethane, propane, n-C4H10, i-C4H10, n-C5H12 and N2. 4. Diglycolamine (DGA) from a gas mixture of N2, O2, CO2 and H2O. 5. Potassium carbonate (K2CO3) from a gas mixture of N2, H2O, CO2, and H2S. 6. Aqueous MDEA from a gaseous mixture of methane, CO2, H2S and H2O. 7. Aqueous monoethanolamine (MEA) from a gaseous mixture of N2, O2, CO2 and H2O. 8. Mixed MEA and MDEA aqueous solution from a gaseous mixture of CO2 and N2. 9. Sodium hydroxide (NaOH) from a gaseous mixture of N2, O2, H2O, and CO2. 10. Ammonia (NH3). 11. Aqueous piperazine (PZ) (cyclic amine) solution. 12. Aqueous solutions of mixed PZ and MEA. 13. Mixed solvent composed of sulfolane, diisopropanolamine (DIPA) and water. 14. Mixed solvent composed of sulfolane, MDEA and water. 15. Aqueous triethanolamine (TEA) solution from a gaseous mixture of hydrogen and CO2. Sequestration of biogenic CO2 is an important yet immature technique for climate change mitigation. Carbon capture and sequestration of biogenic CO2 from fermentation emissions leads to 60% capture and the CO2 can be compressed for pipeline transport for under $25/tCO2. The rate-based carbon capture model provides a rate-based rigorous simulation of the process using electrolyte thermodynamics and solution chemistry, reaction kinetics for the liquid phase reactions, rigorous transport property modeling, rate-based multi-stage simulation, and heat and mass transfer correlations accounting for column specifics and hydraulics.

2.6 Decarbonization Accelerating decarbonization of the energy system requires key technological and socioeconomic goals that must be achieved for attaining net-zero carbon emissions by 2050. Advanced coal conversion consists of thermal processing to upgrade high-moisture, low-rank coals to Syncoal products with high heating values and low sulfur contents. Firstly, in a fluidized-bed reactor, surface moisture is removed by heating with hot combustion gas. Later, chemically bound water, carboxyl groups, volatile sulfur compounds, and a small amount of tar are removed at 600°F in a second reactor. Electrification of vehicles, home heating, aviation, shipping, and steel, cement, and chemicals manufacturing need further innovation and advances to achieve cost-effective decarbonization. Decreases in the costs of renewable electricity and batteries will help in achieving affordable

Environmental Sustainability  51

net-zero emitting energy systems and will benefit all communities. Decision-makers should consider policies for net-zero emissions that are supported by science across many disciplines (Howard and Chp.2017, 2 Sancheza et al. 2018, IEA 2021). Sterner There are several technology goals for decarbonization: (i) produce carbon-free electricity, (ii) electrify energy services in transportation, buildings, and industry, (iii) invest in energy efficiency and productivity, (iv) plan, permit, and build critical infrastructure, and (v) expand the innovation Solar energy toolkit (Cuellar-France and Azapagic 2015). Socioeconomic goals for decarbonization include: (i) strengthen the economy, (ii) promote equity and inclusion, (iii) support communities, businesses, and workers, and (iv) maximize cost-effectiveness.

2.7  Carbon Utilization In a green economy, a CCS retrofit represents a climate change mitigation technology, although it reduces power generation efficiency by around 10%. This can be achieved by adding a chemical conversion process for post capture of CO2 and conversion of it to fuels and chemicals, including methanol, methane, formic acid, urea, and cyclic carbonates (Figure 2.5). Case Study 1: Carbon-neutral fuel directly usable for transportation can be produced by reacting green hydrogen and carbon dioxide as follows: nCO2 + 3nH2 → CnH2n + 2nH2O (2.23) where n is the number of carbon atoms in a long-chain or cyclic hydrocarbon. The only other inputs required are electricity and the widely available chemical, sodium hydroxide. Case Study 2: Waste CO2 conversion to cyclic carbonates, which are commercially important. They are currently produced from epoxide and. CO2. Converting waste CO2 to cyclic carbonates offers a potential focus for CCU processes. However, such processes can be expensive and energy intensive. The latter point can lead to increased GHG emissions. Carbon Sources Cement industry Industry

Oil refinery

Fossil fuels

Iron&steel industry

Biogas sweetening

Postconversion

Preconversion

Oxy-fuel combustion

Solvent absorption

Physical absoption

Solid absobents

Chemical absorption

Combustion with Pure pure oxygen

Memrane capture

Storage options Geological storage

Chemical industry

Chemical looping Chemical looping reforming

Conversion options Chemical feedstock Algae cultivation Minearal carbonation Enhanced oil recovery

Figure 2.5.  Processes for carbon capture and storage/sequestration (CCS) and carbon capture and utilization (CCU).

52  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence

2.8  Environmental Cost of Carbon Emissions Integrated assessment models define baseline emission trajectories by projecting future economic growth, population, and technological changes, per metric ton increase in CO2 emissions to the baseline emissions trajectory. The increase in global average temperature rise, due to GHG emissions, is translated to physical impacts and monetized damages, which include changes in net agricultural productivity, energy use, flood risk, and health risk (Gillingham and Stock 2018, Auffhammer 2018, IPI 2019, Rosen 2021a, 2021b). The methodology uses various socioeconomics and emissions projections: • The socioeconomic module uses statistical methods and expert judgment for projecting distributions of economic activity, population growth, and emissions into the future. • The simple Earth system model predicts the relationships between CO2 emissions, atmospheric concentrations, and global mean surface temperature change and sea level rise. • The damages module improves and updates existing formulations of climate change damages, makes calibrations transparent, presents disaggregated results, and addresses correlations between different formulations based on recent scientific data. • The discounting module incorporates the relationship between economic growth and discounting.

2.8.1  Environmental Impact Assessment The United Nations Environmental Programme (UNEP) defines an environmental impact assessment (EIA) as a tool to identify the environmental, social, and economic impacts of a project and find ways and means to reduce adverse impacts, such as reduced cost and time of project implementation and design, avoided treatment/clean-up costs and impacts of laws and regulations (Morgan 2012, Nordhaus 2017). The fundamental components of an EIA involve the following stages: • Screening projects or developments that require a full or partial impact assessment study. • Identifying (i) potential impacts that are relevant to assess based on legislative requirements, international conventions, expert knowledge, and public involvement, (ii) alternative solutions that avoid, mitigate or compensate adverse impacts on biodiversity, and (iii) terms of reference for the impact assessment. • Assessing and evaluating impacts and developing alternatives. • Reporting the Environmental Impact Statement (EIS) or EIA, including an environmental management plan (EMP), and a non-technical summary. • Reviewing the EIS with the terms of reference and public (including authority) participation. • Decision-making on whether to approve the project or not, and under what conditions. • Monitoring, compliance, enforcement, and environmental auditing as defined in the EMP. Summary Environmental sustainability focuses on emissions and impacts of greenhouse gases and other pollutants and on enhancing or optimizing energy, resource and water usage, waste management, and utilization of renewable fuels and feedstocks. A framework for sustainable engineering requires a strong focus on the environment dimension of sustainable development, including preservation of biodiversity and respect for all life forms regardless of how useful they are to humankind, and staying within the ecosystem’s carrying capacity in terms of resource development and waste assimilation. More generally, environmental sustainability includes socio-ecological areas, like preserving access to ecosystems services essential to health and wellbeing, and eco-economic areas such as developing closed cycles of operation and consumption to minimize waste.

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This chapter covers environmental sustainability and its context, natural earth cycles and greenhouse gases, including carbon tracking. The ecological footprint is covered in depth, through such topics as climate change, environmental burden, global warming potential, acidification, ozone formation and destruction, smog formation, human health, toxicity, eutrophication, habitat destruction, resource depletion and particulate matter. To close, coverage is provided of carbon topics like carbon capture, decarbonization and carbon utilization, as well as the environmental cost of carbon emissions and environmental impact assessments. The bottom line of this chapter is that environmental sustainability is a central and critical part of sustainability, as it represents the ability to maintain and manage the health of the environment and ecosystems, including people and other ecological communities and entities. A key facet of environmental sustainability is the mitigation of climate change, perhaps by limiting the rise in mean global temperature to 1.5°C, which requires quick and decisive action in the near future by the countries of the world. But many other aspects of environmental sustainability also must be considered, including maintaining the carrying capacity of the environment and its ecosystems (i.e., maintaining the ability for resource development and waste assimilation) as well as preserving biodiversity. Environmental impact assessments are important for identifying the environmental, social, and economic impacts of activities and strategies and for developing mean to offset, avoid or reduce adverse impacts. Environmental impact assessments can help enhance designs, reduce requirements of resources, cost, and time in sustainable engineering, avoid treatment/clean-up costs, and establish relevant laws and regulations.

Nomenclature CAPEX Capital expenses CCF Catalyst ceramic filter CCS Carbon capture and storage CCU Carbon capture and utilization CDU Carbon dioxide utilization CFC Chlorofluorocarbon CH4 Methane COD Chemical oxygen demand CO2 Carbon dioxide CO2eq Carbon dioxide equivalent DEA Diethanolamine DMC Dimethyl carbonate Dp Depletion number EB Environmental burden EHR Enhanced hydrocarbon recovery EIA Environmental impact assessment EIS Environmental impact statement EMP Environmental management plan GHG Greenhouse gas GWP Global warming potential H2NO3 Nitric acid IGCC Integrated gasification combined cycle IPCC Intergovernmental Panel on Climate Change LCA Life cycle analysis MEA Monoethanolamine NH3 Ammonia NO Nitrogen monoxide NOx Nitric oxides

54  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence N2O Nitrous oxide OEL Occupational exposure limit OPEX Operational expenses O3 Ozone PC Pulverized coal PM Particulate matter SFP Smog formation potential SO2 Sulfur dioxide StOD Stoichiometric oxygen demand UNEP United Nations Environmental Programme VOC Volatile organic compound

Problems 2.1 Explain how sustainability, efficiency, and the environment are linked? How are they independent? 2.2 What is the relationship between climate change, energy utilization, and sustainability? 2.3 Identify three types of environmental impact and explain how each affects sustainable development. 2.4 Identify two policies that promote the better use of resources to support sustainable development. Explain how these policies support sustainable development. 2.5 Identify the five environmental concerns you consider the most significant threats to humanity and explain each can be mitigated or avoided. 2.6 Which of the following is the better approach to mitigating climate: decarbonization, or carbon capture and sequestration, or carbon utilization? Explain. 2.7 Determine the amount of emissions produced annually, in a typical year, by (a) a coal fired electrical generating station, (b) a natural gas fired boiler that produces hot water for a district heating system, and (c) an automobile fueled by gasoline. Carry out the calculation for each of the five largest emission types, based on the mass of the emissions. 2.8 Assess the environmental sustainability of a specific industry that has not been examined previously in the literature. What are the main environmental issues detracting from environmental sustainability in that industry and how can they be mitigated? 2.9 Identify the sources of energy and material waste in a manufacturing process for computers and propose methods for reducing or minimizing them. 2.10 Most aircraft use gas turbine engines while most cars use internal combustion engines. Identify and compare the greenhouse gas emissions for each application. 2.11 Develop a sustainable or an environmentally beneficial enhancement of a device or method for a specific task (of your choice). Assess the device or method, and compare it to the existing option (prior to your enhancement). Relevant factors should be considered, such as performance, efficiency, energy use, economics, and environmental impact. Utilize appropriate and reliable information sources, possibly including books, journals, conference proceedings, reports, direct communications with relevant experts, and the internet.

Research Projects 2 .1 What are the challenges of carbon capture and utilization? 2.2 How can one assess the cost of carbon emissions?

Environmental Sustainability  55

References Arnold, I. and Comes, F.J. 1979. Temperature dependence of the reactions O(3P) + O3 → 2O2 and O(3P) + O2 + M → O3 + M. Chemical Physics 42(1-2): 231–239. Armstrong, K. and Styring. P. 2015. Assessing the potential of utilization and storage strategies for post-combustion CO2 emissions reduction. Frontiers in Energy Research 3: 8. doi: 10.3389/fenrg.2015.00008. Auffhammer, M. 2018. Quantifying economic damages from climate change. J. Econ. Perspect. 32: 33–52. Cuellar-Franca, R.M. and Azapagic, A. 2015. Carbon capture, storage and utilisation technologies: A critical analysis and comparison of their life cycle environmental impacts, Journal of CO2 Utilization 9: 82–102. Demirel, Y. 2018. Biofuels. Vol. 1., Part B. pp. 875–908. In: Dincer (ed.). Comprehensive Energy Systems. Elsevier, Amsterdam. Demirel, Y. 2021. Energy: Production, Conversion, Storage, Conservation, and Coupling. 3rd ed. Springer, London. ECD (European Commission Decision). 2007/589/EC: Official Journal of the European Commission, 31.8.2007, L229/1. http://eur-lex.europa.eu/LexUriServ/LexUriServ.do?uri=OJ:L:2007:229:0001:0085:EN:PDF. EPA (United States Environmental Protection Agency). Rule E9-5711: Federal Register/Vol. 74, No. 68/Friday, April 10, 2009/Proposed Rules, pp. 16639–16641 (Table C-1. C-2, C-3); http://epa.gov/climatechange/emissions/ downloads/RULE_E9-5711.pdf. Gagnon, B., Leduc, R. and Savardi, L. 2008. Sustainable development in engineering: A review of principles and definition of a conceptual framework. Environmental Engineering Science 26: 1459–1472. Gillingham, K. and Stock, J.H. 2018. The cost of reducing greenhouse gas emissions. J. Econ. Perspect. 32: 53–72. Howard, P.H. and Sterner, T. 2017. Few and not so far between: a meta-analysis of climate damage estimates. Environ. Resour. Econ. 68: 197–225. Howard, P.H. and Sylvan, D. 2020. Wisdom of the experts: Using survey responses to address positive and normative uncertainties in climate-economic models. Climatic Change 162: 213–232. IChemE (Institution of Chemical Engineers). 2002. The Sustainability Metrics, Institution of Chemical Engineers Sustainable Development Progress Metrics recommended for use in the Process Industries. IEA. 2021. Net Zero by 2050: A Roadmap for the Global Energy Sector. Report. International Energy Agency (www. iea.org), July 2021. IPI. 2019. Institute for Policy Integrity. A Lower Bound: Why the Social Cost of Carbon Does Not Capture Critical Climate Damages and What That Means for Policymakers-Institute for Policy Integrity. New York University School of Law (policyintegrity.org). IRENA. 2020. Global Renewables Outlook: Energy Transformation 2050 (Edition: 2020), International Renewable Energy Agency, Abu Dhabi. Kutscher, C.F. (ed.) 2007. Tackling climate change in the U.S.: Potential Carbon Emissions Reductions from Energy Efficiency and Renewable Energy by 2030, American Solar Energy Society. Available at https://ases.org/wpcontent/uploads/2019/01/Tackling_Climate_Change_A.pdf. Morgan, R.K. 2012. Environmental impact assessment: the state of the art. Impact Assessment and Project Appraisal 30(1): 5–14. Naqvi, M.R., Lee, C. and Wang, A. 2021. Controlling air pollution with ceramic catalytic filters. Chemical Engineering April: 40–46. Nordhaus, W.D. 2017. Revisiting the social cost of carbon. PNAS 114: 1518–1523. Rosen, M.A., Dincer, I. and Kanoglu, M. 2008. Role of exergy in increasing efficiency and sustainability and reducing environmental impact. Energy Policy 36(1): 128–137. Rosen, M.A. 2017. Bioenergy and energy sustainability. J. Fundamentals of Renewable Energy and Applications 7(7): 25. Rosen, M.A. 2020. Mitigation of climate change: crucial energy actions. Research Journal of Environmental Sciences 14(1): 1–4. Rosen, M.A. 2021a. Energy sustainability with a focus on environmental perspectives. Earth Systems and Environment 5(2): 217–230. Rosen, M.A. 2021b. Exergy analysis as a tool for addressing climate change. European Journal of Sustainable Development 5(2): em0148. Sancheza, D.L., Johnson, N., McCoy, S.T., Turner, P.A. and Mach, K.J. 2018. Near-term deployment of carbon capture and sequestration from biorefineries in the United States. PNAS 115: 4875–4880. Takai, K. 2019. The nitrogen cycle: a large, fast, and mystifying cycle. Microbes Environ. 34(3): 223–225. Watts, N., Amann, M., Arnell, N., Ayeb-Karlsson, S., Beagley, J., Belesova, K. et al. 2021. The 2020 report of the Lancet Countdown on health and climate change: Responding to converging crises. Lancet 397(10269): 129–170. Zhang, X., Ward, B.B. and Sigman, D.M. 2020. Global nitrogen cycle: critical enzymes, organisms, and processes for nitrogen budgets and dynamics. Chemical Reviews 120: 5308–5352.

Chapter 3

Economic Sustainability INTRODUCTION and OBJECTIVES Economic sustainability refers to the ability of an economy to remain feasible in producing the required results, e.g., goods and services, without unreasonable negative impacts on ecological and social sustainability. That implies economic growth toward increased standards of living level for all. Nations traditionally aim and plan to increase their gross domestic product (GDP) to avoid stagnation and recession. But such an approach is not necessarily sustainable. Stagnation refers to flat GDP, while in recession a nation’s GDP falls for more than two quarters in a row. Bioeconomy and circular economy are the integral parts of economic sustainability. This chapter discusses the bioeconomy, circular economy, and green economy with some case studies on hydrogen and methanol economies. The economic cost of carbon emissions is also discussed. The main objectives of the chapter are to enhance understanding of: • Economic sustainability • Circular Economy • Bioeconomy

3.1  Economic Sustainability Decoupling environmental degradation and economic growth may lead to economic sustainability. The economic importance of nature is made clear by using the expression ecosystem services to highlight the market relevance of an increasingly scarce natural world that can no longer be regarded as both unlimited and free. In general, as a commodity becomes scarce, the cost increases. The idea of sustainability as a business opportunity has led to the formation of organizations such as the Sustainability Consortium of the Society for Organizational Learning, the Sustainable Business Institute, and the World Council for Sustainable Development. Research focusing on progressive corporate leaders who have embedded sustainability into commercial strategies has yielded a leadership competency model for sustainability. Economic sustainability is the ability of an economy to remain feasible in producing the required economic outputs, e.g., goods and services, without unreasonable negative impacts on ecological and social sustainability. Climate change-based natural disasters and resource depletion may lead to global shifts toward sustainability (Geissdoerfer et al. 2016, Korhonen et al. 2018). The increased awareness of sustainability in economies, businesses and society has reached customers.

Economic Sustainability  57

Social 1D Socio-Economic

Socio-Ecologial 2D

Environment 1D

3D Sustainable Eco-Economic 2D

2D

Economic 1D

Figure 3.1.  Sustainability at the confluence of its three dimensions.

A framework for economic sustainability would be fall within the economic dimension of Socioeconomic goals sustainable development (Gagnon et al. 2008), and it would encompass at least the following areas (Figure 3.1): Economy: Maintain a positive genuine long-term investment considering all types of capital. Socio‑economics (2D): • Know your needs andcomplemented focus on achieving individuals. by them for larger number of-ef spending morerelated than tonecessary to achieve an policies. • Allocate fairly the benefits and costs economic activity and public Eco‑economics (2D): • Develop closed cycles of operation and consumption to minimize waste. • Reduce the use of non-renewable resources by investing in renewable substitutes. create • Stimulate innovation to facilitate the adaptation of more efficient and greener technologies. Socioeconomic goals Transformation of the energy system would likely affect most aspects of life. Low-income populations lack the capital to benefit from higher-efficiency technologies. They also suffer disproportionate exposure to health and environmental hazards from power generation and climate change. A transformation to a net-zero economy could combine natural assets with a nation’s culture of innovation to produce an energy system that mitigates social injustices by fairly distributing both opportunities and costs, perhaps complemented by appropriate policies. Cost-effectiveness is important because it can help a country avoid spending more than necessary to achieve an energy transition to sustainability. However, cost effectiveness analysis may ignore how costs and benefits are distributed within an economy. A policy needs to balance cost-effectiveness with equity goals. This could be possible through reliable and affordable energy, opportunities to benefit from the best available technology, and new employment opportunities. Policies should promote fair access to new long-term employment opportunities, provide financial and other support to communities, and create employment with opportunities for skills development (Hall et al. 2014). Economic efficiency can be characterized in many ways, including allocative efficiency, dynamic efficiency, operational efficiency, productive efficiency, and business efficiency. Applications of these principles include efficient-market hypotheses, microeconomic reform, and welfare economics.

58  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence Properties of sustainable economies Sustainable economies may have the following properties (Polasky et al. 2019): • Agriculture and food systems: Community efforts can protect agricultural land, encourage sustainable agricultural practices, support local food producers, and facilitate the production and distribution of locally produced food. • Urban/rural economics: Cooperative efforts can led to land preservation, appropriate development of rural resources, improved trading and tourism, and development of regional planning and transportation systems. Small businesses are sources of employment as well as providers and consumers of goods and services that sustain the local economy. Their operation should support the local ecology, minimize energy use and waste, and utilize recycled products and materials. Local financial resources can be invested in economic initiatives. • Forestry and wood products: Forestry can impact on both urban and rural ecosystems by maintaining desirable microclimates and sheltering wildlife. Trees also have economic value as a raw material used in producing paper, buildings, furniture, and other wood products. • Manufacturing and industry: Communities should work to attract and support economically healthy businesses and industries with minimal environmental impact. • Technology: Communities must guide technological advances in business, health, education, and the environment, providing new opportunities for society. Residents working together, business and government leaders and local non-profit organizations can analyze and identify needs and resources and guide the economy.

3.1.1  Energy Return on Investment The ratio of useful energy output to input energy required to produce the useful energy is called the Energy Return on Investment (EROI). That is, EROI =

Energy output (3.1) Energy input to get this energy output

This parameter helps assess the energy cost as well as its contributions and the limitations of a given type of energy. Most renewable energy resources have lower values of EROI compared with conventional fossil fuels. Some examples: EROI (sugarcane ethanol) ~ 8 and EROI (corn ethanol) ~ 1.2–1.6 (excluding the energy content of dried distiller’s grains with solubles). These values demonstrate a better overall economy for bioethanol production from sugarcane. The high values of EROI for fossil fuels may need to be supplemented or rapidly replaced by alternative energy sources to avoid the potential effects of climate change. These “new” energy sources must be sufficiently abundant and have a large enough EROI value to power technology and development (Hall 2016, Brockway et al. 2019). Table 3.1. Approximate values of EROI for bioethanol and biodiesel production processes from various feedstocks and change in life cycle GHG emissions per kilometer traveled by replacing diesel with 100% biodiesel fuel (Demirel, 2018). Feedstock Sugarcane Sugar beets Corn Wheat Lignocellulosic Rapeseed Soybeans Sunflower

EROI 0.8–10 0.84–1.65 0.69–6.61 1.0–1.5 0.7–2.0 0.4–1.2

GHG emissions change* (%) −87 to −96 −35 to −56 −21 to −38 −19 to −47 −37 to −82 −21 to −51 −63 to −78

* Approximate avoided GHG emissions due to production of bioethanol from a biomass feedstock.

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Table 3.1 shows for biofuel production the EROI, i.e., the ratio of biofuel energy output to fossil energy inputs in the production process, for various feedstocks. The values of EROI for the biodiesels from all the feedstocks are higher than 1, suggesting they contribute to reductions in greenhouse gas (GHG) emissions.

3.1.2  Renewable Energy Cost Renewable energy is competitive and helps decarbonization, as many renewable energy sources are evenly distributed worldwide compared with fossil fuels. A mix of wind and solar based renewable energy may be able to provide the optimum cost of replacing fossil fuels worldwide. Solar power may be used during the day and wind power during the night. In addition, a reliable grid structure, utility-scale energy storage, and smaller wind turbines help increase the contribution of renewable energy. Natural gas can be an important back-up fuel for intermittent renewable energy, complementing the mixture of hydroelectricity and stored renewable energy (Demirel 2021).

3.1.3  Levelized Cost of Electricity The levelized cost of electricity (LCOE) is a measure of the average net present cost of electricity generation for a generating plant over its lifetime, and it is often used to determine the price at which electricity must be produced from a specific source to break even. It is an economic assessment measure and includes initial investment, cost of operations and maintenance, cost of fuel, and cost of capital. LCOE can be expressed as follows: n I +M +F i i i i + r (1 ) i = n E ∑ i i i (1 + r )

∑ = LCOE

Total costs (3.2) Total electricity produced

where LCOE is the average lifetime levelized electricity generation cost, I is the investment expenditures, M is the operations and maintenance expenditures, F is the fuel expenditures, E is the electricity production, i is the interest rate, and n is the useful life of operation. Typically, LCOE is calculated over 20 to 40 year operational lifetimes and is given in the units of $/kWh or $/MWh. The simple levelized cost of energy is calculated by: LCOE = {(capital cost * capital recovery factor + fixed O&M cost)/(8760 * capacity factor)} + (fuel cost * heat rate) + variable O&M cost (3.3) where the capital cost is in $/kW, while the fixed operation and maintenance (O&M) cost is in $/kW-yr and the variable O&M cost is in $/kWh. In the denominator 8760 is the number of hours in a year and capacity factor is a fraction between 0 and 1 representing the portion of a year that the power plant is generating power. Fixed O&M costs include labor, scheduled maintenance, routine component/equipment replacement (for boilers, gasifiers, feedstock handling equipment), insurance, and others. The fixed O&M costs of larger plants are lower per kW due to economies of scale. Variable O&M costs include ash disposal, unplanned maintenance, equipment replacement and incremental serving costs. The capital recovery factor (CRF) for a given length of time n and interest rate i is

CRF =

i (1 + i )n

(1 + i )n − 1

(3.4)

CRF is used to convert a present cost value into annual cost. Fuel cost is optional since some generating technologies like solar and wind do not have fuel costs. Table 3.2 shows the levelized costs of new power generation plants of various kinds (Demirel 2018, 2021).

60  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence Table 3.2.  Estimated levelized cost of electricity for new power generation plants (EIA 2018). Plant type

Capacity factor, %

Combined cycle Combustion turbine Wind, onshore Wind, offshore Solar photovoltaic (PV) Geothermal Hydro

87 30 40 45 30 90 73

Levelized cost of electricity* (LCOE) (2021), U.S. $/MWh Transmission Total system cost Capital Op. & maintenance 9.5 58.1 29.8 106.6 30.6 25.9 36.7

35.5 95.3 9.5 35.4 7.3 19.8 11.5

1.5 4.4 3.9 4.0 3.6 1.9 2.0

1.5 4.4 3.9 4.0 3.6 1.9 2.0

* Costs are expressed in terms of net AC power available to the grid for the installed capacity.

Values of LCOE for renewable energy vary with technology, location, energy resource, capital and operating costs, and efficiency. Most renewable power generation technologies are capital intensive, which has a significant impact on the value of LCOE. Value‑adjusted levelized cost of electricity The IEA developed the value-adjusted levelized cost of electricity (VALCOE) as part of the World Energy Model (WEM), which is used in the 2018 World Energy Outlook (IEA 2018). VALCOE includes energy, capacity, and flexibility and serves as a metric for power generation and complements the LCOE, which only captures relevant information on costs and does not reflect differing technologies. VALCOE considers both cost and value for variable renewables and dispatchable thermal technologies. For each technology, the estimated values elements are compared relative to the system average to calculate the adjustment to the LCOE by considering the perspective of policy makers and planners. For technology (or generation unit) x, VALCOE is calculated by adding value adjustments for energy, capacity, and flexibility values, as follows: VALCOE = LCOE x + ( E − E x ) + (C − C x ) + ( F − Fx ) (3.5) x where Ex is the adjustment for the energy value of a technology (or generation unit) x, determined – as the difference between the individual unit to the system average unit (E ): E x ($/MWh) =

Whole sale priceh ($/MWh) × Output x,h (MW) ∑8760 h (3.6) Output x,h (MW) ∑8760 h

The adjustment for capacity value [Cx] of a generation unit is: C x ($/MWh) =

Capacity credit x × Basis capacity value($/kW) (3.7) Capacity factorx × Hours in year/1000

The capacity credit reflects the contribution to system adequacy, and it is differentiated for dispatchable versus renewable technologies, as follows: • Dispatchable power plants = (1 – unplanned outage rate by technology) • Renewables = analysis of technology-specific values by region with hourly modelling The basis capacity value is determined based on simulation of the capacity market, set by the highest “bid” for capacity payment. The capacity factor is differentiated by technology, as follows: • Dispatchable power plants = modelled as simulated operations in previous year • Wind and solar photovoltaic (PV) = aligned with latest performance data from IRENA and other sources, improving over time due to technology improvements • Hydropower and other renewables = aligned with latest performance data by region and longterm regional averages

Economic Sustainability  61

The flexibility value (Fx) of a generation unit is calculated as follows: Fx ($/MWh) =

Flexibility value multiplierx × Base flexibility value($/kW) (3.8) Capacity factorx × Hours in year/1000

The following points are noted: • The flexibility value multiplier by technology is based on available market data and held constant over time. • The base flexibility value is a function of the annual share of variable renewables in generation. Advantages and limitations of the VALCOE: • It provides a more sophisticated metric of competitiveness incorporating technology-specific information and system-specific characteristics. • It reflects information/estimations of value provided to the system by each technology (energy, capacity/adequacy, and flexibility). • Fuel diversity concern is a critical element of electricity security and is not reflected in the VALCOE.

3.2  Circular Economy The linear economy is characterized by “take, make, and dispose,” while the circular economy (CE) closes the loop by collecting and recycling waste, and using it as feedstock to manufacture new products and preserve natural resources. The concept is illustrated in Figure 3.2. Circular economy strategies prevent waste and increase resource efficiency, moving toward regenerative manufacturing and sustainability, and decreasing energy and water consumptions, sometimes through regulatory compliance. Some focused sustainability and circular initiatives enable companies to: • Reduce nonrenewable source and water use, substitute renewable energy, and invoke wastewater treatment • Reduce emissions such as CO2 and NOx and emphasize decarbonization with feasibility assessments • Improve process efficiency with processes intensification and product value chain integration • Develop innovative product and process options including recycling/reusing • Enhance safe and reliable operation with predictive maintenance solutions

Resources

Manufacture

Circular Economy Waste/Collect/ Recycle

Consumer use

Linear Economy Resources/Take

Manufacture

Consume/Dump

Figure 3.2.  Circular economy versus linear economy. The linear economy involves the sequence of extraction (of raw materials), manufacture, use, and disposal.

62  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence The circular economy forces companies to redesign and remanufacture products, as well as to interact with customers. Here, the customer’s role is no longer just to consume but also to build long-term relationships with industrial sectors for retaining the value and integrity of their products. For example, a continuous process can depolymerize lignin to stable monomeric phenols, such as guaiacol, syringol, and phenolic derivatives. Environmental regulations, energy and water conservation, air quality and climate change are prime concerns for shareholders and customers Consume/Dump alike. In particular, the circular economy of plastics requires a “full-cycle” approach to production and extended use, to conserve resources and protect the environment (Pagoropoulos et al. 2017, Galvao et al. 2018, Kerhonen et al. 2018, Unlu et al. 2020, Tura et al. 2019, Paes et al. 2019).

3.2.1  Circular Economy and Sustainability The CE represents an alternative economic model to the linear economy. It offers potential means 3.2.1 Circular Economy andchallenges Sustainability to address to environmental caused by an overuse of limited resources within a linear The CEthrough represents an alternative economic to theorlinear economy.byItintention offers potential system, an industrial economy that ismodel restorative regenerative and design. address to environmental challenges caused by an overuse of limited The circular economy is viewed as one of the conditions for sustainability as they both emphasize through an industrial economy that is restorative or regenerative by intention and design. regeneration commitments and integrate non-economic aspects into product development through economy is viewed as one of the conditions for sustainability as they both design and innovation. However, sustainability focuses on benefiting the environment, the economy, commitments and integrate non-economic aspects into product and society However, at large, while the CE emphasizes the efficient use of resources with less waste and innovation. s emission, benefiting society through environmental improvements andwaste new and economic activity large, while the CE emphasizes the efficient use of resources with less emission, (Figure 3.3). In the sustainability debate, responsibilities are loosely shared with the focus on society through environmental improvements and new economic activity (Fi interest alignment between stakeholders, while private businesses, regulators, and policymakers are responsible for the transition to the circular economy (Zabaniotou 2018, Tura et al. 2019, Velenturf and Purnell economy 2021). ( the circular ).

Environment Economics

Society

Environment

Society

Economy

Economy

Society

Environment

(a)

(b)

(c)

Figure 3.3.  Conceptual illustrations of sustainability. (a) Equally distributed dimensions, as a triple-bottom line, (b) economy Figure 3.3. Conceptual illustrations of have sustainability. Equally distributed dimensions, as a is at the center, (c) society and economy both central role in (a) the sustainability constrained by the environment.

sustainability Equitable society and sustainability An equitable society maintains environmental quality and economic prosperity for current and future generations (Strawn 2021). A set of three core values has been proposed along with ten principles 7 for the design, implementation, and evaluation of a sustainable circular economy (Geissdoerfer et al. 2016, Galvao et al. 2018):

• Social and individual well-being that meets human rights standards for all. • Environmental quality with the use of resources within planetary boundaries across generations. • Economic prosperity with a collective organization of fair access to resources.

Economic Sustainability  63

Principles: 1. Beneficial flows of resources between nature and society for their mutual sustainable co-existence. 2. Reduction and decoupling of progress from unsustainable material use. 3. Design for circularity in the industrial sector and supply chains, utilization of materials for a sustainable circular society. 4. Circular business models to integrate innovative business models compatible with social and environmental costs of materials and products. 5. Enabling of responsible, reduced, demand-driven and experience-based consumption. 6. Citizen participation in sustainable transitions with social innovations, ideas, and national policy development and decision-making. 7. Development, integration, and implementation of circular economy strategies. 8. Supporting diversity to develop a plurality of circular economy options. 9. An economy for multi-dimensional prosperity with strong sustainability in economic, environmental, and social dimensions. 10. Whole system assessment to understand challenges and options for a sustainable circular economy. Interactions of the dimensions of sustainability may vary. In the triple bottom line (TBL) approach, all dimensions are considered of equal. The economy may also play a central role as the organization of society, and both are dependent on the environment. On the other hand, the economy may be a tool to organize resources for societal wellbeing and environmental protection and development. The CE includes various feedstocks and processes (see Figure 3.2). Organic recycling and the capture and utilization of CO2 are included. Raw materials are converted to products, traded, used, and then they enter the waste hierarchy from share/maintain, reuse/redistribute, remanufacture to recycling. All biomass flows are potentially part of the circular economy with the steps remanufacture and recycling of bio-based products (Kalmykova et al. 2018). Customer feedback Prosumers are the type of customers who participate in business process activities, including customers’ reviews of products or services The CE creates prosumers whose engagements include sharing and knowledge exchange to capture and improve enterprise analysis, adopt ways of limiting negative impacts on the environment and improving ethical and responsible business practices. A conceptual framework of prosumer engagement in business processes is provided in Table 3.3. The business value can improve because of a prosumer’s willingness to share knowledge (Cabrera and Cabrera 2002). The customer cannot be ignored and may be seen as a complementary aspect of new product development, co-designing of new products (development), co-pricing and co-distribution of the new products (commercialization) (Izvercianua et al. 2014, Ziemba and Eisenbardt 2015). The CE helps industrial sectors to evaluate and rethink their processes. The CE could act as a positive driver for business process changes and aid enterprises in meeting carbon neutral plans. The CE offers potential solutions to environmental challenges caused by an overuse of limited resources within a linear system. Information technology empowered customers are increasingly demanding organizations to respond to or even to lead in efforts to reduce and limit negative environmental consequences (Galvao et al. 2018). Figure 3.4 shows a model on the extent to which prosumer engagement is determined by the choice of business activity, customer motivation and communication tools. It is important to note how customer knowledge is used, shared, and created in the enterprise. This offers opportunities for

64

Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence Table 3.3. Conceptual framework of prosumer engagement in business processes.

Sustainability Consumer feedback in environment and societal needs Operational business processes Kind of knowledge (Ziemba DevelopSource: and manage productsand Eisenbardt New2015). product design and specs, raw materials, usage, and recycling Market and sell services

Advertising/marketing, pricing, promotions

Figure 3.4 shows a model on the Service extentavailability to which Deliver services and distribution channels, and ordering business activity, customer motivation and communication tools. It is important to note Manage customer service Customer service, complaints, and warranty services knowledge is used, shared, and created in the enterprise. T Sustainability Consumer feedback in environment and societal needs and for Source: (Ziemba and Eisenbardt 2015).

Business/ Production production Incentives/ Customer motive

Prosumer Engagement engagement

ICT/ Communication

Sustainability challenges

Knowledge Exchange Validation Reinforcement Contradiction

Enterprise Strategy Offerings Activities

Circular economy

Figure 3.4.  Conceptual model of prosumer engagement and knowledge exchange. ICT: Information and Communication Technology.

enterprises to gauge the increased demand for environmentally friendly products and services and for consumers to assess and evaluate sustainable operations and behavior (Geisdoerfer et al. 2016). Figure 3.4 illustrates that the implementation of the CE requires participation of consumers toward a new consumption culture in the sharing economy. Digital technology can help with the transition to a circular economy by enhancing several important factors: virtualization, dematerialization, transparency, and feedback driven intelligence. Sustainability would be supported by the activities in line with circular economy. Table 3.4 provides some engagements of prosumers that support the Table 3.4.  Framework of potential areas of activity for prosumers in a circular economy (Ziemba and Eisanbardt 2015). Circular economy activities marketing strategies that, Prosumer in turn, engagement would support a circular economy. Develop and manage products and services

New product design (shape, colour, cost of products)

Materials areas from which the product should be produced and theireconomy procurement Table 3.4 Framework of potential of activity foris or prosumers in a circular Package or graphic elements of the product (logo or label) 2015)

Circular economy activities Product functionality Prosumer engagement Reliability and durability of the product New product design (shape, colour, cost of products) Ease and intuitiveness of product use Develop and manage products Materials from which the product is or should be produced and services Product performance (effectiveness and efficiency of use) procurement Market products and services Advertising/marketing (no printing and paper) Pricing strategy (avoid gimmicks) Promotions, discounts, loans Deliver products and services

9

Service availability and distribution channels Ordering processes

Manage customer service

Complaint handling and warranty services Customer service

Economic Sustainability  65

activities related to the relationships of consumers with products and services promoting circular economy and hence economic sustainability. There would be an effective feedback structure from the consumers to the customer service of producers. This would help companies to plan their investments, product design and development, and marketing strategies that, in turn, would support a circular economy. A value chain index (VCI) provides for manufacturing based on the impacts from raw materials to consumed and discarded goods. A VCI draws on objective data produced by life-cycle analysis and covers a range of categories, such as land use, water, energy, carbon, toxics, and social welfare. Valuations of ecosystem services are integrated into VCIs to assign true costs to goods sold. It has been suggested that a company that earns better ratings would be rewarded from customers (Benigno et al. 2022). Energy transition The energy transition from fossil-based energy to renewable energy has technological, societal, cultural, economic, and environmental aspects. Bioenergy systems are subjects to legal, technical, environmental, economic, and social requirements, incorporating agricultural traditions and offering waste-management solutions. Bioenergy in the transition to a circular waste-based bioeconomy may face challenges, due to the new demand for biomaterials from the same sources. Bioenergy systems can offer waste management options through standalone, tailor-made decentralized systems while addressing public health and community resilience (IEA 2018, Demirel 2018, 2021). A circular economy leads to better use of resources to generate economic gains while reducing pressure on the environment. Principles of sustainable development are in line with CE practices of maximum waste prevention. CE should restore and regenerate the environment, and optimize social, technical, and economic values of materials and products in society (Velenturf and Purnell 2021). The foundations of economic growth include labor and capital produced by people. Technological change makes better use of capital and allows for sustainable per capita consumption.

3.3 Bioeconomy The bioeconomy is based on all economic activity originating from invention, development, production, and use of biological products and processes in the production of food, energy, and materials. Biomass feedstocks can be used to produce food/feed, fuel, fiber, fertilizers, polymers, chemical feedstocks, pharmaceuticals, heat, and electricity. The bioeconomy responds to diminishing fossil-based resources, climate change, and growing world population through resource-efficient fuels and processes for the well-being of societies. Integrating a bioeconomy and a circular economy can lead to economic sustainability. The future of the bioeconomy requires global agreement on metrics and the creation of a dispute resolution center. The bioeconomy focuses on production of renewable resources from biological materials and converts them into products such as food, animal feed and bioenergy. Non-food crops, such as switchgrass (panicum virgatum), are the focus, along with agricultural and forestry residues and waste materials and gases. A significant concern is reconciling the conflicting needs of agriculture and industry. In a post-fossil-fuel world, an increasing proportion of chemicals, plastics, textiles, fuels, and electricity may be derived from biomass, which requires land for growing. By 2050, the world is expected to need to produce 50–70% more food, increasingly under drought conditions and on poor soils. An internationally agreed biomass sustainability governance framework is now needed, as present biomass assessments often lack comparability. For example, greater use of wood for power and heat generation may decrease GHG emissions, while retaining the sequestration capabilities of forests for carbon and protecting biodiversity (Nguyen and Demirel 2013, Bosch et al. 2015, Whitters et al. 2017, Issa et al. 2019, Unlu et al. 2020).

66  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence al. 2020). 3.3.1  Important Aspects of Bioeconomy

The bioeconomy is not a3.3.1 partImportant of the circular economy, although the research approach, strategy, Aspects of Bioeconomy and policy overlap with aThe CE bioeconomy strategy. However, a comprehensive CE may not bealthough possible without is not a part of the circular economy, the bioeconomy. Figure policy 3.5 displays fundamentals of aHowever, circular bioeconomy. The CE feedstock overlapthe with a CE strategy. a comprehensive may the process fundamentals a advantageously from agriculture, forestry,bioeconomy. fisheries, andFigure food, 3.5 feeddisplays and organic wastesofcan agriculture, forestry,strategy. fisheries,The andproduction food, feedof and organic waste be integrated in the CE with a bioeconomy biofuels and process bioproducts integrated in the CE with and a bioeconomy strategy. productionof bioenergy depends on the availability of biomass feedstock biotechnology. TheThe intersection availability of biomass feedstock andand biotechnology. The intersectio markets with established the markets in agriculture, forestry, water, energy may cause substantial impacts on the prices of agricultural commodities, food, feedstuffs, forest products and fossil fuel of agricultural commodities, fo energy, and on land values (Kumar et al. 2010, Lopes 2015, Demirel 2021).

Bioenergy Biofuels ethanol biodiesel

Agriculture

Non biobased sectors

Bio sector bioproducts

Figure 3.5.  Circular bioeconomy fundamentals.

.

Bioeconomy and bioproducts A bioeconomy needs specific knowledge-based processes and feedstocks, such and as biotechnology, A bioeconomy needs specific knowledge-based processes feedstocks insects, newand applications new links the and other industrial algae or insects, new applications new linksandbetween thebetween bioeconomy Cpetroleum Cpetroleum the use of lignocellulosic biomass bioeconomy. Natural Natural cycles sectors. the Q use lignocellulosic biomass inina abioeconomy. cycles CAlgae =Figure 3.6 illustrates (1-of w) E = illustrates biogas + YwEbiodiesel the bioeconomy can strongly contribute to a circular economy. The b M E AB petroleum such as the nutrient cycle in the bioeconomy can strongly contribute to a circular economy. The ways to the CE, including the including utilizationthe of utilization organic sideofand wasteside streams bioeconomy can contribute in several ways to the CE, organic and fishery, and food sectors waste streams from agriculture, forestry, fishery, and food sectors. Also, biodegradable products can be returned to organic and nutrient circles.(Unlu The et bioeconomy technological expertise al. 2020). concept can link very different industrial sectors that brings together scientific and technological expertise (Unlu et al. 2020).

(

LignoLigno cellulosic cellulosic biomass

Sugar recovery

)

Conversion processes

Ethanol lignin biogass

Lignin valorization 11

Electricity, bioproducts, adipic acid

Figure 3.6.  Biofuels and bioproducts from lignocellulosic biomass (Demirel 2018).

3.3.2  Waste Management Municipal solid waste (MSW) represents both a disposal challenge and potential resource for feedstock production. Reduced landfill capacity and loss of valuable materials in MSW are challenges faced by this resource, which typically has low energy content, high moisture levels, Baseline heterogeneous and variable amount in time. Actualstream impactcomposition, Stock

Economic Sustainability 67

Source reduction & reuse

Recycling & composting

Energy & material recovery Treatment disposal Figure 3.7.  Waste management hierarchy.

Figure 3.7 shows the hierarchy of waste management. Waste has compositional complexity and regional and temporal variability. Waste-to-energy processes from MSW focus on improving cornofstover, straw, to riceproduce straw, sugarcane bagasse and poplar sawdust, 1.26– economic viability wastewheat utilization liquid fuels, biochemicals, and other bioproducts. residue is generated from the biorefining process and 0.19– However, conversion technologies have challenges with the volume and yield necessary to produce high-quality feedstocks. Therefore, the following are needed: (i) characterization and sensing 12 methods for MSW, and (ii) the development of pretreatment processes for chemical, electrochemical, biological, or other hybrid options to reduce feedstock variability. Robotics and AI can help in the characterization, preprocessing, and analyses of value so as to support the economic viability, efficiency, and sustainability of current and future waste management and utilization industries. Two notable types of technology gaps/challenges exist: (i) the development and use of better sensor/AI/robotic technologies to sort MSW into different fractions and (ii) the development of real-time, “online” measurements of chemical composition, particularly for cellulosic materials in the MSW stream (Paes et al. 2019). Electricity generation is possible from lignin residue. For producing one metric ton of ethanol fuel from corn stover, wheat straw, rice straw, sugarcane bagasse and poplar sawdust, 1.26–1.85 tons of dry lignin residue is generated from the biorefining process and 0.19–0.27 tons of biogas is generated from anaerobic digestion of wastewater. The electricity generation from combustion of lignin residue and biogas is in the range of 7121–8180 kWh per ton of ethanol produced (Kumar et al. 2010, Liu and Bao 2017). Converting waste lignin into adipic acid A multi-criteria decision matrix consisting of economic as well as sustainability metrics compares three cases of adipic acid productions. The cases are as follows: case 1: waste lignin-based, case 2: glucose based, and case 3: petroleum-based conventional process (Unlu et al. 2020). The comparison of these cases shows that the two bio-based processes that can displace the current petroleum-based adipic acid production. The bio-based processes are: (i) the conversion of biorefinery lignin residue to adipic acid with base-catalyzed depolymerization of lignin and subsequent microbial conversion of monomers to muconic acid and catalytic upgrading to adipic acid (case 1), and (ii) the catalytic production of adipic acid from glucose oxidized to produce glucaric acid converted to adipic acid via hydrodeoxygenation in the acidic environment and in the presence of a metal catalyst (case 2). Figure 3.8 shows the block flow diagram for glucose-based adipic acid production. Figures 3.9 and 3.10 compare the energy usage and emissions in these cases. The usage of energy is the least in case 3. However, the glucose-based process appears to be feasible and outperforms the lignin-based process as well as the conventional process in producing less carbon emissions, as seen in Figure 3.9 (Unlu et al. 2020, Nandiwale et al. 2020).

68  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence

Figure 3.8.  Block flow diagram for conversion of glucose to adipic acid process (Unlu et al. 2020). MT = Metric tonne; GA = Glucaric acid; AA = Adipic acid; WWT = Wastewater treatment.

Figure 3.9.  consumption in adipicinacid production for three casesfor (Unlu et al.cases 2020). Figure 3.9.Energy Energy consumption adipic acid production three

Figure 3.10.  Total discharged GHG emissions for three cases (Unlu et al. 2020).

Economic Sustainability  69

Internal barriers to a bioeconomy Some of the internal barriers to a bioeconomy are product development, byproduct and coproduct distribution and marketing, continuous project growth, management, strategy, technology for conversion rate and yield. The main external barriers are competition, funding, supplies, pathway processes, tax credits, renewable fuel standards, renewable volume obligation, renewable identification numbers, and energy costs. Renewable fuel standards require that transportation fuels contain at least 10% biofuel. Shortages of biofuels and relatively abundant supplies of fossil fuels lead to subsidies for the following objectives: (1) sequestration of carbon to reduce GHG emissions, (2) achieving greater energy efficiency, (3) integration of rural programs into increasing energy security, (4) stimulating economic growth and development, and (5) obtaining feasible conversion technologies (Withers et al. 2017). Low-cost biomass feedstocks available as by-products from agriculture or forestry can be used to provide competitive electricity. Large scale bioenergy generation plants (> 50 MW) are unattractive compared to fossil fuel plants because of the logistical costs of transporting low-density feedstock from production sites, sometimes located far away. In addition, large-scale production of biofuels can adversely affect land availability for food crops, by causing the land to be reallocated for energy crops. Biofuels are promoted worldwide with tax credits and subsidies to reduce petroleum imports and GHG emissions from vehicles and other devices.

3.3.3  Economic Assessment of Biofuels The effects of biofuel production on the world-wide trade of grains, livestock, biomass, and crude oil are a part of energy-based economic assessments. The biofuel industry also has some economic effects related to the national budget and spending through such tools as tax credits, subsidies, incentives, and other policy instruments. The diversion of land to corn and soybean production and a greater demand for biofuels may coincide with an increase in the price of wheat, corn, and soybean (NRC 2011, Demirel 2021). Conversion of lignocellulosic feedstocks requires first the complex process of conversion of cellulose and hemicellulose into sugars. The costs of biomass conversion processes increase in the following direction: triglycerides → starch → lignocellulosic, while the cost of biomass increases in the following order: lignocellulosic → starch → triglycerides. Bioethanol producers have adopted various technologies such as high tolerance yeasts, continuous ethanol fermentation, co-generation of steam and electricity, and molecular sieve driers to reduce ethanol production costs (Rosen 2011, Demirel 2018). Energy efficiency The energy efficiency of a conversion process for producing biofuels from biomass can be evaluated as follows:

 LHV of biofuel   External energy      produced per 1 kg used in 1 kg biomass     Energy efficiency      to biofuel conversion   (3.9) of biomass        of biomass to biofuel  = e 1 kg biomass LHV of th    conversion       used in the convesion  Here, LHV denotes lower heating value. As the biofuel technology becomes more energy efficient and advanced through technological improvements, the production cost is expected to decline, by around 50% by 2030 (Demirel 2018, 2021). Second generation biomass resources are geographically more evenly distributed than fossil fuel resources. This may lead to energy security and local economic activity and employment. A regional preprocessing infrastructure can be set up to clean, sort, chop or grind, control moisture, densify, and package the feedstocks for biorefineries.

70  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence Table 3.5.  Water requirements for ethanol and biodiesel production from selected biomass crops (Ptasinski 2016). Energy crop

Water use, m3 water/kg crop

Energy use, liter fuel/kg crop

Crop water use, m3 water/kg fuel

Crop water use per unit energy, m3 water/GJ

Ethanol, corn

833

409

2580

97

Sugarcane

154

334

580

22

Corn stover

634

326

2465

92

Switchgrass

525

336

1980

74

Grain sorghum

2672

358

9460

354

Sweet sorghum

175

238

931

35

Biodiesel, soybean

1818

211

9791

259

Canola

1798

415

4923

130

Efficient and affordable depolymerization of cellulose and hemicellulose to soluble sugars and fermentation of them with free inhibitory compounds may advance process integration steps, freshwater usage, and energy costs, which are key to this type of process (Demirel 2018). Table 3.5 shows and compares the large amounts of water required for bioethanol and biodiesel production from various biomass feedstocks. Bioenergy production Bioenergy production has the following settings that effect decisions for producing bioenergy: • Economic settings: Decision making is typically based on the cost of bioenergy production and the scale of plant and location. The installation cost is higher for bioenergy than wind and solar. Greater use of bioenergy can result from combining it with carbon capture and storage/ sequestration (CCS), so as to mitigate carbon emissions. Biomass utilization with advanced combustion technologies, such as circulating fluidized bed boilers and steam turbines with combined heat and power, can compete with other technologies in terms of costs (Ptasinski 2016, Demirel 2018, Allen et al. 2018). • Environmental settings: Bioenergy based GHG emissions can be analyzed using life cycle assessment (LCA). An efficient circular economy requires evaluation of value creation. Environmental sustainability is in line with the circular economy concept, as it protects natural resources and reduces wastes. Carbon dioxide (CO2) reduction through the bioenergy sector can be accounted for within the bioeconomy. • Sociological settings: Bioenergy projects are part of broader environmental, socioeconomic and livelihood aspects that are of relevance to local communities. Farmers, end users, nongovernmental organizations, and government representatives have roles in bioenergy development, in particular to remove such barriers as inadequate information and knowledge and lack of public trust.

3.3.4  Bio Break Model Potential economic and environmental effects of U.S. biofuel policy by the National Research Council of the U.S. suggest renewable fuel standards and economic models (NRC 2011). One such economic model is the Bio break model. This is a flexible breakeven model that estimates two costs: willingness to pay (WTP), which is the maximum price that a biorefinery can pay for a dry ton of biomass delivered at the gate, and willingness to accept (WTA), which is the minimum price that a biomass producer would accept for a dry ton biomass delivered at the gate of a biorefinery (NRC 2011, Demirel 2021).

Economic Sustainability  71

Numerous assumptions are used in the Bio break model:

• • • • • •

Producer minimizes costs on the long-run average cost curve A yield distribution for biomass crops is based on the expected mean yield A transportation cost is based on the average hauling distance for a circular capture region Biorefinery has an annual capacity to be competitive in the market Each biorefinery uses a single feedstock with no market disruptions Impact of energy price uncertainty on biofuel investment is not considered

Willingness to pay Willingness to pay for lignocellulosic bioethanol production per dry ton of cellulosic material delivered to a biorefinery can be expressed as follows (NRC 2011): WTP = (Pgas EV + T +VCP + VO – CI – CO)YE (3.10) where Pgas denotes per gallon price of gasoline (Pgas = 0.13087 + 0.023917Poil) and EV denotes the energy equivalent factor that compares the energy contents of gasoline to ethanol. Beyond direct ethanol sales, the ethanol processor also receives revenues from tax credits T, coproduct production VCP, and octane benefits VO per gallon of processed bioethanol. Biorefinery costs consist of investment costs CI and operating CO costs per gallon. A conversion ratio is used for gallons of ethanol produced per dry ton of biomass YE. The following assumptions are employed: • Price of oil (Poil): The assumed three oil price levels are: $50, $100, and $150 per barrel, which are the low, reference, and high price projections. • Energy equivalent factor (EV) and octane benefits (VO): The energy equivalent ratio (EV) for ethanol to gasoline is fixed at 0.667. For simplicity, the value of VO is fixed at $0.10 per gallon. • Coproduct value (VCP): Excess energy is the only coproduct. • Conversion ratio (YE): This parameter has a mean value of 70 gallons biofuel per dry ton feedstock. • Expected investment costs (CI) by producers. • Operating costs (CO) of production. • Biofuel production incentives and tax credits (T) expected by the produces. Willingness to accept Willingness to accept for lignocellulosic bioethanol production for biomass suppliers per dry ton of cellulosic material delivered to the biorefinery can be expressed as follows (NRC 2011): WTA = {(CES + COpp)/YB + CHM + SF + CNR + CS + DFC + DVC * D} – G (3.11) The value of WTA is equal to the total production costs less the government incentives G (for example, tax credits and production subsidies) for one dry ton of feedstock. Depending on the type of biomass feedstock, costs include establishment and seeding CES per acre land, and biomass opportunity costs COpp per acre, harvest and maintenance costs CHM, stumpage fees SF, nutrient replacement costs CNR, biomass storage costs CS, transportation fixed costs DFC, and variable transportation costs calculated as the variable cost per mile DVC multiplied by the average hauling distance to the biorefinery D. Therefore, the biomass yield per acre YB is used to convert the per acre costs to a dry ton cost. The following assumptions are invoked: • Nutrient (nitrogen, phosphorus, and potassium) replacement costs (CNR) range from $5 to $21. • Harvest and maintenance costs (CHM) and stumpage fees (SF) are assumed to have a mean value of $27–$46 with an additional stumpage fee having a mean value of $20 per dry ton.

72  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence • Transportation costs (DVC, DFC, and D): The Bio break model uses a breakdown of variable and fixed transportation costs. The one-way transportation distance D is evaluated up to around 140 miles for woody biomass and between 5 and 75 miles for all other feedstocks. The average hauling distance is between 13 and 53 miles. • Biomass storage cost (CS) is around $12 per dry ton. • Establishment and seeding costs (CES). • Opportunity costs (COpp). • Biomass yield (YB). • Biomass supplier government incentives (G). For the economic analysis, the BioBreak model estimates the price gap (PG) as follows: PG = WTA – WTP. If PG is negative or zero, a biomass market may be economically feasible. Bio break model for algal feedstock Equations (3.10) and (3.11) for cellulosic material are based on land use. Altering this section into a single variable that encompasses the costs of establishing a cultivation process provides a more effective view of what plays into the production of algal biomass (Crea). The equation for WTA can be modified for algal biomass (Demirel 2021), as follows: WTAalgae = (CRea + CHM + CNR + CS) (3.12) where CHM is the cost of harvest and maintenance, CNR nutrient replacement, and CS biomass storage. The optimum cost of algae biomass depends on the quantity of algal biomass (MAB, tons), and is represented in terms of the energy equivalent of a barrel of crude petroleum, estimated by:

M AB =

Epetroleum Q(1- w) Ebiogas + YwEbiodiesel

(3.13)

where Epetroleum (~ 6,100 MJ/brl) is the energy contained in a barrel of petroleum, Q (m3/ton) is the biogas volume produced by anaerobic digestion (400 m3/ton), Ebiogas (MJ/m3) is the energy content of biogas (~ 2.4 MJ/m3), Y is the yield of biodiesel from algal oil (80% by weight), Ebiodiesel is the average energy content of biodiesel (37,800 MJ ton–1), and w is the oil content of algae biomass. If a barrel of crude oil has the same energy of M tons of algae, the maximum acceptable cost of algae CAlgae becomes

= CAlgae

Cpetroleum Cpetroleum = Q(1- w) Ebiogas + YwEbiodiesel (3.14) M AB Epetroleum

(

)

3.3.5  Bioeconomy and Circular Economy The bioeconomy encompasses the production of renewable biological resources and the conversion of these resources and waste streams into value added products and bioenergy. The circular economy is the economic space where the value of products, materials and resources is maintained in the economy for as long as possible with minimum or no waste. The main target of a circular economy is increased resource efficiency and less demand for fresh materials, with both often linked to job creation. These targets are important for more sustainable and resource-efficient world with a low carbon footprint. The bioeconomy provides renewable carbon to industry and can directly replace fossil fuel carbon in various applications. Although considerations regarding the CE are often dominated by the metal and mineral industries, the bioeconomy provides an additional organic, recycling pathway that expands the circular economy.

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The CE strengthens the eco-efficiency of processes and the use of recycled carbon to reduce the use of additional fossil fuel, thereby maintaining the value of products, materials, and resources in the economy for as long as possible. Therefore, it substitutes fossil fuel carbon by bio-based carbon from any biomass with improved resources, higher eco-efficiency, and a lower GHG footprint. The benefits of the circular economy overlap with elements of sustainability and align with most sustainable development policy. The inclusion of bioenergy in the circular economy requires interests and concerns of policymakers, researchers, technology developers, project developers, and society be satisfied. A matureChp.3 circular bioeconomy can merge urban and rural communities, and provide cross-cutting policies in areas such as environmental protection, waste management, eradication of Epetroleum poverty, and manufacturing strategies (Withers et al. 2017, Zabaniotou 2018, Korhonen et al. 2018, M AB = Galvao et al. 2018, Issa et al. 2019). Energy and cost-efficient biotechnology operation can improve a bioeconomy and provide sustainable fuel and material production with minimum emissions and ecological deterioration (Kumar et al. 2010, Cornelia 2014, Demirel 2017). The concept of the bioeconomy goes beyond the CE and includes new chemical building blocks, precision farming, genome editing, new processing routes, and new functionalities of products.

3.4  Green Economy A green economy is based renewable resources including biomass and can be referred to as a renewables-based economy. For example, an algae-based economy produces versatile and sustainable raw materials for various biotechnological applications including fuels, biodegradable plastics, natural pigments, and food products. Furthermore, large-scale cultivation of algae absorbs CO2 and would have a positive impact on the overall carbon balance. The overall economics are dependent on costs of harvesting the polyester Ligno large quantities of algae. Also in a green economy, Ethanol Sugar Conversion lignin polyethylene furanoate cellulosic (PEF) may compete with polyethylene terephthalate (PET) made from recovery processes biomass terephthalic acid monomer. PEF offers a better environmental footprint as it is biogass bio-based and recyclable with properties of lower permeability of oxygen and water. Recently, there has been an increased investment in green systems toward biodiversity and a growing awareness of corporate dependencies on nature and its health. Natural capital mapping and assessment links nature’s assets including land, water, and air to flows of goods, with the input of intellectual and manufactured capital (Demirel 2017, EERE 2020). This provides a means to source investment in natural processes that can generate economic benefits (Figure 3.11).

Actual impact

Baseline Stock Flow Value

Monitoring Evaluations Citizen science

Investment Mechanism Return Product Risk

Capital investment platform

Predicted impact Flow Value Stakeholders

Opportunity Modeling Identifying Testing

Measures System Cost integration Effectiveness Optimization

Figure 3.11.  Natural capital investment in green technologies, and the ensuing benefits.

74  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence Natural capital Natural capital helps understand the economic value derived from nature, which is an important component of economic resilience (Guerry et al. 2015). Biodiversity net gain may optimize natural capital through the planning of resilient nature networks, through the following and other steps:

• • • •

Align activities with investment opportunities. Identify priority investment areas and measures. Undertake natural capital valuation. Create a natural capital investment platform.

Green economy: hydrogen and ammonia To have renewable energy generation and utilization substitute fossil fuels significantly requires renewable energy storage, transportation, and utilization at a very large scale. For the sustainability of a future green economy and consideration of green hydrogen and ammonia fuels, various issues are important like thermodynamics, kinetic barriers, material challenges, current science and technology readiness, and geographical locations for green hydrogen versus green ammonia as energy vectors (Matzen et al. 2015a, Allen et al. 2018). Green hydrogen Green hydrogen or green ammonia could be a carbon-free fuel in the future after overcoming the technological barriers for production and storage of hydrogen at high volume density. Hydrogen and ammonia can replace conventional liquid transportation fuels. Cheap fuel storage also means cheap transport. Ships for ammonia transport are already in wide use, whereas ships for liquid hydrogen require very complex designs. Hydrogen’s low volumetric energy density and flammability will likely hinder its wide deployment. In contrast, ammonia can potentially circumvent the technical shortcomings of hydrogen by having a high energy density by volume and narrow flammability range to allow an ammonia economy, but it is toxic and is, at present, energy intensive to produce and decompose (Demirel 2018, 2021, Wu et al. 2022). The adoption of hydrogen and ammonia can lead to the transition to a carbon-free economy, in a decentralized renewable energy network. Green hydrogen production has been industrialized for years, with the cost of electrolyzers and renewable electricity having dropped dramatically over the past decade. Green ammonia production, on the other hand, is a relatively new technology where large-scale demonstration projects have just begun to be considered. Moreover, to enable green ammonia to be produced in a decentralized fashion via renewable electricity, it is also necessary to have an agile and flexible cycle using a catalyst. To transport hydrogen over long distances, it is often common to convert the gaseous hydrogen to its liquid form that has higher volumetric energy density. However, the energy for such conversion would be considerable. For stationary applications, on the other hand, compressed gaseous hydrogen is more appropriate, given the space constraints are small. Gaseous hydrogen is also the present state of choice for hydrogen fuel cell vehicles, which contributes the bulk of the use, beyond the role of hydrogen in industrial chemical processes. Meanwhile, ammonia benefits from being more efficient to produce than liquid hydrogen at present, having a higher density than liquid hydrogen, and being cheaper to store. One should also consider life cycle assessment (LCA) to fully evaluate the environmental impacts of the production of hydrogen and ammonia during their entire life cycles. Considerations of green hydrogen or ammonia as an energy carrier requires understanding not only of the technoeconomic complexities but also of the environmental impacts of implementing large-scale production, storage, transport, and utilization, which must be identified and overcome (Wu et al. 2022).

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3.5  Hydrogen, Ammonia and Methanol Economy Hydrogen and methanol are two major chemicals, which can also serve as fuels and energy carriers. They also can assist in renewable energy storage and the conversion of carbon to chemical and fuels. For example, a new modular process based on microwave-plasma reactors can convert natural gas to acetylene and H2 without CO2 formation. The high single-pass conversion rates lead to a more compact reactor and overall simpler operation with straightforward downstream separation (Olah et al. 2011, Matzen et al. 2015a, Dincer et al. 2017, Demirel 2018). Hydrogen economy A hydrogen economy implies producing and using hydrogen as a clean fuel, energy carrier, and energy storage medium, in part because of its high energy density on a mass basis. At 1 atm and 300 K, hydrogen gas has a density of 8.2 × 10–5 kg/L, an approximate volumetric energy density of 12 kJ/m3 and a gravimetric energy density of 140 MJ/kg. This indicates that to store hydrogen, one requires either a very large volume or high compression (up to 700 atm, at which point the volumetric energy density is 5.6 MJ/L). If hydrogen is liquefied at a temperature below 20 K, it reaches to the volumetric energy density of approximately 10 MJ/L. However, a significant amount of energy is necessary to liquefy hydrogen. Metal hydrides also can store hydrogen effectively at ambient conditions. However, there are safety issues to manage in hydrogen storage and transportation. Hydrogen burns with an invisible flame and has a high flame temperature and speed. Because of this, it may be difficult to control hydrogen fires. The contribution of hydrogen toward clean energy depends on (1) replacing fossil fuel-based hydrogen production with low carbon production for blue hydrogen production and carbon free green hydrogen, (2) improved/new hydrogen storage and transportation technology, and (3) cost efficient technologies for electrolysis and fuel cells. The average levelized cost of green hydrogen is predicted to be around US$3.2/kg from solar PV and US$2.8/kg from wind in 2030, and US$2/kg from solar PV and US$1.2/kg from wind in 2050 (Demirel 2021). To produce one kg H2 by water splitting, approximately 26.7 kg water is necessary (Matzen et al. 2015). The hydrogen production cost is highly dependent on the electricity price, which constitute around 75% of the final cost. Table 3.6 shows the historical progress and future predictions based on a Transforming Energy Scenario (TES) of green hydrogen production and electrolyzer costs. Due to decarbonization desires, a new era of renewable energy generation, utilization, and storage at a very large scale is expected and doing so requires an innovative undertaking. As noted earlier, this includes efforts to address thermodynamics issues, kinetic barriers, material challenges, science and technology readiness, and geographical locations for green hydrogen and methanol as energy vectors (Cuellar-Franca and Azapagic 2015). Table 3.6.  Hydrogen and electrolyzer costs: historical and projections based on a Transforming Energy Scenario (TES) (IEA 2018). Technology cost

Historical Progress

TES (2030)

TES (2050)

Green hydrogen production cost (US$/kg)

4.0–8.0 (2015–2018)

1.8–3.2

0.9–2.0

770

540

370

Electrolyzer cost US$/kW

Green ammonia An obstacle to green ammonia production is the limited and relatively high cost of renewable power compared to the abundant and low-cost natural gas. Green ammonia as a hydrogen carrier is a complementary energy vector to hydrogen and may play an important role in long-term energy storage and long-distance energy transportation. Because hydrogen and ammonia are interchangeable via the Haber Bosch and cracking processes, they are not so much in conflict as in concert. Some predict that a green hydrogen economy and a green ammonia economy will occur concurrently to satisfy society’s diverse need for clean energy at large scale (Wu et al. 2022).

76  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence Methanol economy Methanol production from biomass-based syngas has an overall energy efficiency of around 55% based on higher heating value (HHV). Methanol from biomass is at least 2–3 times more expensive than fossil-fuel based methanol (Matzen et al. 2015b). Therefore, renewable hydrogen-based methanol would recycle carbon dioxide. There are already vehicles which can run with M85, a fuel mixture of 85% methanol and 15% gasoline. Methanol can be produced from synthesis gas (syngas) obtained from coal and natural gas, as well as by reductive hydrogenation of CO2. Methanol can be used with the existing distribution infrastructure of conventional liquid transportation fuels. The reported octane number of methanol is 108.7 as compared to that of gasoline, which is 91. This suggests that a methanol-air mixture can be compressed, which increases the efficiency of the engine and ensures cleaner emissions. Unlike gasoline and diesel, methanol does not emit any soot or smoke on combustion. Methanol has a higher “flame speed” than gasoline which enables faster and more complete fuel combustion in engine cylinders (Olah et al. 2011, Demirel 2018).

3.5.1  Methanol and the Environment Methanol is a polar liquid, and it can rapidly degrade both in aerobic and anaerobic conditions. Methanol is toxic and can be used as a fuel for blending or producing other fuels such as dimethyl ether. Methanol may be produced both from fossil fuels (natural gas and coal) and renewables. Methanol can be used in the direct methanol fuel cell. Renewable hydrogen-based methanol would recycle carbon dioxide as a possible alternative fuel to oil and gas. Methanol is also used as a chemical feedstock ultimately to fix carbon (Demirel 2018, Wang and Demirel 2018).

3.5.2  Methanol Economy versus Hydrogen Economy Methanol synthesis from water, renewable electricity, and carbon may lead to chemical storage of renewable energy, carbon recycling, and fixation of carbon in a chemical feedstock (Matzen et al. 2015b, Wu et al. 2022). Methanol can be an affordable alternative to hydrogen, as methanol has more hydrogen by mass in the same volume. Therefore, methanol may be a hydrogen carrier. However, producing green and blue hydrogen has high energy costs, in addition to the considerable energy needed for storage and transportation. Methanol can serve as a safer energy carrier, a relatively less polluting fuel, and a feedstock for the production of other chemicals/fuels (Gumber and Gurumoorthy 2018, Demirel 2021).

3.6  Economic Cost of GHG Emissions Acknowledging allowable pollution limits, the next step is to identify technologies that reduce pollution and comply with regulations. Some factors to consider follow: • Waste disposal and wastewater treatment costs are usually greater than air pollution and GHG abatement costs. • Emissions regulations vary widely by country and region. • The petroleum refining industry together with the food industry accounts for major GHG abatement costs and is responsible for waste disposal and wastewater treatment costs. For greenhouse gas emissions, various legislative requirements exist that specify emissions reduction goals with product or energy-specific benchmarks (Gillingham and Stock 2018).

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3.6.1  Index of Ecological Cost The exergy destruction number NEx is the ratio of the nondimensional exergy destruction number of the improved system to that of the conventional one, and can be expressed as (Crane et al. 2011, Demirel 2021):

N Ex =

Exi* Exc*

(3.15)

where subscripts i and c denote the improved and conventional cases, respectively, and Ex* is the nondimensional exergy destruction number, which is defined by Ex* =

exfd  o C p (3.16) mT

Here, exfd is the specific flow-exergy destruction and To is the reference temperature. The system is sustainable only if NEx is less than unity. The exhaustion of nonrenewable natural resources is called the index of ecological cost. To determine the domestic ecological cost ceco, the impact of imported materials and fuels is considered (Cornelia 2014, Demirel 2021). The degree of the negative impact of a process on natural resources can be characterized by means of the ecological efficiency ηe, where:

ηe =

Exc (3.17) ceco

Here, Exc is the gross consumption of the domestic nonrenewable natural resource. Usually, ηe < 1.

3.6.2  Ecological Cost The production, conversion, and utilization of energy may lead to air and water pollution, impacts on the use of land and rivers, thermal pollution due to waste heat, and climate change. The waste products may be harmful to agriculture, plant life, and human health. Several options exist for reducing ecological cost, such as nonrenewable energy conservation, energy efficiency improvement, increasing reliance on nuclear and renewable energy, and carbon capture, storage and utilization (CCSU) (Cuellar-Franca and Azapagic 2015). Some significant challenges to CSSU are the cost of building and operating capture-ready industrial facilities, the feasibility of permanently storing CO2 underground, the difficulty of constructing infrastructure to transport CO2 to injection sites and converting CO2 into chemicals (Crane et al. 2011, Cornelia 2014).

3.7 Thermoeconomics Thermoeconomics combines thermodynamic principles with economic analysis. Therefore, it may bring some fundamental changes in the economic evolution, design, and maintenance of processes. Thermal systems involve significant work and/or heat interactions with their surroundings and appear in almost every industrial plant. Consequently, the design of thermal systems requires the application of principles from thermodynamics, fluid mechanics, heat transfer, and engineering economics. Thermoeconomics usually utilizes exergy and economics for optimizing the design and operating conditions of thermal systems (Demirel and Gerbaud 2019, Demirel 2021). In the optimization, the cost of the thermal energy source plays an important role. Further, changes in fuel cost from one year to another and from place to place affect designs and hence economic considerations. Thermoeconomics assigns costs to exergy-related variables by using exergy cost theory and exergy

78  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence cost balances (Demirel and Ozturk 2006). For any process or subsystem, the specific cost of exergy c in $/kW-unit time for a stream is . . c = C/Ex (3.18) . . where C are Ex the cost rate and the rate of exergy transfer for a stream, respectively. However, the . total cost also includes the fixed capital investment C and the annual operating cost of the process FCI . COP. Hence, . . . CP = COP + CFCI (3.19) Then the cost rate balance for a single process can be written as

   ( ∑= i ci Exi )out ( ∑ i ci Exi )in + CP (3.20) Exergoeconomics Exergoeconomics converts monetary expenses into equivalent exergy flow rates and the optimization of energy usage for a process is based on these exergy flows. Such an extended representation may provide a consistent framework for including capital cost, labor cost, and environmental impact in an engineering optimization procedure (Sciubba et al. 2019, Demirel 2021, Hamrang et al. 2021). Many applications of exergoeconomics and other exergy-based economic techniques have been reported (Rosen 2011, Dincer and Rosen 2021). These have related to renewable energy technologies like solar (Mosleh et al. 2021, Khoshgoftar Manesh et al. 2021, Keshavarzzadeh et al. 2020). Other studies have been linked to fossil fuel technologies of a conventional nature as well as more novel devices like fuel cells (Sadeghi et al. 2021) and carbon capture (Talebizadehsardari et al. 2005). Investigations have also been reported for specific applications like buildings (Motaghian et al. 2021) and desalination (Hamrang et al. 2021). Given the importance of economics to optimization and the usefulness of exergoeconomics in optimization, many exergoeconomic investigations have involved optimization (Talebizadehsardari et al. 2020, Hamrang et al. 2021, Sadeghi et al. 2021, Motaghian et al. 2021, Keshavarzzadeh et al. 2020, Dincer et al. 2017).

3.7.1  Technoeconomic Analysis Technoeconomic analysis (TEA) involves three phases: pre-investment, investment, and operation. The pre-investment phase involves the identification of investment opportunities, preliminary selection of project ideas, project design, and final evaluation based on the expectations and requirements of various stakeholders. The investment phase involves several interdisciplinary tasks including contracting for the project plant, construction, detailed project design and cost estimates, feasibility assessments, and startup and trial operation. The operation phase involves costs of day-to-day operation and marketing the products (Keshavarzzadeh et al. 2020). TEA produces discounted cash flow diagrams (DCFDs), prepared for the required years of operation using current economic data, fixed capital investments, and operating and maintenance costs. The plant cost index (PCI) can be used to evaluate and update the costs and capacity to the present date as follows: x

Cost New = Cost Old

PCI New  CapacityNew    (3.21) PCI Old  CapacityOld 

where x is a factor, which is usually assumed to be 0.6. A possible depreciation method is the Maximum Accelerated Cost Recovery System. After estimating the revenue, DCFDs are prepared to determine three profitability criteria: net present value, payback period, and rate of return. At least two out of three criteria should be favorable for feasible operation. Plant capacity and the years of operation affect the feasibility. Market energy prices can become high if supply does not meet demand. It is also possible that excess supply causes energy prices to fall below production costs.

Economic Sustainability  79

This can make analyzing the cost of renewable power generation technologies unreliable for some locations and times (Matzen et al. 2015b). Stochastic analysis of economic performance A stochastic model such as Monte-Carlo method helps account for the viability of economic data due to the uncertainties over the operational time frame. The stochastic model produces probability density distributions of important profitability criteria. Risk assessment Risk analysis involves financial, environmental, technical, and social risk elements, which are interrelated (i.e., technical, and financial). If, for example, a biorefinery is a stand-alone plant, availability and failure risk are relatively smaller than if a biorefinery integrated into an industrial plant; in such an instance, the financial risk created by the industrial plant could be disruptive (Allen et al. 2018). Sensitivity analysis There is a large uncertainty gap when it comes to utilizing the technology developed in a lab environment and its full-scale operation in an industrial setting. Therefore, care should be exercised for cost calculations to predict realistic trends and achieve better determinations of production feasibility (Matzen and Demirel 2016). For instance, no plants have yet been built at an industrial scale leading to information on the actual costs associated with the construction and operation of a biorefinery plant. Technoeconomic calculations may differ among the methods employed because their extrapolation on economic data often mismatches inherent assumptions. Summary Economic sustainability refers to the ability of an economy to remain feasible in producing the required results, e.g., goods and services, without unreasonable negative impacts on ecological and social sustainability. That implies economic growth toward increased standards of living for all. Nations traditionally aim and plan to increase their gross domestic product to avoid stagnation and recession. But such an approach is not necessarily sustainable. A bioeconomy and circular economy are often considered to be integral parts of economic sustainability. The chapter aims to enhance understanding of economic sustainability, the circular economy and the bioeconomy through extensive coverage of these topics as well as the green economy. The discussion of economic sustainability considers topics like energy return on investment, renewable energy costs and the levelized cost of electricity. Case studies are presented on hydrogen and methanol economies. The economic cost of carbon emissions is also discussed, including the index of ecological cost. Finally, thermoeconomics and other technoeconomic analysis methods are described. The bottom line of this chapter is that economic sustainability is an essential part of sustainability, as it represents the ability of an economy to remain feasible in producing the required economic outputs, e.g., goods and services, without unreasonable negative impacts on environmental, ecological and social sustainability. An important approach to economic sustainability is premised on decoupling environmental degradation and economic growth, as they are usually incompatible. The economic importance of nature, including the resources derived from it and the repository it provides for waste emissions, is of great significance. The provision of such ecosystem services highlights how resources in finite natural world that cannot be regarded as both unlimited and free, if sustainable development is a desired end. There is increasing appreciation of the importance of economic sustainability, as some leaders in corporate, government and other areas have embedded or begun to embed sustainability into strategies and actions.

80  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence

Nomenclature AI Artificial intelligence CCSU Carbon capture, storage, and utilization CE Circular economy CRF Capital recovery factor DCFD Discounted cash flow diagram EROI Energy return on investment GHG Greenhouse gas HHV Higher heating value LCA Life cycle assessment LCOE Levelized cost of electricity LHV Lower heating value MSW Municipal solid waste O&M Operation and maintenance PCI Plant cost index PEF Polyethylene furanoate PET Polyethylene terephthalate PG Price gap PV Photovoltaic TBL Triple bottom line TEA Technoeconomic analysis TES Transforming Energy Scenario VALCOE Value-adjusted levelized cost of electricity VCI Value chain index WEM World Energy Model WTA Willingness to accept WTP Willingness to pay

Problems 3.1 Economic sustainability is neglected by some in assessing how sustainable a situation is and how to make it more sustainable. Identify the benefits of considering economic sustainability. Identify reasons for the reluctance to consider economic sustainability by some. 3.2 Economic sustainability often is interpreted differently by different groups. Explain how economic sustainability is typically interpreted by (a) a private company, (b) a public company, (c) people in an impoverished community, (d) a government, and (e) sustainability researchers. 3.3 Assess the economic sustainability of a specific industry that has not been examined previously in the literature. What are the main economic issues detracting from economic sustainability in that industry and how can they be mitigated? 3.4 What is the difference between the cost to make a product in an engineering operation, and the price charged for that product? 3.5 Identify a process for hydrogen production based on (a) fossil fuels, (b) land-based biomass, (c) biomass harvested from a marine environment, (d) wind energy, and (e) nuclear energy. Rank them in terms of costs, using typical values available for the present year. 3.6 Some investigators are considering potential methods for methanol production from algae, where the algae is grown on a body of water and then is chemically converted first to hydrogen and then to methanol via methanol synthesis. Determine the costs of such a process and compare them with the more conventional production of methanol from natural gas. Describe the sustainability attributes of each process.

Economic Sustainability  81

3.7 We observe temporal and spatial variations in many economic parameters, e.g., equipment costs, raw material prices, commodity prices, labor rates, and interest rates. Given that, are the results of an economic analysis conducted two years ago still useful today, or next year, or in ten years? 3.8 When comparing manufacturing systems for a given product, how can you use the results of an economic analysis performed today to select and purchase manufacturing system today among competing options, if all options have an expected lifetime of 10 years?

Research Projects 3 .1 What are the differences between bioeconomy and circular economy? 3.2 What are the differences between bioeconomy and green economy?

References Allen, J.W., Unlu, S., Demirel, Y., Black P. and Riekhof, W. 2018. Integration of biology, ecology, and engineering for sustainable algal based biofuel and bioproduct biorefinery. Bioresources & Bioprocessing 5(47): 1–28. Benigno, G., di Giovanni, J., Groen, J.J.J. and Noble, A.I. 2022. A new barometer of global supply chain pressures. Federal Reserve Bank of New York Liberty Street Economics, January 4, 2022. Bosch, R., van de Pol, M. and Philp, J. 2015. Define biomass sustainability. Nature 523: 526–527. Brockway, P.E., Owen, A., Brand-Correa, L.L. et al. 2019. Estimation of global final-stage energy-return-oninvestment for fossil fuels with comparison to renewable energy sources. Nat. Energy 4: 612–621. Cabrera, A. and Cabrera, E.F. 2002. Knowledge-sharing Dilemmas. Organization Studies 23: 687–710. Cornelia, P.G. 2014. True cost economics: Ecological footprint. Procedia Economics and Finance 8: 550–555. Crane, K., Curtright, A.E., Ortiz, D.S., Samaras, C. and Burger, N. 2011. The economic costs of reducing greenhouse gas emissions under a U.S. national renewable electricity mandate. Energy Policy 39: 2730–2739. Cuellar-Franca, R.M. and Azapagic, A. 2015. Carbon capture, storage and utilisation technologies: A critical analysis and comparison of their life cycle environmental impacts. Journal of CO2 Utilization 9: 82–102. Demirel, Y. and Ozturk, H.H. 2006. Thermoeconomics of seasonal heat storage system. International Journal of Energy Research 30: 1001–1012. Demirel, Y. 2017. Lignin for sustainable bioproducts and biofuels. Journal of Biochemical Engineering & Bioprocess Technology 1: 1–3. Demirel, Y. 2018. Biofuels. Vol. 1., Part B. pp. 875–908. In: Dincer (ed.). Comprehensive Energy Systems. Elsevier, Amsterdam. Demirel, Y. and Gerbaud, V. 2019. Nonequilibrium Thermodynamics: Transport and Rate Processes in Physical, Chemical and Biological Systems. 4th ed., Elsevier, Amsterdam. Demirel, Y. 2021. Energy: Production, Conversion, Storage, Conservation, and Coupling. 3rd ed. Springer, London. Dincer, I., Rosen, M.A. and Ahmadi, P. 2017. Optimization of Energy Systems. Wiley, London. Dincer, I. and Rosen, M.A. 2021. Exergy: Energy, Environment and Sustainable Development. 3d ed. Elsevier, Oxford, UK. EIA. 2018. Energy Information Administration, International Energy Outlook. www.eia.gov. EERE. 2020. Advancing the Bioeconomy: From Waste to Conversion-Ready Feedstocks. Workshop Summary Report, Office of Energy Efficiency & Renewable Energy, US Department of Energy, 2020. Gagnon, B., Leduc, R. and Savardi, L. 2008. Sustainable development in engineering: a review of principles and definition of a conceptual framework. Environmental Engineering Science 26: 1459–1472. Galvao, G.D.A., De Nadae, J., Clemente, D.H., Chinen, G. and De Carvalho, M.M. 2018. Circular economy: Overview of barriers. Procedia CIRP 73: 79–85. Geissdoerfer, M., Savaget, P., Bocken, N. and Hultink, E.J. 2016. The circular economy: A new sustainability paradigm? Journal of Cleaner Production 143: 757–768. Gillingham, K. and Stock, J.H. 2018. The cost of reducing greenhouse gas emissions. Journal of Economic Perspective 32: 53–72. Guerry, A.D. et al. 2015. Natural capital and ecosystem services informing decisions: From promise to practice. PNAS 112(24): 7348–7355. Gumber, S. and Gurumoorthy, A.V.P. 2018. Methanol economy versus hydrogen economy. Methanol Science and Engineering, Chapter 25: 661–674.

82  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence Hall, C.A.S., Lambert, J.G. and Balogh, S.B. 2014. EROI of different fuels and the implications for society. Energy Policy 64: 141–152. Hall, C.A.S. 2016. Energy Return on Investment: A Unifying Principle for Biology, Economics, and Sustainability. Lecture Notes in Energy, Volume 36. Springer, London. Hamrang, F., Mahmoudi, S.M.S. and Rosen, M.A. 2021. A novel electricity and freshwater production system: performance analysis from reliability and exergoeconomic viewpoints with multi-objective optimization. Sustainability 13(11): 6448. IEA (International Energy Agency). 2018. World Energy Outlook. https://www.iea.org/reports/world-energyoutlook-2018. Issa, I., Delbruck, S. and Hamm. 2019. Bioeconomy from experts’ perspectives—Results of a global expert survey. PLoS ONE 14: e0215917. Izvercianua, M., Șerana, S.A. and Branea, A-M. 2014. Prosumer-oriented value co-creation strategies for tomorrow’s urban management. Procedia - Social and Behavioral Sciences 124: 149–156. Kalmykova, Y., Sadagopan, M. and Rosado, L. 2018. Circular economy—From review of theories and practices to development of implementation tools. Resources, Conservation & Recycling 135: 190–201. Keshavarzzadeh, A.H., Ahmadi, P. and Rosen, M.A. 2020. Technoeconomic and environmental optimization of a solar tower integrated energy system for freshwater production. Journal of Cleaner Production 270: 121760. Khoshgoftar Manesh, M.H., Abdolmaleki, M., Vazini Modabber, H. and Rosen, M.A. 2021. Dynamic advanced exergetic, exergoeconomic and environmental analyses of a hybrid solar city gate station. Journal of Energy Resources Technology 143(10): 102105. Korhonen, J., Honkasalo, A. and Seppälä, J. 2018. Circular economy: the concept and its limitations. Ecological Economics 143: 37–46. Kumar, A., Demirel, Y., Jones, D.D. and Hanna, M.A. 2010. Optimization and economic evaluation of industrial gas production and combined heat and power generation from gasification of corn stover and distillers grains. Bioresource Technology 101: 3696–3701. Liu, G. and Bao, J. 2017. Evaluation of electricity generation from lignin residue and biogas in cellulosic ethanol production. Bioresour. Technol. 243: 1232–1236. Lopes, M.S.G. 2015. Engineering biological systems toward a sustainable bioeconomy. J. Ind. Microbiol. Biotechnol. 42: 813–838. Matzen, M., Alhajji, M. and Demirel, Y. 2015a. Technoeconomics and sustainability of renewable methanol and ammonia productions using wind power–based hydrogen. Advanced Chem. Eng. 5: 128. Matzen, M., Alhajji, M. and Demirel, Y. 2015b. Chemical storage of wind energy by renewable methanol production: Feasibility analysis using a multi-criteria decision matrix. Energy 93: 343–353. Matzen, M. and Demirel, Y. 2016. Methanol and dimethyl ether from renewable hydrogen and carbon dioxide: Alternative fuels production and life-cycle assessment. J. Cleaner Production 139: 1068–1077. Mosleh, H.J., Behnam, P., Kamazani, M.A., Mohammadi, O., Kavian, S., Ahmadi, P. and Rosen, M.A. 2021. A comprehensive comparative investigation on solar heating and cooling technologies from a thermo-economic viewpoint—a dynamic simulation. Energy Science Engineering 9: 724–742. Motaghian, S., Monajati Saharkhiz, M.H., Rayegan, S., Pasdarshahri, H., Ahmadi, P. and Rosen, M.A. 2021. Techno-economic multi-objective optimization of detailed external wall insulation scenarios for buildings in moderate-dry regions. Sustainable Energy Technologies and Assessments 46: 101256. Nandiwale, K.Y., Danby, A.M., Ramanathan, A., Chaudhari, R.V., Motagamwala, A.H., Dumesic, J.A. and Subramaniam, B. 2020. Enhanced acid-catalyzed lignin depolymerization in a continuous reactor with stable activity. ACS Sustainable Chem. Eng. 10: 4096–4106. Nguyen, N. and Demirel, Y. 2013. Economic analysis of Biodiesel and glycerol carbonate production plant by glycerolysis. J. Sustainable Bioenergy Systems 3: 209–216. NRC (National Research Council). 2011. Renewable Fuel Standard: Potential Economic and Environmental Effects of U.S. Biofuel Policy. Washington, DC: The National Academies Press. Olah, G.A., Goeppert, A. and Prakash, G.S. 2011. Beyond Oil and Gas: The Methanol Economy. John Wiley & Sons, Weinheim. Paes, L.A.B., Bezerra, B.S., Deus, R.M., Jugend, D. and Battistelle, R.A.G. 2019. Organic solid waste management in a circular economy perspective—A systematic review and SWOT analysis. Journal of Cleaner Production 239: 118086. Pagoropoulos, A., Pigosso, D.A. and McAloone, T.C. 2017. The emergent role of digital technologies in the Circular Economy: A review. Procedia CIRP 64: 19–24. Polasky, S., Kling, C.L., Levin, S.A., Carpenter, S.R. et al. 2019. Role of economics in analyzing the environment and sustainable development. PNAS 116: 5233–5238.

Economic Sustainability  83 Ptasinski, K.J. 2016. Efficiency of Biomass Energy. An Exergy Approach to Biofuels, Power, and Biorefineries. Wiley, New York. Rosen, M.A. 2011. Economics and Exergy: An Enhanced Approach to Energy Economics. Nova Science Publishers, Hauppauge, NY. Sadeghi, M., Jafari, M., Mahmoudi, S.M.S., Yari, M. and Rosen, M.A. 2021. Thermoeconomic analysis and multi-objective optimization of a solid oxide fuel cell plant coupled with methane tri-reforming: effects of thermochemical recuperation. International Journal of Energy Research 45(7): 10332–10354. Sciubba, E. 2019. Exergy-based ecological indicators: From thermo-economics to cumulative exergy consumption to thermo-ecological cost and extended exergy accounting. Energy 168: 462–476. Strawn, T. 2021. Corporate progress and action on diversity, equity, and inclusion. The Sustainability Institute by ERM September: 1–46. Talebizadehsardari, P., Ehyaei, M.A., Ahmadi, A., Jamali, D.H., Shirmohammadi, R., Eyvazian, A., Ghasemi, A. and Sciubba, E. 2005. Exergo-economics: thermodynamic foundation for a more rational resource use. Int. J. Energy Res. 29: 613–636. Talebizadehsardari, P., Ehyaei, M.A., Ahmadi, A., Jamali, D.H., Shirmohammadi, R., Eyvazian, A., Ghasemi, A. and Rosen, M.A. 2020. Energy, exergy, economic, exergoeconomic and exergoenvironmental (5E) analyses of a triple cycle with carbon capture. Journal of CO2 Utilization 41: 101258. Tura, N., Hanski, J., Ahola, T., Ståhl, M., Piiparinen, S. and Valkokari, P. 2019. Unlocking circular business: A framework of barriers and drivers. Journal of Cleaner Production 212: 90–98. Unlu, S., Niu, W. and Demirel, Y. 2020. Bio-based adipic acid productions: Feasibility analysis using a multi-criteria decision matrix. Biofuels, Bioproducts & Biorefining (Biofpr) 14: 794–807. doi: 10.1002/bbb.2106. Velenturf, A.P.M. and Purnell, P. 2021. Principles for a sustainable circular economy. Sustainable Production and Consumption 27: 1437–1457. Wang, X. and Demirel, Y. 2018. Feasibility of power and methanol production by an entrained-flow coal gasification system. Energy & Fuels 32: 7595–7610. Withers, J., Quesada, H. and Smith, R.L. 2017. Bioeconomy survey results regarding barriers to the United States Advanced Biofuel Industry. BioRes. 12: 2846–2863. Wu, S., Salmon, N., Meng-Jung Li, M., Bañares-Alcántara, R. and Tsang, S.C.E. 2022. Energy decarbonization via green H2 or NH3? ACS Energy Lett. 7: 1021–1033. Zabaniotou, A. 2018. Redesigning a bioenergy sector in EU in the transition to circular waste-based Bioeconomy—A multidisciplinary review. Journal of Cleaner Production 177: 197–206. Ziemba, E. and Eisenbardt, M. 2015. Prosumers’ participation in business processes. Online Journal of Applied Knowledge Management 3: 1–14.

Chapter 4

Societal Sustainability INTRODUCTION and OBJECTIVES This chapter covers societal sustainability topics as societal well-being, social responsibility, advancing social sustainability and the human development index. Also covered are the ideas of social investment and the social cost of carbon emissions. To enhance understanding and potential uses for broad-based strategies for more sustainable social systems, details are given on improved education and the political empowerment of women, especially in developing countries; greater regard for social justice, notably equity between rich and poor both within and between countries; and intergenerational equity. The main objectives of the chapter are to enhance understanding of:

• • • •

Social responsibility Human development index Social investments Social cost of greenhouse gas emissions

4.1  Societal Sustainability Societal or social sustainability is a measure of human welfare, which in turn is a measure of how well a person can meet her/his needs and hopefully flourish and achieve the desired living standards and lifestyle. This includes intergenerational equity, which requires that we only use the natural resources we need and leave the rest to future generations. For social sustainability, we must endeavor to increase the standards of living of people who lack shelter, clean water, and adequate food to survive. Additional elements affecting social sustainability are population growth, human health, cultural needs, and a clean environment. These elements have a general direct impact on human well-being and cannot be ignored in favor of economic prosperity in the short term. Broad-based strategies for more sustainable social systems include improved education, especially in developing countries, and the political empowerment of women; greater regard for social justice, notably equity between rich and poor both within and between countries; and intergenerational equity. Depletion of natural resources including fresh water increases the likelihood of “resource wars”. A major hurdle to achieve sustainability is the alleviation of poverty. It has been widely acknowledged that poverty, beyond its severe effects on people, is one source of environmental degradation. Societal sustainability requires humans to assume an operational burden to create better health, wealth, and education toward increasing quality of life. This may be achieved by capacity planning

Societal Sustainability  85

Social 1D Socio-Economic

Socio-Ecologial 2D

Environment 1D

3D Sustainable Eco-Economic 2D

2D

Economic 1D

Figure 4.1.  Sustainability at the confluence of its three dimensions: the triple-bottom-line (TBL).

within the system by optimization of a social welfare function and welfare economics. Social Being out affectsbased everyone negatively. Instead,(Gagnon for sustainability dimensions mayleft be described on Figure 4.1 as follows et al. 2008): Society: Offer individuals and communities the opportunity to increase their capabilities. Socio‑Ecological (2D): A diverse, equitable, and inclusive workplace • Preserve access to ecosystems services essential to health and wellbeing. inclusionand can be • Look beyond your with own locality immediate future. impact • Ensure that all material and energy inputs and outputs are inherently benign and safe.

Socio‑Economic (2D): • Know your needs. • Focus on achieving needs of larger number of individuals. • Allocate in a fair manner benefits and costs related to economic activity and public policies. Being left out affects everyone negatively. Instead, for a sustainable society, we need to establish and maintain inclusion, diversity, and equality to live with dignity regardless of ethnicity, gender, and religion. Social challenges often involve international and national laws, urban planning and transport, local and individual lifestyles, and ethical consumerism. A diverse, equitable, and inclusive workplace improves the economic impact of a company. Diversity with inclusion can be profitable for business. Inclusive organizations promote transparency. A diverse, equitable, and inclusive workplace improves the economic impact of a company. Diverse and inclusive teams promote a trustworthy brand image. More diversity and representation lead to understanding and accommodation of different opinions from stakeholders and customers (Keil et al. 2007). There are numerous factors to be considered in determining if a system or a project is socially sustainable (McDermott et al. 2013, Blaber-Weng et al. 2015, Gholami et al. 2019): • Equity • ○ Reduce disadvantage•and assist community members to have more control over their lives, socially and economically ○ Identify the causes of•disadvantage and inequality and reduce them Improve education, training, • Improve access to transport, ○ Meet the needs of any disadvantaged and marginalized people ○ Deliver without bias and promote fairness 2

86  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence • Diversity ○ Identify and recognize diversity within cultural, ethnic, and racial groups to meet their needs ○ Allow for diverse viewpoints, beliefs, and values to be taken into consideration ○ Promote understanding and acceptance of diverse backgrounds, cultures, and customs • Social cohesion ○ Develop and support a sense of belonging in the broader community ○ Increase participation in social activities by individuals and improve the access to public and civic institutions ○ Build links between the diverse groups in the broader community and contribute towards the community. • Quality of life ○ Improve affordable and appropriate housing opportunities, and physical and mental health outcomes ○ Improve education, training, skill development opportunities, and employment opportunities ○ Improve access to transport, the ability to meet basic needs, safety, and security ○ Improve access to community amenities and facilities ○ Allow for democracy and sound governance, allow for a diverse range of people to participate in decision-making processes ○ Facilitate easy and clear decision-making by staff and stakeholders ○ Allocate sufficient budget to ensure adequate delivery by qualified trained staff The UN’s environmental blueprint from the Earth Summit identifies patterns of consumption and production as a chief cause of global environmental degradation. A potential future danger is the link between consumption and welfare that may bridge psychological/social treatments and economic ones. The de-coupling effort questions the benefits of high levels of consumption. Informing households about structural, technological, economic, and social constraints on their consumption and energy usage may help shift toward sustainable energy consumption (UNDESA 2019). According to the UN Global Compact (https://www.unglobalcompact.org), aiming for social sustainability can help businesses in many ways, including: • Unlocking new markets and helping retain, and attract business partners • Becoming the source of innovation for new product or service lines • Raising internal morale and employee engagement • Improving risk management • Improving company-community interactions The UN Global Compact considers human rights as the cornerstone of social sustainability. This extends to areas including labor, gender equality, children, indigenous peoples, and education. When workers are paid fairly and work under safe conditions, they are healthier and more productive. The revised environmental and social sustainability framework of the United Nations Environmental Programme (https://www.unep.org/) enables management of environmental and social impacts throughout the life of a project. The framework serves four broad purposes: • To enhance outcomes by systematically integrating environmental and social dimensions in UNEP-funded programs and projects. • To strengthen alignment of UNEP’s work with the Sustainable Development Goals (SDGs) in addressing environmental and social sustainability of development efforts.

Societal Sustainability  87

• To set standards of sustainability for UNEP’s operations, thereby confirming UNEP’s accountability to its member states. • To enable UNEP to work in a safer manner, thereby minimizing risks to beneficiaries. The framework serves its broad purposes by employing guiding principles: (i) leave no one behind, (ii) a human rights and gender equality-based approach, (iii) sustainability and resilience principles, and (iv) accountability plus eight Safeguard Standards. The Safeguard Standards are as follows:

• • • • • • • •

Biodiversity conservation, natural habitats, and sustainable management of living resources Climate change and disaster risks Pollution prevention and resource efficiency Community health, safety, and security Cultural heritage Displacement and involuntary resettlement Indigenous peoples Labor and working conditions

These safeguard standards reflect UNEP’s “do good” as well as “do no harm” commitments to involve interested stakeholders and the public. Any project-related concerns can be raised through guidance on compliance. The following UNEP policies are related to the framework:

• • • • •

Policy guidance on environment, human rights, and addressing inequalities Indigenous people policy Policy and strategy on gender equality and the environment Promoting greater protection for environmental defenders Partnership policy

4.1.1  Societal Well-Being A major social benefit arising from the presence of a successful process industry unit is the dissemination of skills and know-how, which are used in the community to create wealth and enhance quality of life. It is difficult to quantify these benefits, but estimates may be made to include items such as (i) net value to community of freely published information and know-how, (ii) net value to community of training given to contractors and suppliers, and (iii) net value to community of training given to employees. Value may be estimated by considering what it has cost the company to generate the benefit on the one hand, and what society might be willing to pay for it on the other (UNDESA 2019). Human health Ax noted earlier, carcinogenic effects are offered as a default set, and some of high values the potency factors for this category are listed in Table 4.1. These values are the reciprocal of the Occupational Exposure Limits (OELs) (IChemE 2002), with the OEL for benzene as the normalizing factor, according to: Potency factor of substance = (OEL benzene/OEL substance). Chemicals with OEL > 500 mg/m3 have a minimal impact on the total weighted impact. The unit of environmental burden is ton/year benzene equivalent (UNDESA 2019).

88  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence Table 4.1.  Human health potency factors (IChemE 2002). Compound

Potency Factor, pf

Acrylamide

53.3

Acrylonitrile

3.6

Arsenic & compounds except arsine

160

Azodicarbonate

16

Benzene

1

Bis (chloromethyl) ether

3,200

Cadmium oxide fume

640

1 Chloro-2,3-epoxypropane

8.4

1,2-Dibromoethane

4.1

2-2’-Dichloro-4,4’-methylene dianiline Diethyl sulphate

3,200 50

Hydrazine

533.3

Maleic anhydride

16

4-4’-Methylenedianiline

200

Phthalic anhydride

4

Polychlorinated biphenyls (PCB)

160

o-Toluidine

18

Triglycidyl isocyanurate (TGIC)

160

Trimellite anhydride 400

400

*  A unique numerical identifier of a chemical substance assigned by the Chemical Abstracts Service (CAS)

4.1.2  Social Responsibility Social responsibility expects that industry treat the public well, in the form of employees, suppliers, contractors, and customers, and manage its impact on society at large in a sociological feedback system (Figure 4.2). Some responsibilities are: (i) coordination with external stakeholders concerning company’s operations, (ii) providing indirect benefit to the community from presence of an operating unit in $/year, (iii) managing complications registered from members of the public External stakeholders include customers, andactions other community groups, local government, and for concerning processes or products, andresidents (iv) legal taken against a company or employees non-governmental organizations. work-related incidents or practices. External stakeholders include customers, residents and other community groups, local government, and non-governmental Workplace responsibility considers the employment situation and organizations. involves the following factors: (i) Workplace responsibility situation and following factors: number of employees who haveconsiders resigned the or employment been made redundant per involves year, (ii) the number of direct (i) numberpromoted of employees who resigned been madeabsence redundant per year, (ii) number of direct employees per year, (iii)have working hoursorlost through per year (all unplanned causes strikes, sickness, and absenteeism, notworking holiday orhours training), indicative wage per and year benefit(all packages, employees promoted per year,but (iii) lost (iv) through absence unplanned

(v) indicative wage and benefit packages for lowest paid 10% of employees. External disturbances on policy actions Decentralized decision making with diverse inputs

Complex infrastructures

Feedback Figure 4.2.  Sociological feedback loop.

External disturbances on desired outcomes Multiple flows Outside disturbance on measurements

Societal Sustainability  89

causes—strikes, sickness, and absenteeism, but not holiday or training), (iv) indicative wage and benefit packages, and (v) indicative wage and benefit packages for lowest paid 10% of employees.

4.1.3  Advancing Social Sustainability Various investigators have examined numerous social aspects of sustainability. In the energy field, for instance, Bahrami Ziabari et al. (2021) investigated the social acceptance of renewable energy technologies in ecotourism. In the environmental and ecological domains, Alvarez-Risco et al. (2019) examined how environmental social influence, pro-environmental personal norms and environmental self-identity are determinants of ecological buying behavior. Psychological factors affecting how people react to and behave regarding sustainability have received much attention recently. For instance, the myriad of psychological processes in the science of sustainable development has been elucidated (Di Fabio and Rosen 2018). Also, an exploratory study was reported of a new psychological instrument for evaluating sustainability, based on a sustainable development goals psychological inventory (Di Fabio and Rosen 2020). On the environmental dimension of sustainability, individual differences in connectedness to nature were examined, focusing on personality and gender differences (Di Fabio and Rosen 2019). The impact on students have been investigated of various facets of social sustainability, including the expectations and interests of university students about sustainable development goals (Alvarez-Risco et al. 2021b) as well as the green entrepreneurship intentions in business university students in pandemic times (Alvarez-Risco et al. 2021a). Social progress has the highest importance and impact levels. The goal of an engineering project should be to create a system, a device, a process, or any other outcome that would provide a certain service or benefit to society. One of the important outcomes of an engineering project is the creation of technology, which can be the interface to connect an idea realized through design and engineering effort with outcomes of products or services. Applications of sustainability and resiliency in the engineering disciplines help engineers to select the best alternative for each design that needs engineering and scientific analyses, and hence contribute towards global sustainable development. Eventually sustainable engineering becomes an asset to the community and increases • the quality of life as shown in the following examples: • Heightened awareness of• issues in areas of sustainability (e.g., global warming, ozone layer depletion, deforestation, pollution, issues, fair trade, and gender equity) (Figure 4.3). placed onethical the potential tradeStrong ability to of apply engineering and • Clear understanding of the• role and impacts various aspects ofdecisionengineering (design, technology, related problems. etc.) and engineering decisions on environmental, societal, and economic problems. Emphasis • Demonstrated ca is placed on the potential trade-offs between environmental, social, and economic objectives. practice of engineering.

Recognize

Interconnect

Environment Economical Social Cultural Political Spatial Intergenerational

Implement

Plan

Figure 4.3.  Integration of concepts of equity. Figure 4.3. Integration of concepts of equity

90  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence • Strong ability to apply engineering and decision-making tools and methodologies to sustainability-related problems. • Demonstrated capacity to distinguish professional and ethical responsibilities associated with the practice of engineering.

4.1.4  Human Development Index The human development index (HDI) is a commonly used composite index of well-being and is calculated using four measures of societal well-being: life expectancy at birth, adult literacy, combined educational enrollment, and gross national income (GNI) per capita for standard living (HDI 2021). Life expectancy at birth assesses the health dimension. Years of schooling for adults aged 25 years and more, as well as expected years of schooling, refer to knowledge. The HDI is calculated as the geometric mean of life expectancy, education, and GNI per capita in which the education dimension is the arithmetic mean of mean years of schooling and expected years of schooling for children, in the assessment of the education dimension (UNDESA 2019, HDI 2021, Barro and Lee 2018). HDI emphasizes that people and their capabilities should be the basis for assessing the development of a country, not economic growth alone (Figure 4.4). This means that two countries with the similar levels of GNI per capita can have different HDIs, mainly because of national policy choices toward a decent standard of living for everybody. GNI per capita assesses the standard of living dimension. Note that a better and more comprehensive description of a country’s level of human development would require reflection on inequalities, poverty, human security, and empowerment, which are not considered by HDI. Long/ Long andhealthy healthylife life

Knowledge

A decent standard of living

Life expentancy at birth

Expected years of schooling/ Mean years of schooling

Gross national income per capita

Dimension Index

Life expectancy

Years of schooling

Income/Consumption

Inequality adjusted index

Inequality-adjusted life expectancy index

Inequality-adjusted education index

Inequality-adjusted income index

Dimensions Indicators

Inequality-adjusted Human Development Index (HDI)

Figure 4.4.  Graphical presentation for determination of the human development index (HDI).

4.2  Social Investment 4.2 Social investment

Broad-based strategies for sustainable more sustainable systems Broad-based strategies for more social social systems includeinclude peace, security, social justice, improved education, improved education, and the political empowerment of women. Equity between rich and poor both andand intergenerational equity withinand andbetween betweencountries countries intergenerational equity would also support social investment. natural resources including fresh water increases the likelihood of “resource conflict Depletion of natural resources including fresh water increases the likelihood of “resource conflicts”. sustainability has been referred to as environmental security This aspect of sustainability has been referred to as environmental security and creates a clear need for global environmental agreements manage resources such aquifers and riverset which span boundaries, and to protect shared to global systems including theas environment (Tirado al. political boundaries, and to protect shared global systems including the environment (Tirado et al. et al. 2019) 2015, Gholami et al. 2019).

Societal Sustainability  91

Equity, diversity, and inclusion A diverse, equitable, and inclusive society can help improve societal sustainability because of the following (Thomas 2006, Tomei et al. 2015): 1. Equity and inclusion help create equitable and inclusive communities. This helps everyone attain the support they require and feel as a part of caring and successful society. 2. Inclusive leaders possess higher cultural intelligence and skills to manage diversity to effectively communicate with many individuals from different backgrounds, externally as well as internally. 3. Diversity helps build better strategies to better understand the positive or negative impact on society. This builds more trust and helps the individual feel safe and be more productive toward a sustainable society. 4. Diverse teams are often more innovative and better prepared to take bold actions like changing behaviors within the community towards more sustainable choices. Sustainability and poverty A major hurdle to achieve sustainability is the alleviation of poverty, which is one source of environmental degradation. One approach to sustainable living, exemplified by small-scale urban transition towns and rural ecovillages, seeks to create self-reliant communities based on principles of simple living, which maximize self-sufficiency, particularly in food production. These principles, on a broader scale, underpin the concept of a bioregional economy. Other approaches, loosely based around new urbanism, are successfully reducing environmental impacts by building an environment to create and preserve sustainable cities, which support sustainable transport. An eco‑municipality may adopt a particular set of sustainability principles as guiding municipal policy and typically commits to a bottom-up, participatory approach for implementing this (Blaber-Wegg et al. 2015, Tirado et al. 2015).

4.2.1  Energy Return on Investment Society The ratio of useful energy output returned to society to invest to produce that useful energy is called the Energy Return on Investment (EROI) for society. This parameter helps assess the energy cost to society and its impact on development, and can be expressed as follows: EROISociety =

Energy returned to society ER = (4.1) Energy invested to get this energy EI

Here the numerator (ER) is composed of a nation’s gross domestic product (GDP) multiplied by the energy used in the generation of that GDP. The denominator (EI) is composed of the total energy consumed by that nation each year multiplied by the cost of the acquisition of that energy. There is a strong correlation between the EROI and societal well-being. High values of EROI mean that funds can be redirected toward societal needs, such as health and educational. A minimum EROI of 3 is thought by many to be required for an energy source to be beneficial to society. Indicators of quality of life can include health expenditures, gender inequality index, literacy rate, and access to water (Hall et al. 2014).

4.3  Social Cost of Carbon Emissions Climate change is causing serious and sometimes devastating impacts, including extreme weather events like flooding and deadly storms, the spread of disease, sea level rise, and increased food insecurity. These impacts can cost businesses, individuals and families, governments, and taxpayers considerably through rising health care costs, destruction of property, and increased food prices.

92  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence The social cost of carbon (SCC) translates a unit of CO2 emissions into an economic damage and is estimated by Integrated Assessment Models (IAMs) using approaches based on the natural and social sciences. The IAMs measure the direct costs of average temperature increases. Extreme weather and ecosystem changes are particularly damaging. The SCC and the IAMs are affected by the survey questions on the magnitude of climate impacts and discount rates through the assumptions of the climate damage function. The models are based on climate science, demographics, and economics; some parts of the calculation also require researchers/users to make assumptions that contain value judgements. Therefore, modeling choices affect the value of the SCC. The modeling must also incorporate projections of future economic growth. The modeling process can be repeated with an additional amount of emissions to assess how much it changes the total cost of damages. The model is then run many times to evaluate the uncertainty of the estimates (Howard and Sterner 2017, Howard and Sylvan 2020, Rode et al. 2021). Current SCC estimates depend on the opinions of the four modelers that are dynamic integrated climate-economy (DICE), the framework for uncertainty, negotiation, and distribution (FUND); and policy analysis of the greenhouse effect (PAGE). Disparities may potentially exist over the discount rate and damage function. Replacing general survey questions with specific questions on technological, socioeconomic, and a climate that reduce uncertainties (Nordhaus 2017, Howard and Sylvan 2020). SCC may be estimated from the discounted value of economic welfare affected by an additional unit of CO2-eq emissions. A Dynamic Integrated model of Climate and the Economy estimates that the real SCC grows at 3% per year over the period to 2050. The DICE model views climate change in the framework of economic growth theory. Total carbon emissions E(t) is estimated as total emissions minus captured carbon plus exogeneous land-use emissions. DICE 2061R model defines a social welfare function, W, as the discounted sum of the population-weighted utility per capita consumption (Nordhaus 2017): W

t

t

max V [c (t ), L(t )]R (t ) = U [c(t )]L(t ) R(t ) (4.2) ∑ ∑ tmax t 1= 1

where V is the instantaneous social welfare function, U is the utility function, c(t) is the per capita 1 consumption, and L(t) is the population. The discount factor is R(t ) = , where i is the (1 + i )t discount rate on welfare. The utility function U has an elasticity constant as a function of per capita consumption: U (c ) =

c1− a (4.3) (1 − a )

The parameter a represents a preference for fairness. Net output is reduced by damages and mitigation costs. That is, = Q(t ) D ' (t )[1 − A(t )]Y (t ) (4.4)

where Q(t) is the output net of damages and abatement, Y(t) is the gross output defined from a Cobb-Douglas function of capital, labor, and technology. Here total output is divided between consumption and investment, while labor is proportional to population, and the accumulated capital is related to an optimized savings rate. The damage function D’(t) is defined as D’(t) = D(t)/(1+D(t)), where D(t) = c1TAT(t) + c2[TAT(t)]2 (4.5) Equation (4.5) describes damages of climate change due to global warming as a key component in estimation of SCC using a quadratic function of temperature change TAT with constants c1 and

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c2. Therefore, the DICE-2016R model assumes globally averaged temperature change TAT as a sufficient statistic for damages. The SCC at time t is estimated by = SCC (t )

∂W / ∂E (t ) ∂C (t ) ≡ (4.6) ∂W / ∂C (t ) ∂E (t )

where the numerator represents the impact of emissions in time t E(t) on the marginal welfare W, while the denominator represents the impact of the unit of aggregate consumption C(t) in time t on the welfare. The values of SCC from DICE-2016R estimates are $87 for discount rate of 3% and $140 for discount rate of 2.5% (Nordhaus 2017). The SCC is mainly used in policy design and evaluation through cost-benefit analysis, which compares the total economic benefits of a proposed policy to its total economic costs. For example, the total benefits of a regulation that limits air pollution includes improvements to public health can be compared against the implementation costs to control air pollution. Therefore, the SCC is used in cost-benefit analysis to quantify the policy’s impact on climate change due to GHG emissions. The increase in emissions is multiplied by the SCC, and the result is included as part of the total estimated cost of the policy, while the decreased emission is multiplied by the SCC, and the result is added to the expected benefits of the policy (Rode et al. 2021). Future damages are converted into present-day values using a discount rate to determine how much weight is placed on impacts that occur in the future. Future costs and benefits are generally considered less significant than present costs and benefits, and the discount rate reflects this level of relative significance. A high discount rate means that future effects are considered much less significant than present effects, whereas a low discount rate means that they are closer to equally significant. The effects of different discount rates on estimates of the SCC can be seen in Table 4.2. In the SCC modeling process, the economic impacts can be calculated based on global damages (the total effects of emissions felt all around the world) or they can be limited to domestic damages. This choice significantly affects the outcome of SCC determination, as shown in Table 4.2. Due to the uncertainty involved in this calculation, the SCC is best represented by a central case value, which is usually the average of the evaluations for a given discount rate. Table 4.2.  Evaluation of SCC as a function of discount rate for 2019 US$ per ton of carbon dioxide (Rennert and Kingdon 2019). Discount rate (%)

Global SCC ($/ton CO2)

2.5

75

3.0

50

5.0

14

Policy evolution of the SCC The Interagency Working Group considers discount rates of 2.5 percent, 3 percent, and 5 percent. These changes significantly alter SCC calculations, as seen in Table 4.2. Properly accounting the social cost of GHG emissions due to damages may improve policies to address climate change because of global warming. Public and private sector leaders are increasingly considering the serious impacts of climate change when making decisions about capital investments by using internal measures like the SCC.

4.3.1  Health Effect of Biofuels On average, biodiesel reduces carbon emission by 74% and results in various of benefits, including health benefits. The EPA air dispersion modeling tools with health risk assessments and benefit valuations can assess the public health benefits and result in economic savings of replacing petroleum-based diesel with biodiesel. Biodiesel is widely available today to achieve cleaner air today and helps achieve electrification (Blaber-Wegg et al. 2015, Tomei et al. 2015, Vergara 2021).

94  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence Summary Societal or social sustainability is a measure of human welfare, which in turn is a measure of how well people can meet their needs and hopefully flourish and achieve desired living standards and lifestyles. This includes intergenerational equity, with society only using the natural resources it needs and leaving the rest to future generations. For social sustainability, efforts are needed to increase the standards of living of people who lack shelter, clean water, and adequate food to survive. Additional elements affecting social sustainability are population growth, human health, cultural needs, and a clean environment. These elements have a general direct impact on human well-being and cannot be ignored in favor of economic prosperity in the short term. This chapter covers such societal sustainability topics as societal well-being, social responsibility, advancing social sustainability and the human development index. Also covered are the ideas of social investment and the social cost of carbon emissions. To enhance understanding and potential uses for broad-based strategies for more sustainable social systems, details are given on improved education and the political empowerment of women, especially in developing countries; greater regard for social justice, notably equity between rich and poor both within and between countries; and intergenerational equity. The bottom line of this chapter is that societal or social sustainability is a critical part of overall sustainability, even if it is sometimes overlooked or given less attention than other key dimensions of sustainability. Societal sustainability requires humans to be responsible for improving quality of life and living standards, through enhancements to health, wealth, education, and other factors. Capacity planning within the system can help achieve or shift toward societal sustainability by optimization of a social welfare function and welfare economics. The United Nations Global Compact, the UN Sustainable Development Goals, and the environmental and social sustainability framework of the United Nations Environmental Program all provide useful frameworks and strategies for social sustainability. The UN Global Compact, for instance, considers human rights as the cornerstone of social sustainability, and extends this to labor, gender equality, children, indigenous peoples, education and other areas.

Nomenclature DICE EB EI EPA ER EROI FUND GDP GNI HDI IAM OEL PAGE SCC UNEP

Dynamic integrated climate-economy Environmental burden Energy invested Environmental Protection Agency Energy returned Energy return on investment Framework for uncertainty, negotiation, and distribution Gross domestic product Gross national income Human Development Index Integrated Assessment Model Occupational Exposure Limit Policy analysis of the greenhouse effect Social cost of carbon United Nations Environmental Programme (https://www.unep.org/)

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Problems 4.1 Societal sustainability is neglected by some in assessing how sustainable a situation is and how to make it more sustainable. Identify the benefits of considering societal sustainability. Identify reasons for the reluctance to consider societal sustainability by some. 4.2 Assess the societal sustainability of a specific industry that has not been examined previously in the literature. What are the main economic issues detracting from societal sustainability in that industry and how can they be mitigated? 4.3 Health affects societal sustainability. Explain how societal sustainability in the country where you live was affected by the COVID-19 pandemic. 4.4 Identify three biomass related energy systems and determine and compare the energy return on investment for each. 4.5 What societal benefits are attained when renewable energy resources are used in place of fossil fuels? What are the detriments? 4.6 What societal benefits are attained when resources that can be renewed like wood are used in place of resources that are finite like minerals? What are the detriments? 4.7 What are the potential outcomes if societal sustainability is downplayed or entirely disregarded in actions by companies, governments, and people?

Research Projects 4 .1 What are the metrics for social sustainability? 4.2 What are the impacts of equity, diversity, and inclusion on societal sustainability? 4.3 What are the impact of biofuels on societal sustainability?

References Alvarez-Risco, A., López-Odar, D., Chafloque-Cespedes, R., Del-Aguila-Arcentales, S. and Rosen, M.A. 2019. Environmental social influence, pro-environmental personal norms and environmental self-identity as determinants of ecological buying behavior in Peruvian consumers. Proc. Annual Conference of The Business Association of Latin American Studies - Lighting the Future in Latin America in Times of Uncertainty: Fostering Innovation and Employability, 10–12 April 2019, Lima, Peru. Alvarez-Risco, A., Mlodzianowska, S., García-Ibarra, V., Rosen, M.A. and Del-Aguila-Arcentales, S. 2021a. Factors affecting green entrepreneurship intentions in Business University students in COVID-19 pandemic times: Case of Ecuador. Sustainability 13(11): 6447. Alvarez-Risco, A., Del-Aguila-Arcentales, S., Rosen, M.A., García-Ibarra, V., Maycotte-Felkel, S. and MartínezToro, G.M. 2021b. Expectations and interests of University students in COVID-19 times about sustainable development goals: Evidence from Colombia, Ecuador, Mexico, and Peru. Sustainability 13(6): 3306. Bahrami Ziabari, N., Ghandehariun, S. and Rosen, M.A. 2021. Social acceptance of renewable energy technologies in ecotourism facilities in Iran. Proc. 29th Annual International Conference of Iranian Society of Mechanical Engineers & 8th Conference on Thermal Power Plants, 25–27 May, Tehran, Iran, paper ISME29_351. Barro, R.J. and Lee, J.-W. 2018. Dataset of Educational Attainment, June 2018 Revision. www.barrolee.com. Accessed 20 July 2021. Blaber-Wegg, T., Hodbod, J. and Tomei, J. 2015. Incorporating equity into sustainability assessments of biofuels. Current Opinion in Environmental Sustainability 14: 180–186. Di Fabio, A. and Rosen, M.A. 2018. Opening the black box of psychological processes in the science of sustainable development: a new frontier. European Journal of Sustainable Development Research 2(4): 47. Di Fabio, A. and Rosen, M.A. 2019. Accounting for individual differences in connectedness to nature: personality and gender differences. Sustainability 11(6): 1693. Di Fabio, A. and Rosen, M.A. 2020. An exploratory study of a new psychological instrument for evaluating sustainability: the sustainable development goals psychological inventory. Sustainability 12: 7617. Gagnon, B. Leduc, R. and Savardi, L. 2008. Sustainable development in engineering: a review of principles and definition of a conceptual framework. Environmental Engineering Science 26: 1459–1472.

96  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence Gholami, H., Jamil, N., Zakuan, N., Saman, M.Z.M., Sharif, S., Awang, S.R. and Sulaiman, Z. 2019. Social value stream mapping (Socio-VSM): Methodology to societal sustainability visualization and assessment in the manufacturing system. IEEE Access 7: 131638–131648. Hall, C.A.S., Lambert, J.G., Balogh, S.B., Gupta, A. and Arnold, M. 2014. Energy, EROI and quality of life. Energy Policy 64: 153–167. HDI (Human Development Index). 2021. United Nations Development Programme. (Accessed in April 2022) https:// hdr.undp.org/en/content/human-development-index-hdi. Howard, P.H. and Sterner, T. 2017. Few and not so far between: a meta-analysis of climate damage estimates. Environ. Resour. Econ. 68: 197–225. Howard, P.H. and Sylvan, D. 2020. Wisdom of the experts: Using survey responses to address positive and normative uncertainties in climate-economic models. Climatic Change 162: 213–232. IChemE-Institution of Chemical Engineers. 2002. The Sustainability Metrics, Institution of Chemical Engineers Sustainable Development Progress Metrics recommended for use in the Process Industries. Keil, M., Amershi, B., Holmes, S., Jablonski, H., Lüthi, E., Matoba, K., Plett A. and von Unruh, K. 2007. Training Manual for Diversity Management. International Society for Diversity Management, September. Available at: www.idm-diversity.org. McDermott, M., Mahanty, S. and Schreckenberg, K. 2013. Examining equity: A multidimensional framework for assessing equity in payments for ecosystem services. Environmental Science Policy 33: 416–427. Nordhaus, W.D. 2017. Revisiting the social cost of carbon. PNAS 114: 1518–1523. Rennert, K. and Kingdon, C. 2019. Social cost of carbon 101. Resources for the future August: 1–4. Rode, A., Carleton, T., Delgado, M. et al. 2021. Estimating a social cost of carbon for global energy consumption. Nature 598: 308–314. Thomas, R.R. 2006. Building on the Promise of Diversity: How We Can Move to the Next Level in Our Workplaces, Our Communities, and Our Society. New York, American Management Association. Tirado, A.A., Morales, M.R. and Lobato-Calleros, O. 2015. Additional indicators to promote social sustainability within government programs: equity and efficiency. Sustainability 7: 9251–9267. Tomei, J., Hodbod, J. and Blaber-Wegg, T. 2015. A comparative analysis of the equity outcomes in three sugarcane– ethanol systems. Journal of Environment and Development: A Review of International Policy 24(2): 211–236. 10.1177/1070496515583556. UNDESA (United Nations Department of Economic and Social Affairs). 2019. World Population Prospects: The 2019 Revision. Rev 1. New York. https://population.un.org/wpp/. (Accessed 1 September 2022). Vergara, F. 2021. Biodiesel’s impact on public health. Biofuels Digest, May 31. Available at https://www.biofuelsdigest. com/bdigest/2021/05/31/biodiesels-impact-on-public-health.

Chapter 5

Sustainability Metrics INTRODUCTION and OBJECTIVES The dimensions of sustainability are economic, environmental, and societal. Eco-efficiency metrics are indicative of changes in economic and environmental aspects at the intersection of the economic and environmental dimensions. Sustainability can be viewed as represented by the intersection of all three dimensions. One- and two-dimensional metrics, while useful, cannot alone certify progress towards sustainability. This chapter discusses the how to measure sustainability by using sustainability metrics and indices. The objectives are to identify and describe: • Sustainability dimensions • Sustainability metrics • Sustainability indices

5.1  Sustainability Impact and Indicators Sustainability impacts and impact indicators are related to the depletion of resources and environmental change, as shown in Table 5.1. Defining and using a sustainability framework and the associated potential impact indicators is essential as the set of indicators provides technical, sustainability, and societal perspectives on choices. Such indicators allow comparison of diverse systems, provide a common basis for evaluation, and inform the public and stakeholders on technology development (Harrison and Collins 2012). For each of the dimensions of sustainability, several indicators can be defined and applied. The sustainability indicators in one dimension are chosen so that they are mostly independent of the indicators for other sustainability dimensions. The selection and grouping of the criteria are based on information presented in the literature and on reasoned judgment of the assessor (Harrison and Collins 2012, Gnanapragasam et al. 2010, Rosen 2021a, b). For example, in one sustainability assessment tool, three dimensions of sustainability are considered and taken to be ecological, sociological, and economical (technological), and ten indicators are chosen for each dimension (Gnanapragasam et al. 2010, Rosen 2021a, b). Sustainability assessment The index values for each indicator are related to other indicators depending on their definitions, and governed by energy, economic, and environment considerations. The index value ranges from 0 to 1, divided into 10 steps, with the value of each index chosen using relevant data in the literature and judgment. Thus, index values are somewhat subjective. The criteria for a maximum value of 1 are very stringent, so very few elements within a system likely can receive a value of 1 for some of the indicators. For simplicity, the criteria are taken to be equally weighted, but more complex

98  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence Table 5.1.  Sustainability impacts and potential impact indicators. Impact

Potential Impact Indicator Resource Depletion

Depletion of nonrenewable natural resources

Abiotic resource depletion potential

Depletion of nonrenewable primary energy resources

Non-renewable primary energy consumption potential

Water usage

Water resource depletion potential Environmental (Ecological)

Greenhouse gas effect-CO2e

Global warming potential

Air pollution Air acidification

Acidification potential

Photochemical oxidation

Photochemical ozone creation potential

Depletion of the ozone layer

Ozone layer depletion potential

Water pollution Eutrophication

Eutrophication potential (nitrification)

Soil pollution Soil contamination

Soil contamination potential

Toxic hazards For humans

Human toxicity potential

For aquatic ecosystems

Aquatic toxicity potential

For sedimentary ecosystems

Sedimentary toxicity potential

For terrestrial ecosystem

Terrestrial toxicity potential Economics

Economic profitability

Net present value Payback period Rate of return

Earnings

Cash flow

Inflation

Interest rate

Cost of production

Consumer index

Development

Gross domestic product (GDP) Society

Workplace

Employment

Society

Welfare/poverty

Health and quality of life

Health insurance, education, wellbeing

weighting schemes for the indicators can be incorporated to refine the sustainability assessment (Gnanapragasam et al. 2010, Alhajji and Demirel 2015, Madden et al. 2021). Figure 5.1 presents a procedure for defining indicators within a sustainability assessment. After defining the system boundary, all the relevant environmental, economic, and social impacts associated with a process and supply chain stages must be identified. These can include the following:

• • • • •

Energy consumption Net greenhouse gas (GHG) emissions Freshwater consumption Wastewater treatment Nutrients consumption (e.g., nitrates, phosphates, carbon source)

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Chp.5 1 System boundary definition

4 Sustainability evaluation

5 Assessment and decision making

2 Identification of impacts

3 Selection and prioritizing of indicators Figure 5.1.  Procedure for defining indicators within a sustainability assessment.

Figure 5.1. Procedure for defining indicators within a sustainability assessment.

• Chemicals for oil extraction and biodiesel production (e.g., CH3OH, NaOH) • Residual biomass management • Land use Chp.6 • Potential chemical risk • Net cash flow generated • Employment

Functional A set of sustainability indicators is proposed for quantitative sustainability assessment, for three domain and economical (technological)), based on dimensions of sustainability (ecological, sociological, Systems chain synergetics the impacts deemed relevant for each supply stageon(Gnanapragasam et al. 2010). molecular scale

multifunctional units Thermodynamic 5.1.1  Ecological Indicators domain Spatial domain Ecological indicators in assessing ecosystems and theStructure environment and the impact on them of in molecular Energyhelp efficiency Process catalyst, of a particular element anthropogenicmolecular activity. phenomena, The indicator values specify ecologicalchange, sustainability transport phenomena energy management or process within a system. Many ecological indicators can be employed. Ten ecological indicators Intensification that have been used in one sustainability assessment methodology are as follows (Gnanapragasam et al. 2010): Temporal Knowledge 1. Availability: Sustainable availability of the element within the relevant energy market is domain domain evaluated. The lowest value of 0 is assigned to indicate a lack of availability. The highest value Time, applied Data-driven of 1 is assigned for elements or processes that are available in the local market at competitive decisions, machiene dynamics, process learning allow for longer periods controlof time at low costs and thus receive price. That is, abundant resources a higher score. Also, the sustainability index is higher for any commercially available process. 2. Adaptability: High adaptability implies fewer processes being needed to acquire and process Figure 6.1 Domains intensification Gerven and Stankiewicz al. the element, as wellinasprocess corresponding lower(Van waste generation. A value of2009, 0 is Stankiewicz assigned foretthe 2019, López-Guajardo 2021) A value of 1 if an element or process is highly adaptable. least adaptable item inetaal. system. 3. Environmental capacity: Environmental capacity evaluates the length of time the global ecosystem can supply and support the element or process, without causing significant imbalances in the global ecosystem. A value of 0 is assigned if little of the resource is available in the local market and it impacts the ecosystem significantly. A value of 1 is assigned if the element or process can be sustained for a long time even with an increase in demand. A process capable of recycling is assigned a higher index than one without. 4. Timeline: Timeline evaluates the maturity of the element or process. A value of 0 is assigned for an element is established with little chance for further improvement. A value of 1 is assigned to a well-established process that has greatly evolved over time.

100  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence 5. Material rate: Material rate represents the rate at which the element/process or products for and from the element/process can be procured, considering supply and distribution networks. A value of 0 is assigned for the worst network and a value of 1 to the best. For example, coals are assigned higher material rate sustainability index values than biomasses, due to the well-established network of mining and distribution. 6. Energy rate: Energy rate represents the rate energy can be supplied by the element or process. A value of 0 is assigned for a low energy supply rate and of 1 for a high energy supply rate. This indicator measures the amount of energy available per unit volume of space per time. For example, coals have a high energy rate in that they can deliver more energy per unit mass and time than biomasses. 7. Pollution rate: Pollution rate evaluates the rate of pollutant releases for the element or process. A value of 0 is assigned for a high pollution rate and of 1 for a low pollution rate. For example, sulfur removal technologies are more well evolved and effective at reducing pollution than CO2 separation and storage. 8. Location: Location evaluates the proximity of the element/process from the point of use. A value of 0 is assigned if the source is far from the point of use and 0 if it is near. The system can be placed near to the main solid fuel source. 9. Ecological balance: Ecological balance assesses whether the element or process causes an imbalance in the local ecosystem, and the level of recyclability or reuse. A value of 0 is assigned if recycling is not achievable and of 1 if most of the element or process is recyclable or reusable. Biomass is assigned a higher value than coal for this criterion. 10. Endurance: Endurance evaluates the element work load or demand factor and the maintenance requirement. A value of 0 is assigned for a high maintenance and of 1 for a high load and demand. A lower index value is assigned for an element such as fuel that requires higher equipment maintenance.

5.1.2  Sociological Indicators Sociological indicators serve to facilitate assessments of the impacts on society and the social system and avoidance of undesirable effects. The social system represents the communities that are impacted from the operation and products of the system, either directly or indirectly. Many sociological indicators can be employed. Ten sociological indicators that have been used in one sustainability assessment methodology are as follows (Gnanapragasam et al. 2010): 1. Economics: This indicator assesses the economic benefits of the element or process. A value of 0 is assigned when converting fuels yields an economic loss and of 1 when a maximum net economic benefit is accrued. For instance, current commercial energy production from fossil fuels is more economically advantageous at present renewable energy-based processes. 2. Policy: Policy considers the effects of government policies and implementation trends on the availability of an element or development of a process. A value of 0 is assigned if policies hinder efforts to boost the sustainability of an element or process and of 1 if the policies are supportive. Values are assigned considering advancements in energy technology, economics and ecological and environmental impact of processes. For instance, government financial incentives for biomass hydrogen production enhances their sustainability. 3. Human resources: This criterion assesses the human effort in the life of an element or process, including purchase, production, installation and operation. A value of 0 is assigned if no direct human work is involved with an element or process, and a value of 1 if more human work is involved due to the job creation. 4. Public opinion: This criterion assesses opinions of the public about an element or process. A value is assigned as 0 if there is a negative opinion about the element or process and 1 if most

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people have a positive opinion. For example, the contributions of CO2 emissions to climate change are increasingly understood by the public, so mitigation of such emissions leads to a higher assigned value. 5. Environmental obligation: Obligations regarding an element or process to be benign to the environment are considered through this criterion. A value of 0 is assigned if a process or element is very harmful to the environment and 1 if it is benign environmentally. Note that this indicator discourages process that require extra measures to protect the environment in favor of inherently cleaner processes. 6. Living standards: The effect of an element or process on human living standards is assessed with this criterion, focusing on basic needs (e.g., water, food, shelter, clothing). A value of 0 is assigned if an element or process lowers basic living standards significantly and of 1 if it improves human living standards greatly. 7. Human convenience: The effect is assessed of an element or process on human convenience (above basic necessities). A value of 0 is assigned if an element or process does not provide human convenience and of 1 if it facilitates much greater human convenience and comfort. 8. Future development: Possibilities for future economic and social growth associated with an element or process are assessed by this criterion. A value of 0 is assigned if the element or process provide no future development opportunities and of 1 if the element or process increases potential future societal development. 9. Per capita demand: This criterion assesses the effect of the element or process on sustainably of population and consumer demand. A value of 0 is assigned when per capita demand for an element or process is low and of 1 when it is high. 10. Lobbying: External influences are assessed on the effect of an element or process of lobbying (political or economic) on government sustainability policies. A value of 0 is assigned for no lobbying and of 1 when effective lobbying occurs. Fossil fuel and renewable energy industries engage in political lobbying, although the latter appears to have been more prominent in recent years, leading to a higher criterion value.

5.1.3  Technological Indicators Technological indicators assess from an engineering perspective of an element or process like design, production and performance. The indicators for this dimension assess technical capabilities regarding sustainability, considering aspects such as affordability, environmental constraints, and potential for commercialization and progress. Many technological indicators can be employed. Ten ecological indicators that have been used in one sustainability assessment methodology are as follows (Gnanapragasam et al. 2010): 1. Net energy consumption: The net energy used by the element to transport it to the point of use and to utilize it is considered via this criterion. A value is assigned of 0 if it requires a great amount of net energy and of 1 is assigned if little net energy is required. Processes that generate energy have higher values than those that consume energy. 2. Exergy: The exergy criterion assesses the relative exergy of the element or process for the system relative to the environment. A value of 0 is assigned for an element with low exergy or a process with high exergy destruction, and of 1 for an element with high exergy or a process with low exergy destruction. For instance, combustion receives a lower value for exergy since it has higher exergy destruction relative to many other chemical reactions. 3. Efficiency: The efficiency, expressed as the ratio of desired output to input considering both energy and exergy, of an element or process is assessed through this criterion. A value of 0 is assigned for processes with very low efficiencies and of 1 is assigned for ones with very high efficiencies.

102  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence 4. Design: This criterion evaluates the effect of a process or element design on its sustainability. A value of 0 is assigned for the worst design (i.e., one that hinders good performance and raises wastage), and of 1 is assigned for the best design. A lower value is assigned for designs still in the research phase. 5. Research: Effects of research are assessed with this criterion on prospects and developments of a process or element to be sustainable. A value of 0 is assigned when the probability for research advances is low and of 1 when it is high. Note that technologies undergoing research with great technology prospects and incentives are assigned high values. 6. Demonstration: The demonstration capability of an element or a process is assessed. A value of 0 is assigned when demonstration is required to establish the capability of a technology and of 1 when it is already demonstrated. Hence, commercial processes are assigned higher values compared to those undergoing research. 7. Commercialization: This criterion assesses the potential of a sustainable process or element to become commercially viable. A value of 0 is assigned for processes or elements with little commercialization potential and of 1 for those with high potential. For instance, a process with size constraints that limit its commercial potential is assigned a lower value a process without those constraints. 8. Impact: The impact of a process or element on sustainability is assessed via this criterion. A value of 0 is assigned to processes or elements having a low impact and of 1 to those with a high impact. Hence, a process crucial to efficient and effective system operation is assigned a higher value than one that does not impact the system efficiency notably. 9. Evolution: The capacity for process technology to improve, adapt and grow in the market over time is evaluated through this criterion. A value of 0 is assigned to a process with little development opportunity and of 1 for a process with great opportunity. 10. Environmental limitations: Process limitations due to harmful environmental impacts are assessed with this criterion. A value of 0 is assigned to the processes with great environment limitations and of 1 to processes with few limitations. Note that devices that facilitate pollution control have higher values than those causing environmental degradation.

5.2  Sustainability Indices The sustainability indices of the American Institute of Chemical Engineers (www.aiche.org) help assess a company’s sustainability performance using seven key indices (https://www.aiche.org/ ifs/resources/sustainability-index). These indices help understand how a company’s sustainability efforts are perceived by the community, shareholders, customers, and peers. The indices are: 1. 2. 3. 4. 5. 6. 7.

Sustainability innovation Strategic commitment Environment performance Safety performance Product stewardship Social responsibility Value-chain management

Each sustainability index presents performance indicators, including environment, health and safety (EHS) performance, innovation, and societal measures at the sector level. The indices factor technology and innovation into performance data and enable a company to: (i) benchmark its performance among peers, (ii) assess its performance against well-defined metrics on an on-going basis, (iii) measure progress toward best practices at regular intervals, (iv) access unbiased, expert

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interpretation of publicly available technical data, and (v) better understand public perception of the company’s sustainability efforts. The seven indices are based on the performance of the major manufacturing/industrial sectors. From these indices companies can obtain the following benefits that include confidential reports of their performance metrics for comparison to average industry metrics: • The details behind the computations made to obtain company’s metrics. • Comparisons to the benchmark averages of other indexed companies for better understanding of current sustainability practices in relevant industries. • Recommendations on how to improve sustainability practices and reporting of them based on how the community and public view current company sustainability practices. • Inclusion as desired in all ‘Sustainability Index’ press materials. • A seat at the Sustainability Index roundtable to improve understanding of sustainability measures and help the Index evolve to best meet companies’ needs. The sustainability indices include the following assessment criteria (Swarnakar et al. 2021): 1. Sustainability innovation • Corporate commitment to research and development (R&D) per net sales. • Development of sustainable products and processes with superior environmental, social, and economic performances. • Use of sustainability considerations and decision-support tools in R&D and innovation processes. • Results of the R&D investment that reflect in the number of patents issued and commercialization of new products that enhance environmental and social sustainability. The wide range of sustainability innovation may reflect the diversity of the industrial sector. Companies engaged in developing sustainable products and processes have largely focused on environmental performance over a product’s life cycle. Reducing greenhouse gases and improving energy efficiency are the main drivers. The manufacturing sectors are also engaged in improving customers safety and developing innovations around critical social needs, such as affordable healthcare and clean water. Furthermore, several companies have integrated the use of sustainability approaches including sustainability decision checklists, life cycle assessment, and total cost assessment. 2. Strategic commitment • Public commitment to excellence in environmental and social performance throughout a company’s value chain. • Public commitment to voluntary codes and standards, including Responsible Care, Global Compact, and others. • Timely and comprehensive public reporting of sustainability performance. • A comprehensive set of sustainability goals and programs that are specific and challenging. • Respected agencies’ ratings on company-wide sustainability management and reporting. The industrial sector is often committed to sustainability beyond environmental, health and safety performance, so as to include resource efficiency, product environmental performance, and supplier performance. Commitments need to be supported by accountability through public reporting and clear targets and initiatives. 3. Environment performance • Intensity of energy, material, water consumption, and use of renewable energy and materials. • Intensity of greenhouse gas emissions.

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• Other emissions, wastewater, and hazardous waste releases. • Compliance with environmental liability, fines and penalties, and environmental capital investment.

The manufacturing and industry sectors in general have made considerable progress on reducing emissions despite increasing production. 4. Safety performance • Employee safety and recordable and days-away-from work injury rates. • Process safety and number and trend of process safety incidents, normalized by number of employees, and occurrence of major safety incidents. • Presence of an adequate process security management system, represented by completion of a Responsible Care plant security audit. Regarding employee safety behavior-based safety processes are becoming more widely adopted. 5. Product stewardship • Assurance for product stewardship policies and goals with a responsible care product safety. • Risk communication policies and goals. • Involvement in major legal proceedings related to product safety, risk, and toxicity. Many companies implement responsible care product safety and risk communication processes. Such programs include value-chain engagement, education, and partnership efforts to identify and manage product safety and environmental risks. 6. Social responsibility • Stakeholder partnerships and engagement programs at the project, facility, and corporate levels. • Social investment and contributions through employment and community development projects. • Company image in the community as indicated by reputable awards and recognition programs, including “most admired” and “best employer” ratings. Many companies engage with stakeholder and partnership programs, such as the community advisory panels (CAPs), and with nongovernmental and community organizations to address specific issues. They also established programs with community leaders to stay informed about emerging issues and concerns. 7. Value‑chain management • Presence of environmental management systems (EMS) at the corporate and facility levels. • Supply chain management with policies and procedures is related to suppliers’ sustainability, evaluation, and audits. The responsible care management system (RCMS) and ISO 4001 elements are some of the standards for environmental management in the industrial sector.

5.3  Sustainability Metrics Sustainability metrics often reflect the three dimensions of sustainability and are normalized as ratios of a measure of impact per unit mass, or power, or economic value produced (Schwarz et al. 2002, IChemE 2002, Sikdar 2003, AIChE 2004, Martins et al. 2007, Al Hajji and Demirel 2016, Reddy et al. 2019).

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A set of sustainability metrics that are applicable to a process, plant, or a manufacturing system are: • Material intensity: nonrenewable resources of materials and solvents per unit mass of primary product, or unit power, or unit economic value produced. • Energy intensity: nonrenewable energy per unit mass of primary product, or unit power, or unit economic value produced. • Potential environmental impact: pollutants and emissions per unit mass of primary product, or unit power, or unit economic value produced. • Potential chemical risk: toxic emissions per unit mass of primary product, or unit power, or unit economic value produced. The first two metrics are associated with process operation/manufacturing and focus on the material and energy inputs to the process. The remaining two metrics represent the outputs from the process as chemical risk to human health and the potential environmental impact. The potential environmental impacts can be measured using ‘Carbon Tracking’ and ‘Global Warming Potential’ (see Chapter 2). Integrated sustainability and resilience management includes environmental, social and economic system considerations as well as the ability of a system to prepare for threats, absorb impacts, recover and adapt following a disruptive event. The system does so with these frameworks (Marchese et al. 2018): 1) resilience as a component of sustainability, 2) sustainability as a component of resilience, 3) resilience and sustainability as separate objectives. Resilience is an important attribute, and change, disturbance, uncertainty, and adaptability are fundamental to resilience. The capacity of systems to reorganize and recover from disturbances can be viewed as “safe to fail.” Resilience theory offers a contribution to sustainability. Resilience can be strengthened by biodiversity, modularity, tight feedbacks, social capital, acknowledging slow variables and thresholds, and innovation. Resilience capacity is enhanced from learning through modest failures. Sustainability shares principles, goals, knowledge, and operating methods with resilience theory (Ahern 2011). A sustainability metric for evaluating biomass sustainability needs to consider social, environmental, and economic factors. However, stakeholders (non-governmental organizations, policymakers, research and development entities, bioenergy producers, end-users, and traders) tend to agree on only reduction or minimization of greenhouse gas emissions. Such a single measure does not necessarily address the impact of carbon dioxide or other concerns, e.g., it likely omits impacts of other emissions and labor conditions (Alhajji and Demirel 2016). Life-cycle analysis assesses environmental impacts of all the stages of the manufacture, use and disposal of a product, but does not consider social impacts. Placing monetary values on social and ethical costs and benefits should be considered in some manner. The total factor productivity (TFP) metric measures agricultural sustainability. The TFP reflects the rate of transformation of capital, labor, materials, energy and services into biomass. A cost is attributed to each and to the negative social and economic impacts. For example, just like oil, the price of biomass fluctuates, and sustainability assessment needs to take account such fluctuations. A bioeconomy needs decentralized feedstock access and biomass-sustainability metrics can be aligned with the United Nations Sustainable Development Goals. Measuring sustainability The measurement of sustainability is one of the main instruments to reduce the decline of the environment. Sustainable decision-making can be evaluated by strengths, weaknesses, opportunities, and threats (SWOT) analysis of multi-criteria decision-making (MCDM) techniques. The use of

106  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence MCDM methods to deal with sustainable energy development issues include analytic hierarchy process (AHP), analytic network process, fuzzy set theory, and technique for order preference by similarity to ideal solutions (TOPSIS). The use of LCA is quite popular in combination with multi-criteria decision analysis. LCA is most often applied for the assessment of energy production technologies and for impact assessments (Matzen et al. 2015). Madden et al. (2021) developed a multi-criteria decision matrix that uses combined objective and subjective weighting methods to determine how well alternative biodiesel technology options meet this use-based definition of sustainability. The subjective weighting method used in this methodology is the analytical hierarchy process (AHP) which compares categorical and indicator importance based on stakeholder survey data. For example, a stakeholder analyzes environment (category i) and economics (category j). They then determine the importance using a fundamental scale of absolute numbers from 1 to 9. 1 means that the categories are of equal importance, and 9 suggests category i is extremely important compared to category j. The same methodology is then used to describe indicators that fall under the environmental category, e.g., water scarcity footprint (indicator i) and particulate matter formation (indicator j). Stakeholders complete this comparison with the remaining categories and indicators, ensuring that all permutations of comparison are performed. Lastly, when AHP is complete, researchers calculate the consistency ratio to ensure that a single stakeholder dataset does not contradict itself. Researchers have used the geometric mean method to generate a groupwise matrix due to having multiple stakeholder responses for the AHP method. Stakeholder results that do not meet a consistency ratio of less than 0.1 are excluded from the analysis. The objective weighting method in this methodology is the entropy method (EM). This method requires the matrix data table to be complete with data for the indicators of all alternatives. The indicator data acts as an information source, and the EM measures the contrast between the indicator values of alternatives. The more significant the difference between indicator values, the smaller the entropy value is. With a smaller entropy value, we expect a greater weighting of that indicator attribute in the final result. The first step in this method is the normalization of indicator data using the linear maximum-minimum method since different indicators contain different units. This primary normalization distinguishes between cost and benefit attributes and later utilizes the linearsum-based normalization technique to find the entropy value. This entropy value sj then is used to generate the divergence degree dj = 1 – sj. Divergence degree is used to find the final objective weighting value. Once both objective and subjective methods are available, the integrated weighting method combines them to generate one weight value per category and each indicator in that category. Lastly, TOPSIS (technique for order of preference by similarity to ideal solution method) is used to rank the five alternative biodiesel options. It does so by measuring a process’ differences from a positive ideal solution and a negative ideal solution. The TOPSIS method begins with normalization of the decision matrix and then multiplying that normalization by the combined objective and subjective weight values. Again, a distinction is made here between benefit indicators (Si+) and cost indicators (Si–). A Si− final calculation of Ci = + determines the relative closeness to the positive and negative ideal Si + Si− solutions, with a higher Ci value indicating the process which is closest to achieving the intended goal of sustainability. Researchers and practitioners can then rank alternative sustainability scores from highest to lowest based on Ci.

5.4  Human Development Index The human development index (HDI) is a commonly used composite index of well-being and is calculated using four measures of societal well-being: life expectancy at birth, adult literacy, combined educational enrollment, and gross national income (GNI) per capita for standard living (HDR 2021). This critical function may be a combination of environmental, social, and economic

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dimensions, or as a single holistic indicator. HDI emphasize that people and their capabilities should be the basis for assessing the development of a country, not economic growth alone. A better description of a country’s level of human development requires reflection on inequalities, poverty, human security, and empowerment.

5.5  Sustainability Assessment Tools How is it best to use scientific tools for sustainability assessment so as to help engineers select the best alternative for each project or activity? Sustainable engineering contributes towards global sustainable development by solving some of the most important challenges facing humanity. Sustainable design integrates the building life cycle with green practices. ISO 14040 provides a recognized LCA methodology. A LCA can help emphasize environmental, social, and economic concerns by assessing a full range of impacts associated with all the stages of a process from cradle-to-grave, that is from extraction of raw materials through to materials processing, manufacture, distribution, use, repair and maintenance, and disposal or recycling (Price and Lu 2011, Reddy et al. 2019, Saad et al. 2019). LCA also allows for assessment of energy, water, and materials efficiency. EcoCalculator The EcoCalculator (www.athenasmi.org) provides LCA information for several hundred common building assembles. A more product-oriented tool is Building for Environmental and Economic Sustainability (BEES) software, which combines environmental measures with economic indicators to provide a final rating. The EcoCalculator can be used for new construction projects, retrofits, and major renovations, either to compare specific assemblies or to assess all of the assemblies in terms of a range of performance measures. The performance measures include:

• • • • • • • •

Fossil fuel consumption Weighted resource use Global warming potential Acidification potential Human health respiratory effects potential Eutrophication potential Ozone depletion potential Smog potential

International frameworks and assessment tools The Intergovernmental Panel on Climate Change (IPCC) was established by the World Meteorological Organization (WMO) and the United Nations Environment Programme (UNEP) to assess scientific, technical and socio-economic information concerning climate change, its potential effects and options for adaptation and mitigation. UNEP works to facilitate the transition to low-carbon societies, support climate proofing efforts, improve understanding of climate change science, and raise public awareness about this global challenge. Through international frameworks and assessment tools, UNEP assesses scientific, technical, and socio-economic information concerning climate change, its potential effects and options for adaptation and mitigation. SO/TS 21931:2006, Sustainability in Building Construction is a framework for methods of assessment for environmental performance of construction works. Some of the sustainability assessment tools available include Life-Cycle Assessment (LCA) (Matzen and Demirel 2016), Environmental Impact Assessment (EIA), Life Cycle Costing (LCC), Process Energy Analysis, Social Life Cycle Assessment, and Cost Benefit Analysis. These tools require high-level expertise and address one or two sustainability aspects. Sustainability assessments should: (1) provide reliable information, (2) address a process’s context, (3) point out problem

108  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence areas, (4) point out focused solutions, and (5) be completable within limited time and resources. Tools addressing three elements of sustainability include: (i) fuzzy-based sustainable manufacturing assessment model, (ii) sustainable manufacturing map, (iii) sustainable manufacturing indicators, (iv) indicators for sustainable manufacturing, (v) integrated assessment of sustainable development, (vi) holistic and rapid sustainability assessment tool, (vii) sustainable value stream mapping, and (viii) sustainable domain value stream framework. Most of the tools require extensive reporting. Only a few tools are capable of identifying specific problems and solution for them (Theis 2019, Saad et al. 2019). The International Organization for Standardization (ISO) and the Global Reporting Initiative (GRI) claim alignment with the United Nations Sustainable Development Goals (SDGs). The Grey Absolute Decision Analysis (GADA) model is used to rank the widely adopted certification bodies. More than 22,000 sustainability management standards are used worldwide to fulfil the SDG requirements and cover many aspects of social, economic, and environmental concerns (Ikram et al. 2021). ISO standards ensure consistency, transparency, and transfer of sustainability practices across the world and in international trade partnerships. ISO has developed significant standards that contribute to sustainability across many of the SDGs. For example, the Environmental Management System (EMS) ISO 14001 standard covers SDG 13 on climate action and SDG 15 regarding life on land. Further, ISO 26000, the Social Responsibility standard, provides guidelines and supports SDGs 2 Zero Hunger, 5 Gender Equality, and 10 Reduce Inequalities. The assessment of sustainability in the manufacturing and industrial sectors needs a structured conceptual method that integrates a group of sustainability indicators into value stream mapping (VSM) that captures the information and process flow to identify the sources of wastes. The social dimension includes salary and benefits, and training hours, while the environmental dimension includes environmental goals, supplier relationship, waste disposal, and product life cycle analysis. In addition, employee safety and ergonomics can be considered for the social dimension, while water consumption, energy consumption are part of the environmental dimension. Lean manufacturing has emerged as a model for manufacturing process management. This method constitutes a set of principles, methods, concepts, procedures, and tools utilized to increase production flow by eliminating non-value-added activities and wastes to determine the economic performance of the system (Swarnakar et al. 2021). All sustainability assessment methods have elements that support the sustainable development definition of the World Commission on Environment and Development (WCED), which, as noted earlier, states: “Sustainable development is development that meets the needs of the present without compromising the ability of future generations to meet their own needs.”

5.5.1  Energy Assessments Measurement is the quantitative basis for the informed management of sustainability. Some of the most widely used sustainability measures are prepared by corporate sustainability reporting and triple bottom line accounting. Such measures estimate the quality of sustainability governance for individual countries by the environmental sustainability index and environmental performance index. A sustainable energy pathway helps meet the needs of the present without compromising the ability of future generations to meet their needs. Sustainable energy sources include hydroelectricity, solar energy, wind energy, wave power, geothermal energy, bioenergy, and tidal power. Sustainable energy tools usually also include technologies designed to improve energy efficiency. Energy efficiency and renewable energy are sometimes viewed as the twin pillars of sustainable energy (Alhajji and Demirel 2016, Theising 2016, Theis 2019, Demirel 2021).

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Renewable energy technologies Renewable energy technologies contribute to sustainable energy and energy security by reducing dependence on fossil fuel resources and mitigating GHGs. The IEA defines three generations of renewable energy technologies (Demirel 2018a, 2021): 1. First-generation technologies include hydropower, biomass combustion, and geothermal power and heat. 2. Second-generation technologies include solar heating and cooling, wind power, modern forms of bioenergy, and solar photovoltaics. Solar heating systems are a well-known second-generation technology and generally consist of solar thermal collectors, a fluid system to move the heat from the collector to its point of usage, and a reservoir or tank for heat storage and subsequent use. Such systems may provide considerable heating for domestic hot water, as well as energy for space heating and for industrial applications. 3. Third-generation technologies are still under development and include advanced biomass gasification, biorefinery technologies, concentrating solar thermal power, hot dry rock geothermal energy, and ocean energy. Advances in nanotechnology may also play a major role. According to the IEA, new bioenergy (biofuel) technologies, notably cellulosic ethanol biorefineries (based on corn stover and switchgrass), are promising. Cellulose sources can be sustainably produced worldwide. Green energy Green energy includes natural energetic processes with little pollution. Anaerobic digestion, geothermal power, wind power, small-scale hydropower, solar energy, biomass power, tidal power, and wave power fall under such a category. Additionally, geothermal heat pump systems are efficient for heating and cooling buildings, and often save money over fossil fuel approaches. Renewable energy often needs to be able to be stored to improve efficiency (Demirel 2018a, 2021). Energy analysis consists of (1) acquiring energy use and cost data, and (2) analyzing data to identify energy conservation measures (ECMs) to reduce energy use, cost, and consequently increase efficiency. Based on the cost and operation type, one needs to prioritize energy conservation measures. Energy assessment answers these questions: How much energy is used? Where is energy consumed? How is it used? How can we reduce cost/consumption? How can we determine and reduce losses? The main steps in energy assessments are (Price and Lu 2011): 1. Identify recent trends in energy consumption and cost from earlier energy usage data for developing energy performance indices (EPIs). 2. Estimate the unit costs for all utilities and review the data and manufacturing processes that lead to shutting off equipment, or replacing an equipment, or renegotiation of a supply contract. 3. Compute energy intensity, which is the ratio of total consumption to per unit amount of product or value. This helps create the EPIs for that month. Monthly EPI values show if there is consistency or a need for further investigations if significant variations exist. 4. Evaluate utility supply options by reviewing energy supply contracts to uncover possible saving opportunities. 5. Prepare utility balances that come from annual energy consumption and monthly utility bills. Identify the process units with their percentages of energy consumption costs of operations. 6. Review if there are ECMs previously identified but not implemented by reviewing previous assessments. 7. Develop a detailed schedule to identify ECMs and perform facility inspection. Arrange for energy and equipment surveys by outside parties (Demirel 2018b).

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5.6  Case Study: Sustainability Assessment of Hydrogen Production from Solid Fuels A methodology introduced by Gnanapragasam et al. (2010), for evaluating the sustainability of production of the energy carrier hydrogen from solid fuels (e.g., coal, biomass), is described. The methodology captures both qualitative and quantitative factors, and permits assessments, comparisons and improvements of processes. Many have studied methods and indicators akin or related to the case study considered here. Numerous investigators have examined engineering sustainability generally (Graedel and Allenby 2010) and for specific applications like cities (Alvarez-Risco et al. 2020). Many of these works have focused on energy (Rosen 2021a, Dincer and Rosen 2021, Dincer et al. 2017). Numerous attempts have been made to assess hydrogen production and its sustainability. For instance, Gnanapragasam and Rosen (2017) reviewed hydrogen production using coal, biomass and other solid fuels, while Valente et al. (2021) performed a comparative life cycle sustainability assessment of renewable and conventional hydrogen. Also, Zhang et al. (2018) optimized a system for renewable energy including battery and hydrogen storage, and Moharramian et al. (2021) evaluated a biomass externally fired hydrogen production combined cycle. Research on indicators has also been ongoing regarding social factors (Di Fabio and Rosen 2020) and ecological and environmental factors (Rosen 2017, 2021b, Nielsen et al. 2020, Sciubba 2019, Harrison and Collins 2012). The assessment methodology considered here for the sustainability of systems for hydrogen production from solid fuels considers ecological, sociological, and technological dimensions of sustainability, and ten indicators within each dimension. The technological dimension is somewhat different from the dimensions often used in other sustainability assessments, but it is appropriate for the case study considered here. Sustainability indicators are applied for various factors within each dimension. Each sustainability indicator assesses one sustainability criterion. Once the sustainability criteria for a dimension are assigned values, they can be summed to determine the dimension score. Then an overall score can be attained by adding the dimension scores. Ten indicators are defined and applied for each of the three considered dimensions of sustainability (ecological, sociological and technological). The sustainability indicators for each dimension are mostly independent of the indicators for other sustainability dimensions. The criteria examined for each indicator are based on data and information in the literature combined with reasoned judgment. The index values for each indicator are determined based on the index definitions, and the application of energy, economic, and environment considerations. The index value ranges are determined using relevant data in the literature and careful judgment, and thus have a high degree of objectivity but are admittedly somewhat subjective. Complex weighting schemes can be applied for the indicators to tune and refine the sustainability assessment, but a simple approach is taken here by weighting the criteria equally. The index values are scored between 0 and 1, where the maximum value of 1 is high. Extreme indicator scores of 0 and 1 tend to be rare. The case study considered here assesses and compares two solid fuels for hydrogen production: bituminous coal and farm biomass. The sustainability indices are tabulated for the indicators in each of the three sustainability dimensions in Table 5.2. The index values for each solid fuel are averaged across the 10 indicators in each sustainability dimension as simple means (noting other weightings are possible). It is evident in Table 5.2 that bituminous coal exhibits a lower average value for ecological sustainability than farm biomass, mainly due to the lower environmental capacity and potential ecological imbalances from its use. Bituminous coal scores higher than farm biomass for many other sustainability criteria, although the sustainability score for farm biomass is expected to increase as

Sustainability Metrics  111 Table 5.2.  Sustainability assessment of hydrogen production from two solid fuels, based on ecological, sociological and technological dimensions and the indicators for each. Sustainability measure Dimension

Indicator

Ecological

Sociological

Technological

Fuel Farm biomass

Bituminous coal

1. Availability

0.6

0.8

2. Adaptability

0.6

0.1

3. Environmental capacity

0.6

0.2

4. Timeline

0.7

0.1

5. Material rate

0.5

0.7

6. Energy rate

0.5

0.7

7. Pollution rate

0.5

0.1

8. Location

0.4

0.3

9. Ecological balance

0.2

0

10. Endurance

0.2

0.8

Overall ecological

0.48

0.38

1. Economics

0.8

0.6

2. Policy

0.8

0.5

3. Human resource

0.6

0.7

4. Public opinion

0.8

0.1

5. Environmental obligation

0.7

0.1

6. Living standards

0.4

0.7

7. Human convenience

0.4

0.7

8. Future development

0.8

0.5

9. Per capita demand

0.4

0.8

10. Lobbying

0.6

0.8

Overall sociological

0.63

0.55

1. Energy consumption

0.7

0.6

2. Exergy

0.5

0.8

3. Efficiency

0.5

0.6

4. Design

0.5

0.6

5. Research

0.7

0.6

6. Demonstration

0.6

0.6

7. Commercialization

0.8

0.6

8. Impact

0.7

0.7

9. Evolution

0.8

0.7

10. Environmental limitations

0.8

0.5

Overall technological

0.66

0.63

markets and demand grow. Some specific observations for the different sustainability dimensions follow: • Ecological: Relative to farm biomass, bituminous coal ranks high in availability, material rate and endurance and low in adaptability, pollution rate and ecological balance. • Sociological: Farm biomass exhibits high values for economics, public opinion and lobbying, while bituminous coals exhibit high scores for per capita demand and lobbying and low scores for public opinion and environmental obligation. • Technological: Relative to farm biomass, bituminous coal ranks high on exergy and technology impact.

112  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence The results of applying this sustainability assessment methodology (Gnanapragasam et al. 2010) to the production of the energy carrier hydrogen from coal and biomass fuels captures both qualitative and quantitative factors and permits comparisons. The results can lead to better understanding of and improvements in the sustainability of processes. Summary Metrics are important for sustainability and all its dimensions: economic, environmental, and societal. Various metrics for sustainability or aspects of it exist. Eco-efficiency metrics are indicative of changes in economic and environmental aspects at the intersection of the economic and environmental dimensions. As sustainability can be viewed as the intersection of all three dimensions, one- and two-dimensional metrics, while useful, cannot alone certify progress towards sustainability. Sustainability indicators allow comparisons of diverse systems, provide a common basis for evaluation, and help inform the public and stakeholders on technology development. Sustainability impacts and impact indicators are related to the depletion of resources and environmental change, among other effects. Defining and using a sustainability framework for sustainability metrics and indicators is often important for structuring assessments. This chapter discusses how to measure sustainability by using sustainability metrics and indices. The aim is to identify and describe sustainability dimensions, sustainability metrics and sustainability indices. Sustainability indicators are also covered, including ecological, sociological and technological indicators. The human development index is described as are sustainability assessment tools. Finally, a case study is considered involving a sustainability assessment of hydrogen production from solid fuels. The bottom line of this chapter is that using a sustainability framework and associated sustainability indicators, like impact indicators, is essential to measuring and moving towards sustainable development. A rational and sound set of indicators can provide technical, economic, environmental, and societal perspectives on activities and potential changes to the activities. Such a sustainability framework and metrics like indicators allows assessments and comparisons of diverse systems, provide a common basis for evaluation and interpretation, and can help inform the public and stakeholders on developments and advances. Such metrics are very important for measuring impacts related to the depletion of resources and environmental change, which are central to sustainability. Developing appropriate sustainability metrics for sustainability and its key dimensions (ecological, sociological, and economical) can be challenging, given numerous indicators often can be defined and applied, but convergence on widely accepted metrics is needed for societies and countries to move forward in improving engineering sustainability and overall sustainability in general.

Nomenclature AHP AIChE BEES ECM EIA EPI GADA GHG GNI GRO HDI IEA IPPC ISO

Analytic hierarchy process American Institute of Chemical Engineers Building for environmental and economic sustainability Energy conservation measure Environmental impact assessment Energy performance index Grey absolute decision analysis Greenhouse gas Gross national income Global reporting initiative Human development index International Energy Agency Intergovernmental Panel on Climate Change International Organization for Standardization

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LCA LCC MDDM SDG SWOT TFP TOPSIS UNEP VSM WCDE WMO

Life cycle assessment Life cycle costing Multi-criteria decision-making United Nation sustainable development goal Strengths, weaknesses, opportunities, and threats Total factor productivity Technique for order of preference by similarity to ideal solution United Nations Environment Programme Value stream mapping World Commission on Environment and Development World Meteorological Organization

Problems 5.1 Identify an electrical power plant driven by fossil fuel near to where you live or work. Describe sustainability indicators that can be applied to the power plant and what typical values they have. Show how these values change if the plant is overhauled so it is driven by a biofuel derived from energy crops. 5.2 Identify a plastics production plant driven near to where you live or work and determine what resources it utilizes. Describe sustainability indicators that can be applied to the plastics production plant and what typical values they have. Show how these values change if the plastics production plant is overhauled so it utilizes more sustainable resources. 5.3 Sustainability metrics are used by some in industry to guide their actions but not by others. Identify reasons for the reluctance to use sustainability metrics by some engineers in industry. Identify the benefits of using sustainability metrics in industry. 5.4 Search for a paper on the application of sustainability metrics to a specific industry. List and describe the ways in which sustainability metrics help identify potential improvements in that industry. 5.5 Identify the relevant sustainability metrics for a specific industry that has not been examined previously in the literature. Explain what are the most important and meaningful metrics for that industry? 5.6 Provide typical sustainability metrics for the transportation, industrial, commercial and residential sectors. 5.7 Develop of a sustainable technology or system for a unique or specialized application (e.g., underwater device, a facility in the arctic, an industrial process with special requirements, a device in space), and assess it from a sustainability viewpoint. Relevant factors should be considered, and can include performance, efficiency, energy use, economics, and environmental impact. Utilize appropriate and reliable information sources, possibly including books, journals, conference proceedings, reports, direct communications with relevant experts, and the internet.

Research Projects 5 .1 Discuss the importance of measuring sustainability in a process and in a corporate business. 5.2 Compare sustainability indexes and sustainability metrics.

114  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence

References Ahern, J. 2011. From fail-safe to safe-to-fail: Sustainability and resilience in the new urban world. Landscape and Urban Planning 100: 341–343. AIChE. 2004. Center For Waste Reduction, Technologies Focus Area: Sustainability Metrics (http://www.aiche.org/ cwrt/pdf/BaselineMetrics.pdf). Alhajji, M. and Demirel, Y. 2015. Energy and environmental sustainability assessment of crude oil refinery by thermodynamic analysis. Int. J. Energy Research. 39: 1925–1941. Alhajji, M. and Demirel, Y. 2016. Energy intensity and environmental impact metrics of the back-end separation of ethylene plant by thermodynamic analysis. Int. J. Energy Environ. Eng. 7: 45–59. Alvarez-Risco, A., Rosen, M.A., Del-Aguila-Arcentales, S. and Marinova, D. (eds.). 2020. Building Sustainable Cities: Social, Economic and Environmental Factors. Springer Nature, Cham, Switzerland. Demirel, Y. 2018a. Biofuels. Vol. 1., Part B. pp. 875–908. In: Dincer (ed.). Comprehensive Energy Systems. Elsevier, Amsterdam. Demirel, Y. 2018b. Energy conservation. Vol. 5. pp. 45–90. In: Dincer (ed.). Comprehensive Energy Systems. Elsevier, Amsterdam. Demirel, Y. 2021. Energy: Production, Conversion, Storage, Conservation, and Coupling. 3rd ed. Springer, London. Di Fabio, A. and Rosen, M.A. 2020. An exploratory study of a new psychological instrument for evaluating sustainability: the sustainable development goals psychological inventory. Sustainability 12: 7617. Dincer, I., Rosen, M.A. and Ahmadi, P. 2017. Optimization of Energy Systems. Wiley, London. Dincer, I. and Rosen, M.A. 2021. Exergy: Energy, Environment and Sustainable Development. 3rd ed. Oxford, UK: Elsevier. Gnanapragasam, N.V., Reddy, B.V. and Rosen, M.A. 2010. A methodology for assessing the sustainability of hydrogen production from solid fuels. Sustainability 2(6): 1472–1491. Gnanapragasam, N.V. and Rosen, M.A. 2017. A review of hydrogen production using coal, biomass and other solid fuels. Biofuels 8: 725–745. Graedel, T.E. and Allenby, B.R. 2010. Industrial Ecology and Sustainable Engineering, Upper Saddle River, NJ: Prentice Hall. Harrison T.A. and Collins, D. 2012. Sustainable use of natural resources indicator. Proceedings of the ICE Engineering Sustainability 165: 155–163. HDR (Human Development Report). 2021. United Nations Development Programme. Available at: https://hdr.undp. org/en/content/human-development-index-hdi. IChemE. 2002. The Sustainability Metrics: Sustainable Development Progress Metrics Recommended for Use in the Process Industries. Report. Sustainable Development Working Group, Institution of Chemical Engineers, Rugby, Warwickshire, UK. Available at. https://www.greenbiz.com/sites/default/files/document/O16F26202. pdf. Ikram, M., Zhang, Q., Sroufe, R. and Ferasso, M. 2021. Contribution of certification bodies and sustainability standards to sustainable development goals: An integrated grey systems approach. Sustainable Production and Consumption 28: 326–345. Madden, S., Alles, K. and Demirel, Y. 2021. Measuring sustainability of renewable diesel productions using multi-criteria decision-matrix, Bioproducts & Biorefining, Biofpr. 15: 1621–1637. Marchese, D., Reynolds, E., Bates, M.E., Morgan, H., Clark, S.S. and Linkov, I. 2018. Resilience and sustainability: Similarities and differences in environmental management applications. Science of the Total Environment 613-614: 1275–1283. Martins, A.A., Mata, T.M., Costa, C.A.V. and Sikdar, S.K. 2007. Framework for sustainability metrics. Ind. Eng. Chem. Res. 46: 2962–73. Matzen, M., Alhajji, M. and Demirel, Y. 2015. Chemical storage of wind energy by renewable methanol production: Feasibility analysis using a multi-criteria decision matrix. Energy 93: 343–353. Matzen, M. and Demirel, Y. 2016. Methanol and dimethyl ether from renewable hydrogen and carbon dioxide: Alternative fuels production and life-cycle assessment. Journal of Cleaner Production 139: 1068–1077. Moharramian, A., Habibzadeh, A., Soltani, S., Rosen, M.A. and Mahmoudi, S.M.S. 2021. Advanced evaluation of a biomass externally fired hydrogen production combined cycle. Chemical Engineering & Technology 44: 1585–1595. Nielsen, S.N., Müller, F., Marques, J.C., Bastianoni, S. and Jørgensen, S.E. 2020. Thermodynamics in ecology—An introductory review. Entropy 22: 820. Price, L. and Lu, H. 2011. Industrial energy auditing and assessments: A survey of programs around the world, European Council for an Energy Efficient Economy (ECEEE).

Sustainability Metrics  115 Reddy, K.R., Cameselle, C. and Adams, J.A. 2019. Sustainable Engineering Drivers, Metrics, Tools, and Applications. Wiley. Rosen, M.A. 2017. Bioenergy and energy sustainability. J. Fundamentals of Renewable Energy and Applications 7(7): 25. Rosen, M.A. 2021a. Exergy analysis as a tool for addressing climate change. European Journal of Sustainable Development 5: em0148. Rosen, M.A. 2021b. Energy sustainability with a focus on environmental perspectives. Earth Systems and Environment 5(2): 217–230. Saad, M.H., Nazzal, M.A. and Darras, B.M. 2019. A general framework for sustainability assessment of manufacturing processes. Ecological Indicators 97: 211–224. Schwarz, J., Beloff, B.R. and Beaver, E. 2002. Use Sustainability metrics to guide decision-making. Chemical Eng. Progress July: 58–63. Sciubba, E. 2019. Exergy-based ecological indicators: From thermo-economics to cumulative exergy consumption to thermo-ecological cost and extended exergy accounting. Energy 168: 462–476. Sikdar, S.K. 2003. Sustainable development and sustainability metrics. AICHE Journal 49: 1928–1932. Swarnakar, V., Singh, A.R., Antony, J., Tiwari, A.K. and Cudney, E. 2021. Development of a conceptual method for sustainability assessment in manufacturing. Computers Industrial Engineering 158: 107403. Theis, J. 2019. Quality Guidelines for Energy Systems Studies: Cost Estimation Methodology for NETL Assessments of Power Plant Performance. Quality Guidelines for Energy Systems Studies: Cost Estimation Methodology for NETL Assessments of Power Plant Performance (Technical Report) | OSTI.GOV. Theising, T.R. 2016. Preparing for a successful energy assessment. AIChE, Chem. Eng. Prog. May: 44–49. Valente, A., Iribarren, D. and Dufour, J. 2021. Comparative life cycle sustainability assessment of renewable and conventional hydrogen. Science of The Total Environment 756: 144132. Zhang, W., Maleki, A., Rosen, M.A. and Liu, J. 2018. Optimization with a simulated annealing algorithm of a hybrid system for renewable energy including battery and hydrogen storage. Energy 163: 191–207.

Chapter 6

Process Intensification INTRODUCTION and OBJECTIVES Process intensification (PI) focuses on substantial improvements in manufacturing and processing by remodeling of existing operation schemes into ones that are both more precise and more efficient. PI often involves combining separate unit operations such as reaction and separation into a single piece of equipment, resulting in a more efficient, cleaner, and more economical manufacturing process. At the molecular level, PI technologies significantly enhance mixing, which improves mass and heat transfer, reaction kinetics, yields, and selectivity. These improvements translate into reductions in device numbers and/or sizes, facility footprint, and process complexity, and thereby, often reduce or minimize cost and risk in chemical manufacturing facilities. The main objectives of this chapter follow:

• • • •

Providing an understanding and ability to apply significant techniques for PI Describe and illustrate PI in the industrial sector Describe and illustrate PI in the energy sector Explain and discuss the relation of PI and sustainability

6.1  Process Intensification Process intensification focuses on considerable improvements (at least 10% or more) in manufacturing and processing by any means of modifications of existing operations or new designs so as to make them more precise, efficient, economical, and safe. Therefore, PI can directly foster sustainable design and processes that support environmental, economic, and societal dimensions of sustainability simultaneously. PI can play an important role in making or keeping the industrial, manufacturing and processing sectors competitive. PI elements consist of the improvements and/or optimization of equipment, methods, and plants. At the core of PI is the optimization of process performance by focusing on molecular level kinetics, thermodynamics, and heat and mass transfer. In addition, PI offers enhancements to safety and control for designs during both steady-state and dynamic operation. Some basic aspects in PI follow (Keil 2018): • A generalized modular representation framework to derive novel intensified design configurations • Flexibility and risk analyses for operation and safety performance at the conceptual design stage • The PAROC (PARametric Optimization and Control) framework to ensure dynamic operation under uncertainty • Simultaneous design and control of verifiable, operable, and optimal intensified systems.

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Despite its benefits, PI has some challenges. PI presents increased decision-making challenges with respect to computational efficiency and flexibility across multiple temporal and spatial scales. Also, there exist barriers to PI deployment, and these include risk of failure, scale-up unknowns, unreliability of equipment, and uncertain safety, health, and environmental impacts. Some examples of PI include reactive distillation leading to a 20–80% reduction in capital costs and/or energy usage, especially for equilibrium-limited reactions that are driven towards higher yields by separating the unreacted reactants from the products. A reactive distillation unit combines a chemical reactor and a distillation column into a single unit. Reactive distillation is widely used in the petrochemical industry for production of methyl tertiary butyl ester and acetates (methyl, ethyl, and butyl), as well as for hydrolysis reactions, methylal synthesis, and many other processes. In such processes, heats of reaction are applied directly to the separation process (Harmsen et al. 2018). Other examples of PI include the static mixer, which achieves a significant improvement over mechanical agitation due to its lower energy costs with no moving parts, large reactors, compact/microchannel process units, divided wall column (DWC) distillation, ultrasonic and microwave units, and reverse flow reactors. PI can increase the level of control of product characteristics through, for instance, the use of a spinning disc reactor (SDR) to produce polymers. Polymers produced using a SDR have low polydispersity, which indicates good quality. Microwave heating can lead to fast reactions that can lower resource use and reduce the production of unwanted side products, and hence attain efficient separation of the products. Microwaves penetrate the sample more immediately, and heating takes place throughout the reaction mixture simultaneously and the reaction time is reduced. With microwave heating, reactions in solvents occur above their boiling points and at pressures of about 20 atmospheres. This leads to faster reaction times and overall better yields due to the reduction or minimization of side reactions. For instance, conventional thermal synthesis of pyrazolo[1,5-a]pyrimidines requires almost 2 days of high heat, and attains a yield of 60%. With microwave heating, reaction time can be reduced from days to about 30 min, with an improved yield of 85%. For purification, high-performance liquid chromatography requires expensive solvents, while high-performance flash chromatography systems can speed up processes for small molecules as well as for large biological molecules with reduced solvent rates, leading to greener processes. The last step of purification is to remove the solvent by evaporation using a rotary evaporator, which is efficient and fast for volatile solvents.

6.1.1  Process Intensification Fundamentals The fundamentals of a PI focus include crosscutting unit operations for intensified and modular processes with efficient separations of species, chemical reactor design, catalysts, mixing, and heat transfer. Table 6.1 shows the main differences between process intensification, process optimization, and process synthesis. Process systems engineering (PSE) can contribute to process synthesis, design, analysis, and optimization tools. PSE addresses such questions as: (i) how to efficiently address the combinatorial design space and systematically deliver intensified designs, and (ii) how to ensure the operability and performance of the derived intensified structures at an early design stage (Tian and Pistikopoulos 2019). Some synthesis methods have been specifically developed for PI with classical optimization. Process synthesis is either based on heuristics or relies on mathematical programming to select the optimal equipment types, interconnections between them and operating conditions. The formulated structure is then translated into a cost optimization problem. Synthesis approaches for PI are: (i) knowledge-based methods, (ii) optimization-based methods, and (iii) hybrid methods. Design tools help process synthesis generate designs containing feasible solutions. Table 6.1 compares PI with PSE and process synthesis and displays overlapping aspects (Keil 2018). Details on various important areas of PI are provided and explained in the subsections that follow.

118  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence Table 6.1.  Comparison of process intensification, process optimization and process synthesis. Process Intensification

Process Optimization

Process Synthesis

Objectives

Considerable improvements with new concepts, methods, equipment, and material

Improvements in economics, materials, and energy of known concepts

Integration of multiscale process units, components, flowsheet of a process design

Focus

Design, experiment and interfaces

Modeling, math, and numerical methods

Modeling and software

Interdisciplinarity

Strong: kinetics, chemistry, catalyst, applied physics, material science, economics, electronics, and data processing

Weak: interface with computation and applied mathematics

Modeling, computation, data analytics, and control

Process intensification vision The vision of PI focuses on developing new engineering processes intensified through fundamental research and development with the following objectives: • Transform separations and/or chemical reactions into modular intensified processes requiring less capital, operating costs, and energy consumption. • Develop fundamental technologies and hierarchical multiscale, multifunctional materials to enable intensification and/or modularization of separation and reaction systems. • Create technologies for use of alternative energy forms (e.g., microwaves, plasmas, electrocatalytic processes). • Advance low energy separation processes (e.g., adsorption/membranes) and energy efficient chemical reaction platforms. • Develop multifunctional modules, including hybrid separation/reaction schemes that utilize adsorption and membrane processes, and reactor/heat exchangers and mixers. Key approaches to achieve the PI vision • Facilitate interactions between industrial, academic and government facilities to use resources effectively. • Develop and integrate PI and modularization fundamentals into existing and new large scale chemical processes. • Utilize experimental and modeling facilities at all size scales for applying PI fundamentals to reaction and separation. Expected outcomes for the PI vision • Multiscale modeling tools to design intensified reaction, separation, and hybrid processes with their associated materials. • Multifunctional materials/processes including use of alternative energy forms and design principles. • Integration of intensified rate and transport processes in a modular fashion within large-scale manufacturing industries. • Development of intensified adsorption, membrane, reaction and/or hybrid processes for distributed, modular applications.

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Process intensification aspects An intensified process should outperform existing designs in at least one of the following aspects:

• • • • •

Equipment number and size reduction, Energy consumption and cost reduction, Enhanced environmental footprint and safety, Better analysis, methodology, or modeling, Synergetic approach to intensification.

Outstanding challenges in PI

• • • • • • •

Property prediction and material selection. Multi-scale optimization framework. Accurate cost functions for novel technologies. Safety, controllability, and operability features. Dynamic/cyclic operation synthesis. Strategies for identification of intensified equipment and flowsheet synthesis. Software platforms to generate and validate intensified systems through detailed models.

6.1.2  Process Intensification Principles Process intensification relies on four major principles (Keil 2018, Stankiewicz et al. 2019): • Maximize the effectiveness of intermolecular and intramolecular interactions through, for example, chemical routes and kinetics, topology of catalyst support, and pore connectivity. • Provide each molecule the same processing experience with uniform cross sectional area properties, static mixers for increasing interfacial area for heat and mass transfer, structured packing in reactors, improved local mixing, and microwave heating. • Optimize driving forces and resistances at every scale, maximize the specific surface areas to which those forces and resistances apply, improve mass and heat transfer rates, and utilize uniform driving forces in space and time, • Maximize the synergistic effects of processes with multifunctional devices, and of reactive distillation with catalytic packing. Process intensification principles include the following activities: 1. Provide uniformly distributed conditions for all molecules, such as a plug flow reactor with uniform heating. 2. Optimize and maintain equipartition driving forces to reduce the energy/power dissipation, such as via countercurrent heat exchangers. 3. Use alternative energy forms (e.g., microwaves, plasmas, electrocatalytic processes) and maximize synergetic effects among processes, such as heat integration. 4. Use modular processes to reduce emissions and risks, and utilize unconventional feedstocks. 5. Combine separations and/or reactions into modular intensified chemical processes with hierarchical multiscale technologies, and utilize multifunctional materials. 6. Select low energy separation processes like absorption, adsorption, and membranes, and energy efficient reaction platforms with hybrid separation/reaction schemes. 7. Increase conversion levels and yields in chemical conversions through precise control, and intramolecular and intermolecular interactions.

120  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence 8. Improve process and product safely. Use smaller inventories so as to reduce the consequences of accidents. 9. Reduce the irreversibility associated with pressure drops, fluid friction, stream-to-stream heat transfer due to temperature differences, and other processes when they are highly irreversible. 10. Reduce waste heat and material by designing suitable heat recovery systems and waste treatment facilities. 11. Select cost-effective designs that may result from a consideration of the trade-offs between possible reductions of exergy losses and potential increases in operating costs. 12. Apply experimental and modeling work toward PI fundamentals, in various areas including chemical reaction and separation, reactive processes, controls, components.

6.1.3  Process Intensification Domains PI principles lead to spatial, thermodynamic, functional, temporal, and knowledge domains with micro, mesoChp.5 and macro scales relevant to crosscutting unit operations and modular processes (Figure 6.1). 1 System boundary The principles of PI apply to all the domains. PI4.0 is a design strategy with data-driven models definition enabled by machine learning that is directed toward intensified new or existing equipment, as well as an overall method for plant design. This can lead to accelerated product and materials discoveries with sustainable practices (López-Guajardo et5 al. 2021).and Details of these PI domains Assessment 2 Identification of follow: 4 Sustainability decision making impacts evaluation • The functional domain aims at combining functions within a single device or a small number of devices and closely aligns with the principle of synergy in separate units. For example, a monolithic stirrer may perform catalysis and mixing simultaneously. 3 Selection and • The spatial domain maintains structure prioritizing in equipment, avoiding variability in products. Processes of indicators targeting heat and mass transfer do so with channels, and processes targeting fluid flow do so with static mixing devices. • The temporal domain suggests that time can change through an induced unsteady state into a steady-state process that leads to significantly improved performance, such as in oscillatory and cyclic manners as well as reverse-flow processes. Chp.6 • The thermodynamic domain focuses on energy conversion and transfer with minimal energy loss and greenhouse gas (GHG) emissions. Electricity is the primary energy source in electric Functional domain Thermodynamic domain Energy efficiency molecular phenomena, energy management

Systems synergetics on molecular scale multifunctional units

Spatial domain

Process Intensification

Structure in molecular change, catalyst, transport phenomena

Knowledge domain

Temporal domain

Data-driven decisions, machiene learning

Time, applied dynamics, process control

Figure 6.1.  Domains in process intensification (Van Gerven and Stankiewicz 2009, Stankiewicz et al. 2019, López-Guajardo et al. 2021).

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fields, plasmas, light, and others. The domain also considers molecular phenomena with thermodynamically optimum driving forces and uniformly distributed driving forces in time and in space to reduce energy dissipation due to irreversibility. • The knowledge domain focuses on data needed for intensification: (i) to design intensified new or modify existing equipment and processes, and (ii) to create data-driven decisions for better process operation, maintenance, and control.

6.1.4  Process Intensification Strategies Figure 6.2 shows major process intensification strategies, which consist of the following: • Determining the capacity based on market conditions and customer needs. • Identifying all reaction components, including reaction kinetics and mechanisms, products, and wastes. • Creating a technology database that provide a starting point for relevant equipment. • Identifying the physical properties and handling needs of all species. These strategies can help determine feasibility, performance, and pre-sizing, and thus can lead to the most suitable and intensified technology. Such technology may include, for example: • In situ removal/recovery. • Membrane separation processes. • Removal/separation based on phase equilibria (liquid-liquid, stripping). Accounting for feasibility ensures that operation and design parameters are compatible. Assessing performance determines whether the technology can deliver the required output. For example, the Damköhler number (Da) is a dimensionless value that measures whether equipment can effectively transfer heat relative to the reaction kinetics. That is, Da = tconv/tR where tR is the time for the reaction to take place. When the value of Da is greater than 1, reaction may be the limiting • maximum temperature is also used to simulate possible runaway reactions and step. The possible the equipment •must effectively dissipate the heat. Lastly, pre-sizing determines if some equipment wastes. may have options for scale-up or scale down depending upon the intensified performance. The best • intensified technology offers promise regarding numerous facets of sustainability, including reducing •

Production Reaction objective kinetics/data

Technical database

Thermo-physicalchemical properties

1 Feasibility

Operation conditions Design parameters

2 Performance

Transport phenomena: Heat, mass, momentum transfer

3 Pre-sizing

Number and arrangements of process units

Most suitable technology Figure 6.2.  Process equipment intensification strategies.

122  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence energy use, protecting the environment from the impact of excessive emissions of greenhouse gases (GHGs) and other pollutants, and providing benefits to society and individuals. Based on the above strategies, the following improvements may result from intensified processes: 1. Combining process tasks or equipment into a single unit, such as membrane reactors, sorption-enhanced reaction processes, and reactive distillation. 2. Usage of novel multifunctional materials, including ionic liquids, metal organic frameworks, and zeolites. 3. Process synthesis, including material and/or energy integration. 4. Miniaturization of process equipment like microreactors. 5. Alteration of operation modes, like simulated moving bed reactors. 6. Application of enhanced driving forces, such as rotating packed beds and ultrasonic mixing. 7. Establishment of useful operational strategies, like periodic operation and dynamic modes. A fully intensified process applies PI fundamental approaches in all or some of the domains of structure (spatial), energy (thermodynamic), functional (synergy), temporal (time), and data processing (knowledge).

6.1.5  Process Intensification Techniques Process intensification techniques are based on unit (equipment), method/model, and process/plant improvements, as shown in Figure 6.3 (Ponce-Ortega et al. 2012, Keil 2018). Details on these three areas of improvement follow: 1. Units (equipment) incorporate special designs that optimize critical parameters in heat transfer, mass transfer, momentum transfer, thermodynamics, and reaction kinetics. 2. Method/modeling with multiple processing steps are optimized and integrated with rigorous modeling and operation. Model-based approaches may combine multiple reaction and separation processes into a single unit, enhancing chemical and physical driving forces. Model-based process intensification relies on experiments for data retrieval and for validation with novel measurement techniques. 3. Processes/plants utilize modular approaches, improved designs, and synthesis with energy integration, as well as operation efficiency and safety. For considerable improvements, modeling and simulation for PI may use artificial intelligence and machine learning technologies by collecting data to enable better decision-making in design, control, and operation. PI is an enabler of sustainability by its potential to address several of these steps in industrial processes and manufacturing. Some of the techniques that PI typically introduces include the following (Sitter et al. 2019): • Structured devices, including structured catalyst-based reactors, microreactors, and non-selective membrane reactors. • Hybrid processes, including extractive crystallization, heat-integrated distillation, reactive distillation, and selective/catalytic membrane reactors. • Energy transfer processes, including rotating packed beds, sonochemical reactors, and microwave-enhanced operations. • Dynamic processes, including oscillatory baffled reactors and reverse flow reactor operation.

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Intensifications Unit

Process/Plant

Equipment with chemical reactions: reactors, micro reactors, plug flow reactors, mixed flow reactors

Retrofits for multi production: processing multiple inputs, and partial modularization

Equipment without chemical reactors: mixers, vessels, heat exchangers, towers, packed beds, compressors, pumps

Artificial intelligence: input and output, technology integration, green operation, domestic raw materials, labor, and energy sources

Minimize delay; delay: maximize performance, substition of better units, resizing, and safer operation

Minimize waste material and heat: minimize inventory, maximize yield, provide safer operation

Method/Modeling

Multifunctional reactors: reactive distillation, electrolysis, membrane reactors, hybrid seperators, adsorptive disillation Alternative enery sources: renewable energy, microwave methods, supercritical fluids, dynamic reactor operation

Effective energy usage: increased energy conversion efficiency, rigorous operation

Figure 6.3.  Process intensification techniques (Ponce-Ortega et al. 2012, Keil 2018).

Figure 6.3. Process intensification techniques (Ponce-Ortega et al. 2012, Keil 2018).

6.1.6  Implementation of Process Intensification PI must be validated for increased productivity, safety, capacity, composition (purity), and flexibility, and for decreased complexity, footprint, undesired byproducts, energy usage, waste, capital costs, and operational costs. To achieve PI, collaborations between industrial, academic, and government facilities are helpful and sometimes essential to ensure the use of resources efficiently in design and operations. Multi-scale design, modeling, and optimization with integrated heat and mass transfer under uncertainty may help achieve the desired PI (Stankiewicz and Moulijn 2000, Stankiewicz 2020). Significant effort is required to implement PI principles and validate the use of new technologies. This involves the following steps: 1. 2. 3. 4. 5. 6. 7. 8.

Identify business and process drivers. Develop an overview of the entire process. Identify rate-limiting steps. Generate design concepts. Analyze design alternatives. Select equipment. Compare PI solutions with conventional equipment in a holistic manner. Make informed decisions regarding implementation.

The above steps for PI implementation and validation require data collection, technology evaluations, generation of energy and material balances, construction of process flow diagrams, and

124  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence cost estimations. Once a PI study is completed, the intensified process should operate differently from conventional processes. Both bench and pilot scale experimentation may be required to determine optimal process conditions and help with scale-up of an intensified process. Continuous processing and scalability are key concerns for PI under external and internal constraints, as are risks.

6.1.7  Operability of Intensified Processes PI removes redundancy and synthesizes processes, and hence often reduces the degrees of freedom of the process and the operation. This may lead to more difficult dynamics, control, and optimization. Open questions are: • How to account for operability, controllability, and safety aspects in the conceptual design stage, and • How to address the unique operability challenges due to intensified systems. Process flexibility analysis Process flexibility analysis (PFA) refers to a sensitivity analysis of a process to satisfy product demand, quality, material properties, and environmental conditions under varying operating conditions. PFA is based on the feasible operational region determined by modeling material and energy balances integrated with thermodynamics and reaction kinetics. Process operability analysis (POA) refers to the ability of a process design to deliver its required output under expected disturbances. PFA determines the maximum disturbance set, while POA determines whether a controller can perform its objective under a set of input and output variables. Such approaches have been shown to result in footprint reductions of approximately 77% in reactor volume and 80% in membrane surface area for an equivalent level of performance (Freund et al. 2019). PI may introduce new process dynamics and reduce the number of manipulated variables for control. For example, reactive distillation and a dividing-wall column take advantage of synergistic effects to overcome equilibrium limitations and enhance process economics by increasing productivity and selectivity and by reducing energy use and the need for solvents (Freund et al. 2019). Multi‑level reactor design Enabling feedstock flexibility as a PI effort becomes increasingly critical with the transition from raw materials towards renewable feedstocks. This transition requires flexible chemical reactor operation, which can convert different feedstocks to the desired product(s) with high yield and selectivity. For example, a multi-level reactor design (MLRD) methodology identifies the material and energy flows for more economical and sustainable chemical reactors and leads to predictive determination of the best reaction concept. In the MLRD methodology, a multi-objective problem formulation aims at maximizing the mean performance and minimizing the variance for all considered feedstock scenarios. For example, maleic anhydride can be synthesized from three different feedstocks, namely n-butane, n-butenes and a mixture consisting of 25% n-butane and 75% n-butenes. Two main reactor designs are capable to transform all three feedstocks to maleic anhydride while satisfying the requirements for pressure drop, conversion, and yield (Freund et al. 2019). Scheduling and control Two approaches to scheduling and control may arise from price fluctuations and raw material variability: i) Top-down approaches, which integrate detailed dynamic behavior to the scheduling problem for feasible decisions. ii) Bottom-up approaches, which focus on embedding economic considerations for overall optimal operation. Devices such as pressure swing adsorption units and moving bed reactors, as well as multi-product reactive distillation columns, can benefit from the integration of scheduling and control.

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PI in a transient regime can exploit the system nonlinearities to attain more efficient and safe operations. For example, better scheduling and control of air separation units operating under real-time electricity pricing requires transient operation of a cryogenic distillation column involving a constant change of setpoints determined by the scheduling problem under demand specifications and energy costs (Dias and Ierapetritou 2019). For example, Ashrafi Goudarzi et al. (2022) apply optimal scheduling to residential building energy systems for various operation modes.

6.1.8  Intensification Factor The intensification factor (IF) is composed of evaluation criteria that combine qualitative and quantitative factors. The IF can assist in decision making at the equipment and plant levels, particularly for comparing one setting with another. For a given number of changes, the individual values of IFi can be calculated as (Rivas et al. 2018): di

F  IFi =  bi  (6.1)  Fai  Here, Fb is the current value, and Fa is the value after the modifications leading to intensification. The sign of exponent di is determined whether a decrease or an increase in a factor F is beneficial. The value of di would be +1 if a decrease in F is required and –1 if decease in factor F is not required. The values of independent IFi can be calculated for each possible change (improved safety, or ecological impact, or economic benefits, or energy efficiency) for a given device or process under consideration. The total value of the intensification factor, IFtotal, of a global intensification initiative having p potential intensification strategies can be determined as follows: p

IFtotal = ∏ ( IFi )ci (6.2) i =1

The exponent ci indicates the weight or importance of the independent factors. When information is limited, the value of each ci can be set to one that leads to the base case for IFtotal for improvements. For example, if a reactor is replaced with a smaller and more compact alternative and a heat exchanger is added to use rejected heat, the total intensification factor IFtotal can be calculated as follows: IFtotal = IFreactor IFheat exchanger

(6.3)

Figure 6.4 shows the steps in using the method. The objectives and weights need to be estimated, depending on the situation. Without experimental data for Fai, experts should reach a consensus on assumptions. For example, the scope of factors could be specific variables associated with economics, or the environment, or society, or safety to obtain a value for IF for a selected scope (Rivas et al. 2018). As an example, in Table 6.2 we compare a current process batch reactor for the saponification reaction to a continuous process with an oscillatory baffle reactor as an intensified unit with the IF factors of temperature, pressure, volume, and residence time. Oscillatory flow reactors are tubular reactors, with baffles positioned on perpendicular axes to the flow, which utilize oscillatory flow for effective and uniform mixing. Here a decrease in each factor F is required (d = 1) for the intensified unit. p

By using IFtotal = ∏ ( IFi )ci with an equal impact of each factor of ci = 1, the total value of IF is i =1

determined to be 19.44 for p number of factors of 4. This value represents an overall improvement. For this intensification case, a decrease in temperature is required. A decrease in pressure is required for safety and the value of IF is less than one. However, if higher pressure is required (d = –1), for example for better reaction kinetics, then a new IF is estimated. For volume and residence time a decrease is required to decrease the inventory and d = 1. Using step 2 in Figure 6.4, the most

126  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence

Step 1: Select Objectives Factors (F), parameters on drives, such as safety, efficiency, cost, emissions, waste, safety Define values of Fb, Define values of exponent d based on the change of F (desired/undesired)

Step 2: Establish Scope Environmental, economics, societal Check source reliability Re-assess parameters to define the values of Fa if no experiment available If experiment is available, obtain and use empirical values of Fa

Step 3: Apply IF Method Consider empiricical or estimated values of Fa and Fb

Step 4: Select Relevant Values of IFi

Rank values of IFi considering estimated or empirical values, Try to split into: 1) technoloy, 2) economics, 3) environmantal, 4) society, 5) safety

Step 5: Propose Intensification Solution(s) Analyze the solution based on Steps 1 and 2 Design experiements to validate parameters in Step 2 Decide whether or not to implement the intensification Figure 6.4.  Steps in using impact factors (Rivas et al. 2018).

Figure 6.4 Steps in using impact factors (Rivas et al. 2018). Table 6.2.  Intensification factor for batch and continuous reactors for saponification reaction (Reay et al. 2013). Factor, F Temperature, K

Existing batch reactor, Fb

Continuous oscillatory baffle reactor, Fa

di

Fraction (Fb/Fa)di

388.15

358.15

1

1.08

Pressure, bar

2.01

171.01

1

0.012

Volume, m3

75

0.5

1

150

Residence time, min

120

12

1

10

relevant IFi may be selected, which includes technical or economic feasibility considerations (Reay et al. 2013).

6.2  Intensification Methods and Modeling Modeling and simulation efforts for PI often focus on developing new modeling and simulation tools for intensified and modular processes, including advanced designs for processes, controls, components, and systems. Nonlinear programming, reduced-order modeling, software tools, parallel computing, and artificial intelligence (AI) are enabling the implementation and optimization of large-scale intensified systems. The simultaneous optimization of operation and control offers a potentially promising strategy for PI. Such effort requires the control and operation of PI systems

Process Intensification  127

with machine learning, neural networks, and nonparametric approximators in control problems (Lutze et al. 2012, Stemkowski and Mrugalska 2018, Tian et al. 2018, 2019, Sitter et al. 2019, Stankiewicz 2020). Vision The vision for modeling and simulation efforts for PI is the development of enabling methods, tools and open-source software that employ multi-scale, integrated, and systems-level approaches to PI design, optimization, and control. These are anticipated to be applied for: • Modular process intensification of periodic separation processes (e.g., pressure swing adsorption, vacuum swing adsorption, temperature swing adsorption, simulated moving bed) • Process intensification of micro-reactor and heat exchange processes (e.g., micro-reactors for stranded natural gas conversion) • Modular process intensification via process integration of existing processes Key approaches Key approaches for modeling and simulation efforts for PI include the following:

• • • • •

Multi-scale representation, modeling, integrated materials design, and process optimization, Optimization tools for models under uncertainty, Robust and decentralized control strategies and tools, Incorporation of operability, and Safety criteria in modeling, optimization, and control.

Product development For faster product development and marketing, a framework for an agile and databased approach is often the most suitable. Such a framework consists of a process model as well as an information mode, and replaces current deterministic, linear development approaches. The process model records the current development process about the development network, the business process landscape, and the actual development activities. The information model consists of an analysis of currently used information systems, a description of the development’s information need, and identification of the information carrier objects sector (Demirel 2005, Riesener et al. 2021). Modeling and simulation Modeling and simulation of PI require reliable and predictive mathematical descriptions of membrane-assisted processes, reactive adsorption, microreactors, HiGee technologies, reverse flow reactors, and structured reactors. Software tools significantly accelerate manufacturing sector modeling, simulation, and optimization. For example, in modeling of microreactors, the following aspects are considered: (i) laminar flow with modeling accuracy; (ii) short diffusion paths resulting in high heat- and mass-transfer coefficients; (iii) high surface-to-volume ratio; (iv) high share of solid wall material, which emphasizes the role of conduction in design; and (v) compact devices. This complexity necessitates new detailed models as well as advanced optimization and control approaches (Daoutidis et al. 2019). Rigorous partial differential equation and computational fluid dynamics based modeling for mass, momentum, and energy balances are employed in design, e.g., in adsorption in binary separation and chromatographic technologies. For such technologies, there are four steps to improve modeling: (i) gathering pure component and multicomponent data, (ii) developing improved predictive thermodynamic models, (iii) creating adsorption equilibrium calculation algorithms, and (iv) creating mechanistic models to describe how the species adsorb (De Koejier et al. 2002, Demirel 2004, 2006a,b, 2013a,b, Ponce-Ortega et al. 2012, Castillo-Landero et al. 2019).

128  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence

6.2.1  Thermodynamic Method An important decision is the selection of a method for simulating thermodynamic properties in modeling of an industrial process. Beside state variables, including enthalpy, Gibbs free energy, and phase equilibria, such methods determine or estimate pressure, temperature, composition, volume for multi-component and multi-phase systems. Equations of state, activity coefficient models, Henry’s law, special models, and steam tables are used according to the type of species in the process streams and operating conditions like temperature and pressure. Thermodynamic methods need to identify and predict properly the nonideality of a system for correct predictions of properties, state variables, and phase equilibria. Equation of states are mainly useful in predicting nonideality in the gas phase, while activity coefficient models are more suitable for determining nonideality in the liquid phase. The predictive ability of a selected method depends on the suitability of the mathematical models to describe behavior in the chemical system, and the quality of the component data used in the model (Choudhari 2019, Demirel and Gerbaud 2019, Chen et al. 2020). Property methods estimate phase and chemical equilibria and thermophysical properties required during simulations. Polarity, shape, size, and interactions between the molecules may be responsible for the level of nonideality, which can be studied with thermodynamic phase diagrams, such as plots of temperature or pressure versus composition. For example, Raoult’s law and ideal gas law may be selected for systems that are ideal or nearly ideal. Depending on the level of nonideality of mixtures and operating conditions activity coefficient models (NRTL, UNIQUAC, UNIFAC) or equation of states (RK, SRK, Peng-Rob) are used to estimate the properties. Activity coefficient models are good for predicting nonideality in the liquid phase, while equations of state are used for predicting nonideality in the gas phase (Demirel and Gerbaud 2019). Rate‑based separation Rate-based separation systems account for the rate process on each of the trays in a distillation column by estimating heat and mass transfer rates, as well as hold ups. Therefore, equilibrium stages assuming 100% Murphree vapor efficiency on each tray are not used in the modeling of separation process such as distillation, and absorption. Instead rate-based methods provide a rigorous simulation of the process by using electrolyte thermodynamics, solution chemistry, and reaction kinetics for liquid phase reactions. Rate-based modeling includes rigorous transport property modeling and heat and mass transfer correlations for columns specifics and hydraulics. The model includes the following key features for a rate based model for carbon capture (Aspen Plus 2008): • True species including ions • Electrolyte NRTL method for liquid phase and Redlich-Kwong equation of state for vapor phase • Concentration-based reaction kinetics • Electrolyte transport property models • Rate-based models for absorber and stripper Flash drum simulation and Henry’s law Henry’s law predicts the composition of supercritical or light gas components such as CO2, N2, and O2 when they are declared as the Henry components. Below is the simple separation by a flash drum operated at 37.8°C and 1 atm, using two feed streams containing light gas components of O2 and N2. Table 6.3 shows the simulation results obtained using the activity coefficient model NRTL, with or without declaring the light gases O2 and N2 as the Henry components for a simple flas separation shown in Figure 6.5. The improvement in the separation of light gases is around 98% when the activity coefficient model NRTL is used with Henry’s law model.

Process Intensification  129 Table 6.3.  Improvements in separation of light gases using Henry’s law.

Henry’sMass lawfraction predicts the Henry Mass fraction with Henry Constituent without component declaration component declaration o Water,atH37.8 0.9985 C and 1 atm, 2O

Nitrogen, N2

1056.83 × 10–5

0.9999

0.145

1.15 × 10–5

98.9

–5 the Henry components light Oxygen, O2 gases O2 and 40.83N × 210as 6.91 × 10–6

H2O : 454 kg/hr T : 10 oC P : 1 atm

98.3 3

1

O2: 11 kg/hr N2: 34.5 kg/hr T : 10 oC P : 1 atm

Difference, %

FLASH

2

4

Figure 6.5.  Simple flash separation.

6.2.2  Thermodynamic5Analysis

. The Gouy-Stodola theorem availableofwork Wloss is directly proportional to the Table 6.3 Improvements separation lightrate gases using Henry’s law .states that theinlost rate of entropy production Sprod dueMass to irreversibility in a process: Constituent fraction without Henry . . % Wloss = ToSprod (6.4) , -5 N2 1056.83 × 10-5 Therefore, an improved 1.15 × 10design 98.9 where To is theNitrogen, environmental temperature. with process intensification would lead to the least possible practical irreversibility and hence to a reduced loss of available work rate: 13 . . Sprod (after intensification) < Sprod (before intensification) (6.5) Thermodynamics, fluid mechanics, heat and mass transfer, kinetics, material analysis, operational and design constraints, and geometry are required to establish the relationships between physical configuration and irreversibility and to minimize entropy production. For example, for a steady state flow process for heat and fluid flow, the rate of entropy production results from local irreversibility due to heat and viscous effects (Demirel and Al-Ali 1997, Demirel and Kahraman 2000, Demirel 1995,2001,2013a, Demirel and Gerbaud 2019): Sprod =

k T

2

(∇T )2 +

µ T

Θ (6.6)

Thermodynamic analysis can lead optimum design and operations with retrofits and modifications that reduce irreversibility. This can be achieved by focusing on the molecular level of interactions, thermodynamics, and heat and mass transfer. Thermodynamic analysis can: • Maximize effectiveness of intramolecular and intermolecular interactions to foster higher conversion, yield, and selectivity, • Provide uniformly distributed conditions for all the molecules, such as in a plug flow reactor with uniform heating, • Maintain equipartition driving forces to reduce energy/power dissipation, such as via counter current heat exchangers, and • Maximize synergetic effects among processes, such as heat integration and reactive distillation.

130  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence Therefore, PI can help the manufacturing and processing sectors remain competitive and sustainable, while thermodynamic analysis can identify natural limits in attaining sustainability. Thermodynamic cost Thermoeconomics uses exergy cost theory and exergy cost balances, in which mass, energy, exergy, and cost considerations are unified by a single formulation. Thermoeconomic methods consist of cost accounting methods and minimizing the overall cost, under technical constraints, to identify theofoptimum design. Extended exergyvalues accounting financial, labor, and environmental fuel represents economic for considers e remediation costs, as functions of the technical and thermodynamic parameters of systems (Sciubba 2005, Demirel and Gerbaud 2019, Rosen 2011, 2012, 2022, Dincer and Rosen 2021b). exergy c in $/kW-unit time for a stream is Since exergy provides a consistent measure for thermodynamic work, heat and material flows, as well as irreversibilities within a system, it provides a valid and rational basis for assigning  The cost of fuel represents economic values for exergy loss. Cost estimates depend monetary c = C /costs. Ex on the costs of fuel and equipment, values that change with time and location. For any process or subsystem, the specific  x cost of exergy c in $/kW-unit time for a stream is where . . C and E c = C/Ex (6.7) COP . . cost Cofand theExprocess, i.e.,rate CP and = Cthe + Cof where are the cost FCI rate OPexergy transfer for the stream, respectively. The system cost includes the fixed capital investment CFCI and the operating cost of process COP. This is called the total cost of the process, i.e., CP = CFCI + COP. Then the cost rate balance for a single process is: j

j

in   out      + C (6.8) c j Ex  ∑=  ∑ c j Ex j j P  j     out  j in

Distillation columns

Distillation columns Distillation column systems involve energy intensive processes, where the heat supplied at a higher temperature source in the reboiler is discharged at a lower temperature in the condenser (Figure 6.6). Assuming the2004, columnDemirel to be a reversible (Demirel 2013): heat engine, the work available from the thermal energy becomes (Demirel 2004, Demirel 2013b):

 o  To    T T   T WW = Q= RQ1R− 1 o−  −o QC−Q − 1 1C − (6.9)  heat heat   TR TR   TC  TC  where To is the ambient temperature, and QR and QC are the reboiler and condenser duties, respectively. where To Condenser Qc, T c

FEED Reboiler QR, TR

COLUM N

Figure 6.6.  Distillation column as a heat engine between reboiler and condenser.

Process Intensification  131

The minimum separation work Wmin required for separation is the net change in availability A: –Wmin = Aprod – Afeed (6.10) where A = H – ToS. The change of availability of separation is the difference between the work supplied by the heat and the work required for the separation of components in the feed stream (Demirel 2004, 2006a,b). An efficiency expression for the process based on the second law of thermodynamics is

η=

Wmin (6.11) Wmin + Wloss

Column targeting tools The column targeting tool (CTT) is based on the practical near-minimum thermodynamic condition approximation representing a less irreversible operation (Demirel 2004, Demirel, 2013b). The CTT performs (i) thermal, (ii) exergy, and (iii) hydraulic analyses that can help identify the targets for appropriate column modifications to: (i) reduce utilities cost, (ii) improve energy efficiency, and hence (iii) reduce GHG emissions. Thermal analysis produces column grand composite curves (CGCC) and exergy loss profiles based on the theoretical minimum heating and cooling requirements in the temperature range of separation. CTT can help in identifying the following column modifications: • Feed location: If a feed is introduced too high up in the column, a sharp enthalpy change occurs on the condenser side on the CGCC plot; in such cases, the feed stage should be moved down toward the reboiler. If a feed is introduced too low in the column, a sharp enthalpy change occurs on the reboiler side on the CGCC; then the feed stage should be moved up toward the condenser. A more appropriate feed location may lead to considerable reductions in reboiler and condenser duties as well as stage exergy losses. • Feed conditioning: If a feed is excessively subcooled, the CGCC plots show a sharp enthalpy change on the reboiler side and the feed should be heated. • Reflux ratio: The gap between the pinch point and ordinate on the CGCC plot suggests that the duties in the reboiler and condenser can be further lowered by reducing reflux ratio. This modification requires a change in the total number of stages in the distillation column. • Side condensing or reboiling: If a significant area exists above the pinch on the CGCC plot, a side reboiler can be placed at a convenient temperature level. This allows heat supply to the column using a low-cost hot utility, hence lowering the overall operating costs. If a significant area exists below the pinch, a side condenser can be placed at a convenient temperature level. This allows heat removal from the column more effectively and by a cheaper cold utility. Exergy loss profiles Exergy (Ex) is the maximum amount of work that may be performed by bringing a resource into equilibrium with its surroundings through a reversible process:

Ex = ∆H ‑ To ∆S + ∑ ni ∆µ i (6.12) where H and S denote enthalpy and entropy, respectively, To is the surroundings temperature, which is often assumed to be 298.15 K, ni is the number of moles of species i, and ∆µi is the chemical potential difference of species i. In many thermal processes, the effect of chemical exergy due to chemical potential difference of species i is negligible. The exergy loss profiles identify inefficient use of available energy due to irreversibility and should be reduced by suitable modifications. A comparative thermodynamic analysis before and after retrofits can be useful for estimating the extent of possible reductions in the waste energy and emission of CO2 in energy intensive separation systems such as distillation columns (Demirel 2004, Demirel 2013b, Demirel and Gerbaud 2019).

132  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence Equipartition principle The equipartition principle shows that the best trade-offs between entropy production and transfer area in transport processes are possible when the thermodynamic driving forces are uniform over the transfer area. For instance, in a rate-controlled chemical reaction, the distribution of ΔG/T should be uniform through space and time in the reactor system. Also, mathematical models show that a cascade drying process with a uniform driving force across every stage yields a substantial decrease in energy consumption (Demirel 2004, Demirel 2013a, Demirel and Gerbaud 2019). Some options for achieving a thermodynamic optimum are to improve an existing design so the operation is less irreversible and to distribute the irreversibility uniformly over space and time. This approach relates the distribution of irreversibility to the minimization of entropy production as follows: . . Sprod (equipartioned) < Sprod (arbitrary) (6.13) The entropy production of a process with a uniform driving force is smaller than that of a nonuniform situation with the same size, and a duration of the same average driving force, and the same overall load. Adiabatic columns are highly irreversible and often the irreversibility is not evenly distributed. The stage-exergy loss profiles indicate the distribution of stage irreversibility, and hence the distribution of driving forces in a column operation. Distillation columns operating with close to uniform thermodynamic forces are analyzed for separating n-pentane from n-heptane (Table 6.4). The chemical separation force should be uniform throughout the column to reduce energy dissipation due to irreversibility. Table 6.4.  Reboiler and condenser duties and entropy production rates for an adiabatic, isoforce, and near-optimum columns for separation of a n-pentane and n-heptane mixture (De Koeijer et al 2002). . . Operation QR MW QC MW Reduction in entropy production (%) Adiabatic

2.37

0.704

-

Isoforce

1.89

0.732

13.56

Near optimum

1.90

0.797

13.33

Heat exchanger operation modes An optimum condition requires a uniformly distributed entropy production rate in a heat exchanger or a separator. Consider the examples of countercurrent and cocurrent heat exchangers shown in Figure 6.7. Temperature profiles show that the driving force ∆T, or 1/∆T, is more uniformly distributed in the countercurrent than in the cocurrent flow operating condition. This is the basic thermodynamic reason why countercurrent is better than cocurrent operation thermodynamically. The amount of heat transferred by the heat exchangers depends on the flow rate and inlet and outlet temperatures (T1 and T2) of the cold streams. The heat exchangers are identical except for the flow arrangements. The cocurrent heat exchanger requires a higher flow rate and/or higher temperature of hot fluid, and hence the operating cost will be higher than that of the countercurrent heat exchanger. Alternatively, the cocurrent heat exchanger requires a larger heat transfer area for a specified flow rate and inlet temperature of the hot fluid, which leads to a greater initial financial investment. Therefore, a countercurrent heat exchanger can reduce operating and investment costs compared with a cocurrent heat exchanger (Demirel and Gerbaud 2019, Demirel 2021). In a packed duct, wall-to-air heat transfer increases approximately three times compared with that of empty duct with uniformly heated at the top wall and insulated at the bottom wall. This type of design can enhance the radial heat transfer in solar air heaters, chemical reactors, electronic cooling, and many other engineering applications (Demirel et al, 1999a,b).

6.2.3  Industry I4.0 Industry 4.0 (I4.0) stands for the fourth industrial revolution and is constructed on full life cycle assessment and supply chain management, and addresses process design, quality management, and

Process Intensification  133 T

T

2

2 1

1

z

z 2

2

countercurrent

1

1

cocurrent

Figure 6.7.  6.7. Heat exchangers with countercurrent and co-current modes operation. Temperature profiles along the heat Figure Heat exchangers with countercurrent andof coexchangers are shown at the top and cross-sections of the heat exchangers - are shown at the bottom.

customer services. Some examples of I4.0 are the industrial internet of things (IIoT), cloud-based smart with real-time shared information on business and production processes, and 6.2.3manufacturing Industry I4.0 customer-based continuous improvements in quality of products and services, as well as waste reduction (Vaidya et al. 2018, Ghobakhloo 2020). Industry 4.0 (I4.0) improves how different process systems operate within an interconnected Some examples of I4.0 are the industrial framework. This leads to flexibility and customization, profitability, safety, optimization, realand productivity; achieved through the implementation of smart or intelligent technological infrastructures, such as IoT, cloud computing, advanced fabrication tools, mobile technology, AI, cybersecurity, big data and analytics, and cyber‑physical systems (CPS) (Lopez-Guajardo et al. 2021, Aroma et al. 2019, Buer et al. 2018, Badri et al. 2018, Barrientos 2019, Bhat et al. 2021, Canas et al. 2021, Risse 2019).

6.2.4  Six-Sigma Analysis cyber‑physical systems (CPS) (LopezSix-sigma (6σ) analysis is a statistical procedure for total quality management, strategic planning, al. 2018, Barrientos, A. 2019, Bhat 2021, Canas et al. 2021).

and leadership in a manufacturing process. Six sigma analysis measures the level of quality in production and reduces the variation in a process by estimating the standard deviation, a critical value 6.2.4 Six-Sigma Analysis to quality in the form of defects. It is assumed that the variable’s variance is distributed normally Six-sigma (6σ) analysis statistical (Gaussian distribution). Basedisona the confidence level required six Sigma analysis determines the number of defects per million opportunities (DPMO). After that, if necessary, six sigma analysis reduces improvements the variation in a process by estimating the standard deviation, critical v to as maintains using the tools of total quality management, whicha are referred of defects. define, measure, analyze, improve and control (DMAIC). This may lead to considerable elimination the and confidence level required s ofon defects hence improves product quality in manufacturing and service operations (Cherrafi (DPMO). et opportunities al. 2017, Stemkowski and Mrugalska 2018, Bhat et al. 2021, Chiarini and Kumar 2021). Probability density function and defects The distribution isand normalized that the(Cherrafi total areaetunder the curve is unity. The probability manufacturing service such operations al. 2017, density function f(x) is estimated by: 2021, Chiarini and Kumar 2021).  1  x − µ 2  1 density −   (6.14) f Probability = ( x) exp  2  σ   σ 2π  

function f(x)value is estimated by:to quality’ variable, f (x) is the probability of the quality at a value where x is the of a ‘critical of x, and µ is the average value of x. The defects per million opportunities for +3σ is estimated by: 2

134  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence Table 6.5.  Change of confidence level of six sigma with the defects per million opportunities (Burke and Silvestrini 2017). Confidence level Sigma level

Shifted operation DPMO at µ = 1+1.5σ

Normal operation DPMO at µ = 0

±2σ

308,770

45400

±3σ

66,810

2700

±4σ

6210

63

±5σ

233

0.57

±6σ

3.4

0.001

 1 6  µ +3σ f ( x)dx = 10 1 − ∫ f ( x)dx  = 1350 (6.15)  µ −3σ  2 µ +3σ   ∞

DPMO = 106 ∫

This means that a total defect of 2700 is obtained with 1350 defects can be expected in a normal sample above the upper critical level and below the lower critical value. Usually, in accepted six sigma methodology a case shift of 1.5σ in the distribution of quality is assumed, especially for the case of generic equipment usage (Antony et al. 2019). Table 6.5 shows the effect of the sigma level on the total DPMO with a shift of +1.5σ in mean and without a shift representing a normal operation using new equipment for a single manufacturing step. Capacity lost in manufacturing due to defects Usually manufacturing processes involve many steps (n). For a process with n steps, the overall defect-free throughput yield (TY) is estimated as follows, when each step has its own value of DPMO: = TY

n



∏ 1 − i =1

DPMOi   (6.16) 106 

When the values of DPMO in each step are identical, then the throughput yield can be determined as: n

 DPMO  TY= 1 −  (6.17) 106   The percentage of the production capacity lost due to defects becomes Lost capacity (%) = (1−TY)/100

(6.18)

For example, for a manufacturing process with 40 steps each operating at 4σ level with the corresponding value of DPMO = 6210 in a shifted operation, the lost capacity because of defects becomes: 40

Lost capacity (%) = 1 − TY = [1 − (1 − 0.00621) ] /100 = 22% This shows that 22% of production capacity is lost due to defects. Procedures to improve performance The design, measure, analyze, improve and control (DMAIC) procedure offers improvements in the manufacturing line to reduce defects. Engineers may follow the following procedures to achieve improved performance (Burke and Silvestrini 2018, Titmarsh et al. 2020): • DMAIC: Define, Measure, Analyze, Improve and Control • DMADV: Define, Measure, Analyze, Design and Verify • DMEDI: Define, Measure, Explore, Design, and Implement

Process Intensification  135

Usually, DMAIC is used for improvement of product, process, and services, while DMADV and DMEDI are used for design and development of new product, process, and services. A flow chart for application of DMAIC is illustrated in Figure 6.8. This cyclic procedure consists of: • Define (D): Define the design problem and/or business opportunity: focused problem statements, project schedule, product specifications (size, shape, and capacity) and allocation of resources. • Measure (M): Measure the current performance, and capability: literature search and prepare data base, process and material properties, cost data, and possible problem location and occurrence. • Analyze (A): Analyze the root cause of problem and confirm with data, perform engineering analysis, design, and experiments, as well as develop a theory to be tested and confirmed. • Improve (I): Improve by implementing the best possible solutions: improve product design and manufacturing steps. • Control (C): Control by standardizing solutions and monitoring performance: control your analysis with existing theory, quality control, evaluate results and outline steps for on-going improvements.

Control by standardizing solutions and monitoring performance • Control risk • Keep the goals

Define the design problem and/or business opportunity • Define the scope and the metrics of the project

DMAIC procedure

Improve by implementing the best possible solutions • Generate and evaluate solutions and re-integrate

Measure the current performace and capability • Data availability • Measure progress

Analyze the root cause of problem • Perform cause and effect analysis • Resource management

Figure 6.8.  Define, measure, analyze, improve, and control (DMAIC) procedure to improve production for total quality management.

Example: Improvements in manufacturing A primary product in a reactor violates its flow rate (design specification) during 5 hr/month on average evry month. a) Determine the sigma level at µ = +1.5σ shifted operation b) When DMAIC reduces the violations to 0.5 hr/month on average, estimate the sigma level of the operation after improvements. a) Estimate the approximate value of DPMO: DPMO = (5 hr)/(30 day × 24 hr/day) × 106 = 6944 From Table 6.5 this value of DPMO at a µ = +1.5σ shifted operation would be approximately: σ = 3.8.

136  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence b) After applying the DMAIC procedure the value of defects per million opportunities becomes: DPMO = (0.5 hr)/(30 day × 24 hr/day) × 106 = 694.4. From Table 6.5 this new value of DPMO at a µ = +1.5σ shifted operation would be approximately: σ = 4.7 indicating a considerabler improvements in the reaction in manuafacturing. Change of the values for DPMO becomes: (6944–694)/6944 = 0.90 (or 90%) and shows the improvements in product quality. Design team and six sigma A product may be suggested to the team after a market search and customer survey. Idea generation follows this with possible customer interviews. Later literature is examined especially for the existing patents and competitive products. After, the design team analyzes the process technical feasibility with specifications of product and performance, aiming toward a test for the most promising manufacturing process with total quality management, using a DMAIC approach and supplier, inputs, process, outputs, and customer (SIPOC) analysis (Titmarsh et al. 2020). Definitive screening design Definitive screening design (DSD) is a new beneficial design technique and is part of a six-sigma optimization strategy. Six sigma uses statistical tools to optimize processes and products. The concept of “define, measure, analyze, improve and control” (DMAIC) is part of six sigma’s roadmap, which uses statistical methods to collect data and convert them into useful information and actionable knowledge. The DSD methodology can lower the number of runs in design of experiments compared with classical screening designs. DSD allows the engineer to make a shortcut from screening to optimization for six sigma projects in which many factors need to be tested (Stemkowski and Mrugalska 2018, Peeters et al. 2019). Lean six sigma analysis and Industry 4.0 Lean six sigma is a multi-dimensional manufacturing strategy in Industry 4.0 and contributes toward the circular economy by adopting reduce, reuse, and recycle actions, called the 3R concept. In a manufacturing context, the enabling factors of six sigma are its abilities for systematic and structural approaches. Six sigma can respond to the required organizational learning and manufacturing improvements to reduce defects and increase economics, standing and market share. Six sigma also can support circular economy-based models by practicing the 3Rs and hence support environmentally sustainable manufacturing (Sanders et al. 2016, Buer et al. 2018, Varela et al. 2019, Letchumanan et al. 2022). In a structural model there are several key factors with strong relationships with each other:

• • • •

Strategic integrity, Human resource management, Technologies and tools, Eco-production and network.

Lean six sigma implementations can be investigated with key factors, which in turn, help decision-makers toward its implementation in the manufacturing and industrial sectors (Bhat et al. 2021). Industry 4.0 is aimed at adding fast response to market changes, customer demand, and sustainable value creation, through the closed loop of the product life cycle with resource efficiency. With I4.0 and digitalization, more data are available in the industrial sector. Figure 6.8 shows the improvements that I4.0 offers for various value drivers in manufacturing sectors, where six sigma is an effective strategy for continuous improvement (Sanders et al. 2016).

Process Intensification  137

Four emerging trends in lean six sigma are (Abu et al. 2021, Antony et al. 2019, Titmarsh et al. 2020): • Analysis of large data through six sigma • Implementation of an environmental dimension in six sigma analysis as it was introduced for productivity and cost reduction. • Deploying six sigma for small to medium-sized enterprises (SMEs). • Integration of six sigma into I4.0 The tools to achieve objectives in sustainable manufacturing are interrelated with the level of available technologies. Six sigma and sustainable manufacturing As Figure 6.9 shows many sustainable manufacturing drivers including market readiness and competitiveness may lead to deploying six sigma approaches. For example, six sigma can reduce defects and increase productivity, use of leadership, and energy and resource management in the manufacturing sector (Cherrafi et al. 2017, Titmarsh et al. 2020). Six sigma and I4.0 complement each other as six sigma offers improvements and robustness within the vision of I4.0 predictive analytic and data collection technologies. Also, a DMAIC methodology can be used in the IoT for predictive maintenance (Sanders et al. 2016, Varela et al. 2019, Chiarini and Kumar 2020, Letchumanan et al. 2022). Asset utilization

Market readiness • 20%-50% reduction • 3%-5% increase in productivity

Market forecasting • Up to 85% increase in supply/demand match

• 30%-50% reduction in total process downtime • 10%-40% reduction in maintenance cost

Workforce • 45%-55% increase of productivity

Value drivers in sustainable manufacturing

Cost for quality

Cost of inventory holding • 20%-50% decrease

• 10%-50% reduction Figure 6.9.  Industry 4.0 impact on value drivers in the manufacturing sector (Letchumanan et al. 2022).

6.3  Intensification in Units The manufacturing andRecycle processing sector is increasingly considering sustainabilityPermeate as a key to Feed Feed Separator Reactor improving operational efficiency, reducing waste, and lowering costs. Intensifying equipment with Reactor special designs can optimize critical transport and kinetic parameters, e.g., heat and mass transfer (Stankiewicz and Moulijn 2000), and hence remove the shortcomings in design and operations. For example, each process device has transport phenomena that may limit specific reaction kinetics. Membrane Larger reactors may require longer residence times and may be Retentate targets for intensification through

138  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence selections of smaller equipment. The following equation describes heat transfer: NTUT = UAt = t/tconv where NTU is number of thermal units, t is the residence time, tconv is the convective heat transfer time, U is the overall heat transfer coefficient and A is the transfer surface area. For mass transfer, we have NTUM = KAt where K is the mass transfer coefficient and A is the transfer surface area. These dimensionless equations can be used to compare the units before and after a PI action regarding heat and/or mass transfer performance. Mass and energy balances for the design of a pressure swing adsorption (PSA) unit target minimum total annualized operational cost. PSA for air separation, for example, demonstrates the applicability by determining the attainable feasible region and, hence, affects the cost of the operation. Simulated moving bed (SMB) processes can be modeled either by simplistic equilibrium theory, or by more detailed modeling, considering mass transfer limitations. Systematic PI leads to general chemical processes by decomposing the entire process into a set of process functions and phenomena (Gourdon 2020).

6.3.1  Advanced Separation Systems The energy used for separation processes like distillation is often considerable and PI technologies can reduce this energy consumption. Some intensifications include heat pump assisted distillation (e.g., vapor compression or compression–resorption), heat-integrated distillation column, membrane distillation, HiGee distillation, cyclic distillation, thermally coupled distillation column (Petlyuk column), dividing-wall column, and reactive distillation columns (Nguyen and Demirel 2011, Simon et al. 2019, Tian et al. 2018, Tian and Pistikopoulos 2019, 2020). For enhanced modeling of distillation systems, one needs: (i) properties and phase equilibrium measurements, (ii) interface equilibrium distributions, (iii) phase equilibrium predictions, and (iv) distillation and flowsheet models for equilibrium and rate-based operations. Membrane separation for enhanced modeling requires: (i) membrane properties and performance data, (ii) improved thermodynamic modeling, (iii) modeling of the transmembrane flux, and (iv) membrane module and process models. For enhanced absorption modeling one needs: (i) absorption measurements and simulated data, (ii) absorption thermodynamic modeling, (iii) multicomponent absorption equilibrium predictions, and (iv) absorption unit and process models. Two representative reactive adsorption technologies are simulated moving bed reactors and pressure swing adsorption reactors, or more generally, sorption enhanced reaction processes. Reactive adsorption chromatographic separators can be combined with reactions by using the catalytic adsorbents to yield chromatographic reactors. They are particularly useful with fine, non-volatile chemicals or pharmaceutical products, which may be sensitive to heat. A major challenge for combining adsorption and reaction, however, is the development of multifunctional materials that have the desired selectivity for separation, high activity for the reaction at the same temperature, and stability to withstand the periodic nature of the processes (Tian et al. 2020). Distillation columns Figure 6.10 shows several distillation column systems with new designs aimed at PI. Some advances in separation processes include hybrid nonreactive separation, dividing wall columns, membrane distillation, heat-integrated distillation, combined reaction/separation, reactive distillation, simulated moving bed chromatography, reactive extraction, and processes assisted with external fields. The latter include HiGee, ultrasound, and microwave processes (Simon et al. 2019, Tian et al. 2018, Tian and Pistikopoulos 2019). Details on these follow: • Reactive distillation (RD): The dynamics of reactive distillation feature steady-state multiplicity, high nonlinearity, and strong interactions between variables. • Dividing wall column (DWC): The integration of two columns into one shell results in stronger interactions among manipulated and control variables. The available control strategies for DWC

Process Intensification 139

A

AB

A-B

A B

A-B-C

A-B

B

a

Figure Figure 6.10 6.10. Various

RZ SZ

BC COLUM N

A

SZ

B

C COLUM N

b

COLUM N

c

configurations of advanced (intensified) distillation columns including reactive distillation; (a) conventional column, (b) dividing wall column (DWC) separating a ternary mixture in a single column with side withdraw, (c) reactive distillation (RD) column with separations zones (SZs) and reactive zone (RZ) with a catalyst.

vary from classic three-point control structure and PID controllers in a multi-loop framework to MPC and other advanced controls. • Extractive distillation: The control of this type of highly integrated multi-column system is very sensitive to the choice and pairing of manipulative and control variables. • Heat integrated distillation column (HIDiC): HIDiC configurations can lead to significant energy saving, sometimes on the order of 50%, compared to a vapor recompression column (Demirel 2004, 2006a, b, Nguyen and Demirel 2011). 24 can occur in a continuous mode as in SMB • Simulated moving bed (SMB): Adsorptive separation systems. The chromatographic separation in SMBs makes use of solid adsorbents (or liquid absorbents) that facilitates separation of the components in a feed mixture via differences in their affinities towards adsorption, which results in different flow rates for different components through the column. The process does not reach steady state, but a cyclic steady state can be achieved. Cyclic distillation aims to maximize the driving force between the gas and liquid phases in each separation stage, and simultaneously to minimize the operational and capital costs. PI technologies like reactive distillation and the dividing-wall column integrate functions and steps into a single unit, thus taking advantage of synergistic effects to overcome equilibrium limitations. This can lead to compact equipment and increased overall efficiency (Figure 6.10). RD increases process economic benefits, as it enhances productivity and selectivity, reduces energy use, eliminates the need for solvents, and intensifies mass and heat transfer (Nguyen and Demirel 2011, Simon et al. 2019, Weinfeld et al. 2018). Reactive distillation column Reaction separation for equilibrium limited reactions results in processes representing a significant class of multifunctional reactors. In reactive distillation (RD) with heterogeneous or homogeneous catalysis, the reaction takes place in the liquid phase and reaction products are simultaneously removed via vaporization to increase selectivity for the products. However, the reaction temperature range needs to fall to that of vapor-liquid equilibrium of the reaction mixture. In addition to the capital cost reduction, RD also offers benefits in energy costs for exothermic reactions as the reaction heat can be used to vaporize the liquid phase. Also, hot spot formation in the catalyst is prevented as the maximum temperature of the operation is limited by the boiling point of the mixture. Furthermore, if the catalysts are positioned above the feed location, catalyst poisoning from potential impurities in the feed can be avoided (Nguyen and Demirel 2011, Weinfield et al. 2018). In an RD column, at least two phases exist in counter-current motion which necessitates the simultaneous consideration of interfacial contact phenomena and hydrodynamics together with mass transfer, reaction, and thermodynamics. Available models for reactive separation can be

140  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence short-cut models, equilibrium-stage models, and rate-based models. The models describe mass transfer, reaction, and hydrodynamics using two-film theory, penetration theory, or surface renewal theory. Under given input and output specifications, synthesis design can be performed to provide the equipment and operating parameters required by detailed simulations and to test the feasibility of the proposed distillation system. Stochastic optimization methods are also used for the synthesis and optimization of RD systems. The simulated annealing (SA) method and differential evolution algorithm are two examples of stochastic methods used in RD synthesis. The three different types of models are (i) a detailed rate-based model, (ii) a simpler rate-based model, and (iii) an extended equilibrium stage model with equilibrium stages and chemical reaction rates (Masuku and Biegler 2019). For example, reactive distillation is successfully commercialized, both in petroleum processing and in the manufacture of chemicals, including the production of tertiary-amyl-methyl ether or methyl-tertiarybutyl ether, and methyl acetate. RD is also used to produce high purity isobutene for aromatic alkylation, to produce isopropyl alcohol for ethylene glycol, for selective desulfurization, and for various selective hydrogenations. Extraction distillation is also used to produce anhydrous ethanol. RD technology is used in the manufacture of oxygenates and fuel additives, the synthesis of a range of fatty acid esters, and in the recovery of lactic acid from fermentation broth (Charpentier 2007). In biodiesel production, the use of RD can reduce capital and operation costs (Nguyen and Demirel 2011). Petlyuk column The Petlyuk column as an intensified device can provide notable benefits including high outcome and thermal efficiency, but at the expense of relative operational complexity (Figure 6.11). The Petlyuk column requires less energy than a conventional distillation column to separate its mixture into at least three pure components (Egger et al. 2018). The energy reduction potential is up to 50%. A non-equilibrium model and modular approach for possible applications can make the complexities manageable. The proposed model demonstrates how a Petlyuk column can work at steady state, and how the chosen mathematical equations produce accurate temperature, concentration and pressures profile data (Egger et al. 2018). Carranza-Abaid and Gonzales-Garcia (2020) differentiate between physical feasibility and dynamical feasibility of a system, where a physically feasible system aligns with thermodynamic and conservation principles, while a dynamic system negative eigenvalues on CAPEX andhas OPEX calculations when modeled. Modeling the data shows that a small perturbation on the (Kaur systemand canSangal cause 2017 important -separation device anomalies ). in column operation. A thermodynamic analysis shows that the number of stages and the

Liquid split

A-B+C

A

B

RZ RZ

FEED

FEED

RZ

Vapor split

C

PETLYUK

PETLYUK

COLUM N

(a)

(b)

(c)

1 Figure 6.11. Various configurations of advanced (intensified) distillation columns including: (a) reactive dividing wall column (RDWC) with liquid split at the top part and vapor split at the bottom part of the column, (b) conventional thermally integrated Petlyuk column, (c) reactive distillation Petlyuk column as with reaction zone (RZ).

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flows to achieve some types of separation are inversely proportional (Carranza-Abaid and GonzalesGarcia 2020). Reactive Petlyuk columns Reactive Petlyuk columns and reactive divide wall columns (RDWCs) are intensified processes involving combined reactions and multicomponent separations, that involve replacing traditional process equipment like reactors followed by distillation columns (Figure 6.11). This results in considerable savings in capital expenditures (CAPEX), up to 20%, and operating expenditures (OPEX), between 15% and 75%, in chemical and petrochemical industries (Weinfeld et al. 2018). For example, ethyl tertiary butyl ether (ETBE) can be synthesized in a RDWC as an alternative to using a reactor followed by a distillation sequence or a reactor plus reactive distillation. ETBE is used both as biofuel and bio additive in gasoline to reduce GHG emissions. A comparison of these alternatives based on CAPEX and OPEX calculations shows that RDWC could be promising as an intensified reaction-separation device (Kaur and Sangal 2017, Egger et al. 2018). Intensified carbon capture In conventional solvent-based carbon-capture technology, exhaust gas with CO2 contacts solvents enter an absorber column. In an intensified technology, centrifugal force from rotating horizontal packed beds is used to enhance the gas-to-liquid contact. Solvents are pumped into the center of the rotating cylinder and the centrifugal force is created from the rotation. The higher centrifugal forces created by the rotation improve mass transfer and enable smaller units with lower capital costs to be used. The system has the potential to reduce the levelized cost of CO2 below of $30/ton CO2. This easy-to-scale-up this process-intensification may capture 1 ton CO2 per day from power-plant flue gas effluents (Dennis et al. 2011, Jenkins 2019, 2021). Adsorptive separations Conventional technologies applied to adsorptive separations may suffer from low conversions, low selectivities, low energy efficiencies, high costs, together with some kinetic and thermodynamic limitations. Suitable PI techniques based on process, equipment and novel material development methodologies can play a significant role for the proper organization of the adsorption separation processes (Keil 2018, Tian et al. 2018, Kopac 2021). Adsorptive separations applications include CO2 capture (González and Manyà 2020, Meloni et al. 2021), hydrogen storage (Dastbaz et al. 2019), gas separation (Kane et al. 2015, Sen et al. 2019), and desulfurization of fuels (Suryawanshi et al. 2019). Modified adsorption systems, process integration, thermal integration, microreactors, monolithic reactors, microwave reactors, acoustic cavitation/ultrasonication or heat pump coupled with adsorption, novel sorbent developments are among the significant processess involved adsorptive separations. Product purity, productivity, gas uptake, component recovery, separation effficiency, adsorbent property, bed/reactor volume, production yield, selectivity, energy efficiency, process capital cost, operating cost environmental sustainability are among the intensification parameters involved depending on the type of the separation system (Kopac 2021). PI represents a promising technology for such systems offering enhanced separation, better yiel, increased component uptake, productivity, better sorbent characteristics (higher surface area, micropore volume, thermal stability), reduced reactor size, reaction time, lower energy consumption, lower process capital and operating costs, lower pressure drop, enhanced heat and mass transfer coefficients, and energy efficiency. The reduced process volumes handled in intensified systems reduces material costs and enhance safety. Much better intensification levels can be obtained by the combination of materials, methods and equipment, or two or more technologies in a given process (Wang et al. 2017, Adamu et al. 2020, Kopac 2021).

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6.3.2  Advanced Reactors A large proportion of chemical reactions are equilibrium limited, which leads to low-grade products and low reactant conversions. Increasing product purity requires further downstream operations, which involve energy-intensive separation equipment (e.g., distillation devices), higher reactor volumes, or elevated operating temperatures, all resulting in increased operating and capital costs. Reaction engineering is fundamental to many chemical production processes and can drive radical revolutions in chemical and energy industries by employing advanced reactor designs and combinations with separation techniques to improve volumetric or energy efficiency. PI can be achieved at the catalyst level with bifunctional catalysts, reaction inter-phase level like monolith reactors, and inter-reactor level like circulating fluid-bed reactors. Structured catalytic reactors Structured catalytic reactors enhance interphase transport by eliminating the border between catalyst and reactor by using reactors with a structured catalyst (monolith reactors), and reactors based on structured arrangements of packings with “normal” catalyst particles (parallel-passage reactors and lateral flow reactors). Monolith catalytic reactors are typically manufactured from ceramic or metal. They have multiple thin, vertical, parallel finned or unfinned channels that provide uniform flow distribution, lower pressure drop, enhanced mass transfer rates, and easier reactor scale-up. For example, hydrogenation of α-methyl styrene, which is mass-transfer limited, was carried out over an egg-shell nickel catalyst in both monolith and trickle bed reactors and a 40 times higher catalyst efficiency was observed in the monolith reactor (Tian et al. 2018). Monolith reactors are also used as catalytic converters in the abatement of automobile exhaust gas emissions. Microreactors Microreactors are multi-purpose systems with a length scale of less than one millimeter, which significantly helps to improve heat and mass transfer rates, to enable distributed point-of-use synthesis of chemicals, and to reduce investment/operating costs. Inherent process safety is also improved in these systems because of the short residence time and low quantity of material present during operation (Tian et al. 2018, Zong and Yue 2022). Small-scale equipment may have less and simpler moving parts, increasing the life of equipment. Microreactors can be useful in various applications. Some examples: • Hazardous chemicals production, for instance, continuous nitroglycerin production with around 15 kg/hr. • Gas to liquid processes in high temperature multifunctional microsystems for syngas production with high energy efficiency and stack stability. • Portable hydrogen production for portable electronic devices and on-board reforming in transportation. • Fine chemicals and pharmaceuticals production (although such processes with continuous microreactor operation have a higher CAPEX, which can hinder the implementation of this technology). Oscillatory flow reactors Oscillatory flow reactors (OFRs) are tubular reactors that utilize oscillatory flow induced by external oscillators for effective and uniform mixing. At high oscillation rates, the flow profile becomes steady and can be characterized as plug flow. OFRs are useful in the synthesis of mechanically sensitive chemicals, including enzymes, proteins, and crystals. Several issues exist for this technology, relating to scale-up problems and handling of gaseous species (Reay et al. 2013).

Process Intensification  143

Reverse flow reactors Reverse flow reactors (RFRs) utilize flow direction reversal in a periodic manner. The feed stream Asset utilization is first fed from one side and then switched to the other side after some time, enhancing catalyst • 30%-50% reduction stability as well as reducing utility requirements (Tian et al. 2018).

6.3.3  Reactor and Separators

Market in maintenance cost integration to PI, often in the tight There are control implications in the transition from process Workforce readiness integration through material recycling (Figure 6.12). For a reaction–separation–recycle system, • 45%-55% • 20%-50% consider the case of a mixed flow reactor followed by a separator. increase The reactant is converted to a of reduction product with first-order reaction kinetics; the slow reaction must beproductivity compensated for by increasing • 3%-5% increase in productivity the reactor volume and/or increasing the recycling rate. Reactor volume as a function of recycle rate Value drivers in conventional, integrated, and intensified units with capital and operating costs (CAPEX, OPEX) in sustainable are estimated for an isothermal operation.manufacturing The connection between these design variables can be easily accounted for by component Market mass balance equations (Baldea 2015). Cost of forecasting Three separate designs are low values of recycle with the highestinventory CAPEX, or process integration with an increase of yield and reduce equipment size and CAPEX, orholding intensified design with very • Up to 85% increase high material recycling rates, in in which reaction and separation occur in a single device. OPEX and • 20%-50% supply/demand CAPEX can be reduced after elimination of recycling and one unit. decrease match Costcontrol for quality A combined reactor separator with precise can convert more of the raw materials into final products by increasing yields, conversions, and selectivity. Higher heat-transfer coefficients • 10%-50% reduction can be achieved in intensified heat exchangers with optimum driving forces and increased energy efficiency. In addition, the more efficient use of raw materials can reduce energy consumption. Alternative energy sources, including ultrasound or microwaves, can increase the efficiencies of impact onthereby value providing drivers inathe manufacturing mixing and heating processes, PI alternative. This sector simple (model shows that a tight integration may be a necessary but not sufficient criterion for intensification (Baldea 2015).

Feed

Recycle Reactor

Feed

Separator

Retentate (a)

Permeate

Reactor

Membrane (b)

-separator recycle stream, reactor(Diban with etinal.situ Figure 6.12.  (a) Integrated reactor-separator and recycleand stream, (b) reactor with in(b) situ separator 2013, Baldea and Daoutidis 2014).

Membrane‑assisted reactive separation Membranes can also be utilized to increase process performance via integration with reaction operation to form membrane reactors (Figure 6.12). The functions are selective and nonselective addition of reactants to the reaction media, removal of reaction products, and retention of catalyst material. Membranes can withstand high operating temperatures while providing targeted selectivity and flux as well as avoiding coke formation and fouling. These are paramount factors for the adaptation of this technology in large-scale applications. Membrane-assisted fluidized bed reactors provide more effective heat transfer and less pressure drop and can be utilized in practice, although they can suffer from mechanical difficulties. Membrane reactors are used in the water-gas shift reaction with a CO2 selective membrane and for water removal in esterification, as well as for bioprocesses, where enzymatic reactions enable purification and concentration of the product streams. Such membrane reactors are used in the production of foods, plant metabolites, amino acids, antibiotics, anti-inflammatories, anticancer drugs, vitamins, proteins, optically pure enantiomers,

144  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence isomers, fine chemicals, and biofuels, as well as in wastewater treatment (Diban et al. 2013, Baldea and Daoutidis 2014).

6.3.4  HiGee Technology HiGee (high gravity) technology uses high centrifugal forces in gas-liquid absorption processes like rotating packed beds (RPBs). High gravity field introduces more intense process conditions such as thin liquid films, tiny droplets, and intensely renewed interfaces. This increases volumetric mass transfer coefficients (by 1–2 orders of magnitude) while reducing equipment size and cost. RPB reactors and spinning disc reactors (SDRs) focus on fundamental mass transfer and hydrodynamic behaviors. Modeling studies of SDR for chemical vapor deposition (CVD) typically focus on multi-dimensional flow, transport phenomena, and chemical kinetics (Tian and Pistikopoulos 2019, Rao 2022). HiGee technology can present both advantages and disadvantages (Zhang et al. 2010, Tian et al. 2018, Tian and Pistikopoulos 2019). Advantages include the following: • Size reduction by a factor of 10–50 with cost reduction, inherent safety, and “instant” startup. • Ability to perform as a mixer, reactor, and gas demister. Disadvantages include the following: • Higher maintenance and power consumption and lower reliability, as well as complicated fabrication. • Unsuitability for processes with large heat effects and for reactive separation, due to the small residence time.

6.3.5  Crystallization Equipment Crystallization is typically used for fine chemicals and food products, as well as for lithium, cobalt, and nickel recovery from recycled resources like battery materials. The latter process is in line with the concept of a circular economy. In fact, crystallization is essential in recycling, bio-based material productions, and wastewater treatment for enabling a circular economy (Bailey 2021c). Crystallizers can be operated with renewable energy and use little water. Crystallization using mechanical vapor recompression is more sustainable than multiple-effect crystallizers, as it can use renewable energy, recover evaporated water, and improve efficiency. Crystallization-based alternatives produce a high-purity sulfate of potash from natural ores, and brines. One of the fastest-growing bioplastics on the market is polylactic acid (PLA), which is recyclable, compostable, and based on a sugar feedstock. As PLA production scales up, effective separation and purification steps are increasingly important, and crystallization is fundamental to the delivery of high-quality bioplastic materials. Crystallization, distillation and polymerization technologies can provide a hybrid distillation-crystallization process to deliver a high-purity lactide, which is an essential PLA building block. While crystallization alone is an effective tool for biomaterial production, inserting a distillation stage prior to crystallization maximizes throughput, while crystallization achieves extremely high purity levels with low energy consumption while also minimizing degradation risk. Through subsequent repetitions, it is possible to obtain a lactide that is more than 99.9% pure. The nature of bio-based materials makes their separation especially challenging, and the impurity profiles found in fermentation mixtures are often complex. The goal of such separation is to reduce cost while achieving product quality, including purity, crystal size distribution, polymorph form, morphology, bulk densities, solubility, and dissolution rates. Biomaterial mixtures tend to have components with very similar boiling points and heat sensitivities, so their separation via distillation is typically expensive. This reinforces the importance of crystallization, as it offers high-purity products without thermal degradation, decreases energy and solvent requirements, and

Process Intensification  145

improves the overall sustainability for the separation process. Capacity expansions can be more effectively realized with continuous crystallizers (Bailey 2021c, Keiler et al. 2020).

6.4  Intensification in Plants Manufacturing sectors remain competitive by investing in new and innovative processes. This creates pressure on cost and assets to deliver and maximize profitability. Table 6.6 shows design principles for PI in the manufacturing sector. The rapid advancement in PI deployment focuses on breakthrough technologies for energy productivity and energy efficiency in the industrial sector (Adamu et al. 2020). The main objectives are identification of promising PI technologies that improve unit operations to achieve increased efficiency and productivity while reducing costs, pollutant emissions, and wastes (Barecka et al. 2017). Key approaches include working with the supply chain toward standardizing and intensifying components and modules, including advanced manufacturing, to deliver sizable improvements. Manufacturing sector organizations commonly apply the “seven tenths power law of cost versus size” in economies of scale. In addition, large plants grouped together can share utilities and effluent treatment facilities as well as interchanging feedstocks, products, and energy. For example, Hasanzadeh et al. (2022) apply multi-objective optimization of efficiencies and emissions to the gasification of polyurethane foam wastes (Alhajji and Demirel 2015, 2016, Niu and Rangaiah 2016, Bailey 2018, Bielenberg et al. 2019). Table 6.6.  Design principles and their definitions in an intensified manufacturing sector (Keil 2018). Principle Interoperability

Modularity

Decentralization

Real-time capability Technical assistance Virtualization

Information transparency

Definition Use of information requires synchronized data flow from the interconnected equipment and processes through IoT and/or cloud technology. This enables coordinated actions between devices, sensors, machines, and operators and hence leads to process designs and operation that are less irreversible. Flexible continuous and standardized modules with fixed outputs can satisfy production demands and needs. This principle leads to easy scale-up with multiple modules of each unit operation satisfying the production demand. Distributed processes make localized data-driven decisions for subsystems. For PI4.0 this means increased efficiency and flexibility of the process due to coordinated and parallelized communication between operations, methods, and energy and material distribution. The capability to capture and interpret the relevant volume of data instantly leads to real-time capability. This is important for flow processes that use information to intensify closed-loop systems and thereby drive automated and self-driven factories. Connecting operators, engineers, and stakeholders of a process in decision making usually leads to reduced downtime. This results in increased capacity, safety, and remotely operating equipment, reducing the risks of processes in shifting toward PI. Virtual representation of a process helps intensify by sensing variables and parameters. This “digitaltwin” allows data-driven simulations, design, optimization of processes. Virtualization contributes to risk reduction and the prevention of equipment failure. Availability and accessibility of information from the entire process and external knowledge are essential. The generated data is constantly being processed and visualized in an interpretable manner to adjust production with demand and facilitate process improvements and intensification strategies.

6.4.1  Modular Manufacturing The focus of modular manufacturing includes modular chemical PI. Efforts include reducing the cost and improving the reliability of modular subsystems and intensified components that are pre-assembled, transported and installed at a chemical processing site. Objectives of modular manufacturing follow: • Drive cost and reliability improvements in module and component manufacturing (Beck 2016, Bielenberg and Palou-Rivera 2019, Yelvington and Ndlela 2020). • Standardize modules and components to drive demand and capital investment within the supply chain.

146  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence • Lower the cost of PI equipment using advanced manufacturing technology. • Reduce technical risk by improving reliability of PI equipment. Key approaches for modular manufacturing are as follows: • Convene the supply chain to identify opportunities for standardizing and intensifying components and modules • Use design-for-manufacturing-and-assembly principles in redesign of modules and components. • Develop new manufacturing process technologies for module and component manufacturing. • Improve reliability and advance standardization through the development, validation, and assimilation of design tools. Expected outcomes of modular manufacturing are: • Cost reductions of PI equipment and reductions of business risks for module manufacturers. • Reliable components demonstrated in commercial deployment, reducing technical risks for module manufacturers. • More robust supply chains driven by breakthrough PI equipment. Modular process intensification (MPI) can manage investment risks through standardizing manufacturing of small-scale processes to achieve similar economic benefits as production at a larger scale. MPI focuses on reducing the cost and improving the reliability of modular subsystems and intensified components that are pre-assembled, transported and installed at a processing site. For example, production of biofuels and bioproducts at feedstock production sites leads to sustainable operation by reducing cost and safety concerns associated with transport and storage of low-density feedstocks and improving local economies. When deployed broadly, MPI improvements can reduce energy and carbon footprints while also improving economics (Bielenberg and Palou-Rivera 2019). Life cycle analyses should be conducted to capture both upstream and downstream impacts. MPI deployment in energy-intensive industries has been limited by several barriers, including: • Capital costs and maintenance risks involved in committing to new processes. • High complexity of an intensified, modular system, without simplifying standardization techniques. • Insufficient or inadequate software and design tools and data to develop intensified processes. • Challenges in developing standardized design and manufacturing protocols for complex new technology spaces at an early point in the technical and commercial development. • Limited understanding of MPI technologies across a broad range of key industry participants. Modular processing is a promising and sustainable technology. With a unitary modular manufacturing process, a system is a conglomerate of units that produces a specific output. Each module is built, transported, and then installed at the new plant site. This allows plant development and site preparation simultaneously for easy start up. A parallel modular process accepts more modules or removes them depending on market conditions. This ability to scale up or down presents a less risky manufacturing option for stakeholders and optimizes the use of assets. Such modules promote safe and sustainable engineering as the assembly of modules can occur near the primary feedstocks or the target user. This promotes less risk in the transportation of unsafe and low-density feedstocks, such as biomass, through reduced transport distances. Sustainable engineering can help meet the UN Sustainable Development Goals, especially the goals of reducing poverty, improving health, providing clean water and energy, supporting quality work, improving industry and infrastructure, and enabling responsible production practices (Cignitti et al. 2018, Yelvington and Ndlela 2020).

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Streamlining design with a modular approach Modular designs may reduce the time for design, schedule duration, and cost uncertainty, and may help achieve a faster start-up. Modular designs improve construction management by modifying process locations, applications, and scale, thereby reducing engineering, construction, and cost risk. A project design for oil and gas is often the first key area for a modular approach with standard design modules and existing templates. This includes dehydration, liquefied natural gas (LNG) separation, gas purification, large compression operations, and acid gas removal. For example, compressor modules can be standardized to be reused in various settings, accounting for throughput, contaminants, viscosity, weight, and size limits. Note for LNG plants, however, that replicating modules to make up the plant can lead to logistical and construction cost tradeoffs and safety risks in remote construction workforces. As modular design and construction projects become more common, even the less experienced engineer can complete datasheets, develop CAPEX estimates and communicate with all stakeholders working on the project using integrated engineering tools. Modularization supports business strategies with business leaders seeking lower capital project investments, while still driving towards business growth. An optimized design for minimum CAPEX and OPEX reduces energy consumption and increases sustainability, and leads to intensified processes (Beck 2016).

6.4.2  Heat Integration Process heat integration with waste heat recovery can improve efficiency considerably. Cogeneration reduces utility costs which are typically 10 to 20% of the total cost of processing and manufacturing in industries, including chemical process industries. Better overall energy efficiency is also beneficial for environmental protection by reducing GHG and other emissions. Energy integration can be especially important for utilities with energy intensive processes, such as petrochemical industries, refineries, and iron and steel companies. Other possible techniques for improving process energy efficiency are waste heat recovery, process optimization, and optimum energy-supply systems (Alhajji and Demirel 2015, Ang and Foo 2021, Demirel 2021). Heat integration by pinch analysis is based on the minimum approach temperature (∆Tmin). Pinch analysis-matches available and required heats between hot and cold streams in a process using composite curves and grand composite curves for a specified value of ∆Tmin. This analysis yields the total heat load to be recovered and the minimum hot and cold utilities needed, as well as an optimum heat exchanger network system (HENS). Figure 6.13 shows the typical relationship between the cost and minimum approach temperature ∆Tmin. An increase in ∆Tmin raises energy costs and lowers capital cost. The optimum value of ∆Tmin is identified at the minimum total annual cost, accounting for capital cost and operating cost. Table 6.7 shows that the optimum value for ΔTmin is generally in the range of 3 to 40°C depending on the type of processes (Demirel and Gerbaud 2019, Demirel 2021). The minimum number of heat exchangers NHx, min needed in a HENS design can be determined as follows: NHx, min = NHs + NCs + NHU + NCU – 1

(6.19)

where NHs and NCs are the number of available hot and cold streams, respectively, and NHU and NCU are the number of hot and cold utilities, respectively. Table 6.7.  Optimum minimum approach temperature ∆Tmin for various processes. Process

Optimum ∆Tmin, oC

Refinery

20

Chemical/petrochemical Cryogenic

10–20 5

148  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence

total cost Cost energy cost

Investment cost

Hot utility

capital cost

Hot utility ∆Tmin

Cold utility Hot utility

Optimum

Cold utility ∆Tmin

Cold utility

∆Tmin

∆T

(a)

Operating cost

(b)

Figure 6.13.  (a) Optimum ∆Tmin as energy cost and capital cost vary in heat integration, (b) impact of ∆Tmin on the cost.

6.4.3 Optimization Methods for modeling, analysis, and optimization in engineering begin with deciding on system geometry, components, and outputs. Optimization determines the most favorable conditions for Fertilizer/ optimal performance including minimum cost or maximum profit under given constraints. The search Solvent Animal feedlarge number of design variables and Water for an optimal design may be challenging because of a very CO2 Evaporation Methanol initial conditions in a plant operation. In practice, it is often more straightforward to examine several alternative configurations, optimize their performances, and compare the optimized alternatives. Biodiesel Oil Phase Optimization been applied to energy systems (Dincer etEsterification al. 2017). Algae has pondoften Harvesting Separation Algae In complex designs, engineers Extraction must deal with many degrees of freedom.Glycerol This requires a Culture mathematical method for searching for the root of the objective function in the feasible region identified by the Water constraints. This Water approach may lead to better and morePower complex designs. For Anaeorobic Biogas complex engineering problems, there can be challenges. For instance, there may not be a single Generation Digestion optimum solution, or it may be difficult to find a global optimum. treatment procedures. There are various optimization Two ofSludge these are: Municipal Water

wastewater

• In designing a heat exchanger network system, one can estimate the total annual cost for a possible combination of heat exchangers for required heating and cooling loads. There may exist significant differences in control, operability, safety, and environmental impact in a complex HENS design. • Mathematical modeling is the key to a good process design. However, mathematical modeling usually deals with the optimization of the design variables for a specified process. PI in a design is often a result of changes in the process and engineers need to be able to make final design decisions after carefully considering the results of mathematical modeling (Demirel 2013a, Harmsen et al. 2018).

6.4.4  Process Synthesis Process synthesis can provide significant cost savings, in some instances up to 60%, using optimal equipment and interconnections under optimum operating conditions. Incorporation of PI principles into synthesis can lead to better process economics, safety, and sustainability. Powerful synthesis methods for various subsystems include heat exchanger network synthesis, distillation network synthesis, membrane network synthesis, and reaction network synthesis. Integration of processes can lead to energy savings through shared utilities as well as a circular economy approach since waste outputs of a process may be recycled to another process as a feedstock. Below, some integrated

Process Intensification  149

processes and their operations are briefly discussed, to move toward PI (Tian and Pistikopoulos 2019, Demirel et al. 2019). Municipal wastewater treatment Municipal wastewater treatment consists of biological and chemical systems and is essential for clean water and public health. A typical treatment plant includes the following steps (Alfaro et al. 2006, Rao et al. 2017, Zwaan 2019, Ondrey 2021b)

Zwaantreatment 2019, Ondrey 2021b) 1. Primary involving sedimentation of solid materials. 1. Primary treatment involving sedimentation of solid materials. 2. Secondary treatment for removing suspended and dissolved organic materials. 2. 3. Tertiary treatment for final treatment of water prior to discharge into the environment. 3.

Figure 6.14 shows the processes, intermediates, and products in a municipal waste treatment facility.

Processing municipal solid waste/treatment

• Incineration • Pyrolysis, gasification, anaeorobic digestion, combustion

Intermediates

• Biodegradables, landfills, fertilizers • Secondary raw materials - metals • Solid recovered fuels

Products

• Compost • Heavy metals, glasses, nutrients

Figure 6.14.  Municipal waste treatment pathways and some products (Rao et al. 2017).

Integration of wastewater treatment with algal cultivation Algal biomass can be processed and converted to biofuel by a variety of methods, while the remaining residual algal biomass after lipids extraction can be utilized (i) for biogas production, Algal biomass can b (ii) as fertilizer, or/and (iii) as feed for animals. Beside the transesterification of algal lipid to biodiesel, liquefaction of residues with pyrolysis and hydrothermal processes are available for the conversion of relatively dry biomass feedstocks. The use of solvents in lipid extraction may lead to environmental, health and safety issues. Affordable algal biofuels have the potential to contribute to improving the sustainability of the transportation sector. The removal of many dissolved inorganic compounds during wastewater treatment includes nitrogen N and phosphorus P, which are potential nutrients for microalgae cultivation. Figure 6.15 shows the main steps in algae-based biotechnology integrated with municipal wastewater treatment. Algae cultivation is achieved in photobioreactors and ponds, where nutrients and CO Microalgae exhibits significant potential for biological removal of hazardous shows the main steps in algae2 are introduced. or toxic compounds due to their negatively charged surfaces. Algae cultivation integrated 2with a municipal wastewater treatment facility can address the circular economy with valuable nutrients recovered from wastewater and recycled for algal cultivation (Allen et al. 2018, Guo et al. 2019). This charged surfaces.health and safety in a sustainable way, subject to the following goals: improves environmental 1. 2. 3. 4. 5.

Waste reduction Materials management Pollution 1. prevention 2. enhancement Product Sustainable energy-water nexus

150  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence

CO2 Evaporation

Algae Culture

Algae pond

Water

Harvesting

Water

Nutrients/ Water

Wastewater treatment

Solvent Oil Extraction Water

Municipal wastewater

Fertilizer/ Animal feed Phase Separation

Anaeorobic Digestion Sludge

Methanol Esterification

Biogas

Biodiesel Glycerol

Power Generation Biopower

Figure 6.15.  Flow diagram for nutrient recovery from municipal wastewater for algae biomass growth and biofuel and biopower production.

A national assessment of land requirements for algae cultivation accounts for climate, fresh biofuel and biopower production. water, inland and coastal saline water, wastewater resources, and sources of CO2. To prevent spatial and temporal mismatches between algae growth and wastewater resources, microalgae farms should be close to wastewater sources. Microalgae consume nitrogen and phosphorous in a fixed ratio called the Redfield ratio, which represents a molar ratio of N:P. The bioavailability of P may vary depending on the wastewater source. Changes in the N:P ratio for wastewater may affect the valorization of micro algae biomass. Temperature variations affect microalgae productivity. Contaminants like heavy metals, persistent organic pollutants, and microbial contaminants can inhibit microalgae growth and limit the valorization of the biomass. Sewage sludge‑to‑hydrogen Sewage sludge can be converted to renewable hydrogen fuel for fuel cell mobility and power generation. A typical waste-to-hydrogen facility can process 1 ton of dried sewage sludge per day to generate 40 to 50 kilograms of hydrogen per day. In addition to wastewater sludge, plastic, paper, municipal solid waste, and other refuse can be processed. The waste is heated to a high temperature and converted to a gas, from which pure hydrogen is extracted. The waste-to-hydrogen facility is carbon-neutral and generates its own fuel in a closed-loop process. With 95% of the world’s hydrogen produced from natural gas and coal, producing renewable hydrogen from waste is an important pathway for increasing global clean energy supplies while addressing climate change and global waste concerns (Bailey 2021a). Degradation of microplastics in wastewater Conventional treatment approaches that tackle microplastics in wastewater involve physical separation techniques without degrading them. A new treatment degrades microplastic pollutants using electrocatalytic oxidation at ambient temperature and pressure. Electricity activates specific electrodes that produce, in situ, powerful oxidants for microplastic degradation to water and CO2, without chemicals and toxic wastes. A boron-doped diamond electrode, which is a conductive material with wide potential, can degrade around 90% of the microplastics in water (Bailey 2021b). The electrocatalytic process also works well in the presence of other contaminants, including pharmaceutical compounds and detergents (Alfaro et al. 2006). Decomposing plastics to monomers Waste plastics can constitute a resource, with well over 300 million mt of plastics waste being generated per year, creating significant environmental challenges. In chemical recycling, polymers are reduced to their monomers, which are similar to fossil-fuel-based monomers. For example,

Process Intensification  151

some catalytic processes can convert these monomers into useful polymers. Other efforts are biological depolymerization of polyethylene terephthalate (PET) in a biorecycling process with specially engineered enzymes extracted from bacteria into monomers, monethylene glycol, and purified terephthalic acid. PET can be depolymerized by microwave technology to speed it up the saponification reaction with sodium hydroxide in water. In another technology, an iron-based ionic liquid converts any PET waste into high grade materials. Recycling of multicomponent plastic packaging films of layered materials can be dissolved selectively by specific solvents, where one component of the multi-layer film is dissolved while the other is left as a solid and separated by rotation while the solvent is recovered and recycled. Another approach is the pyrolysis of waste plastics via thermal decomposition in the absence of oxygen to produce pyrolysis oil, and then to separate it into fractions as in petroleum refining (Jenkins 2019). Polycaprolactone (PCL) and polylactic acid (PLA) are both biodegradable plastics. They are used for food containers and biomedical applications, and often take months or years to decompose. By encasing plastic-degrading enzymes into the plastics allows them to decompose into monomers in hours to days upon exposure to humidity and temperatures of 40 to 60°C (DelRe et al. 2021).

6.5  Biochemical Processes and Bioproducts Various enzymes and microorganisms convert organic compounds into alcohols, biogas, biofuel, food/feed, and other chemicals. For enhanced biomass conversion modeling one needs: (i) biomass characterization and conversion data, (ii) thermodynamic and physical property models, (iii) transmembrane flux information, and (iv) membrane module and process models (Demirel 2015, Demirel 2018b, Escobar and Laibach 2021). Biochemical reactions occuring at lower temperatures have lower conversion rates requiring less energy. Therefore, suitable process control systems help maximize the required product and reduce the side reactions and hence wastes. Two biochemical processes operated at industrial scales are fermentation and anaerobic digestion. Bioethanol and biodiesel have the largest share of the global biofuels markets. Fermentation converts first-generation biomass including corn, sugarcane, and wheat to bioethanol, while biochemical and thermochemical processes convert lignocellulosic biomass to bioethanol, other fuels, and chemicals. The cost of the biomass conversion processes increases in the following direction: triglycerides → starch → lignocellulosic, while the cost of biomass increases in this order: lignocellulosic → starch → triglycerides. Figure 6.16 shows some of the basic steps of the conversion processes used in biofuel production. The continuous

Biomass

Sugar crops

Pretreatment process

Intermediates

Mechanical grinding

Glucose

Starch crops Lignocellulosic Algae OIlseed crops

Thermochemical hydrolysis Biochemical single and multiple enzyme treatment

Conversion process

Biochemical fermentation

Xylose

Lipids

Products

Residue /Waste

Ethanol

Carbon dioxide

Butanol

Animal feed

Acetone Chemical esterification

Xylitol Biodiesel

Figure 6.16.  for products high value from products from biomass (Prado-Rubio al. 2016). Figure 6.16. Processes for Processes high value biomass (Prado-Rubio et al.et 2016).

Lignin Residue

152  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence acid-catalyzed lignin depolymerization yields stable and high monomers including guaiacol, syringol, and phenolic derivatives (Prado-Rubio et al. 2016, Galána et al. 2019).

6.5.1  Biopharmaceutical Processes Biomolecule separation may need four to five chromatography steps that decrease product yield and increase cost of production. Some highly selective affinity resins can improve the purification process with fewer chromatography steps. PI with increased productivity may select smaller volumes of material, integrate upstream and downstream unit operations, and scale up. Next-generation ion exchange chromatography resins with strong cation/anion exchange offer high capacity, high resolution, and salt tolerance at faster flow rates, permitting increased throughput. In addition, the use of membrane chromatography may increase throughput. In some cases, ion exchange is being used instead of affinity chromatography, mainly motivated by cost reasons (Harmsen et al. 2018, Cignitti et al. 2018).

6.5.2 Biotechnology Applications of bio-based processing offer economic advantages over traditional petrochemical approaches. Five basic building blocks for most chemicals and polymers are methane, ethane, propane, butane, and aromatics. Butenes include the four-carbon chemicals butene-1, butene-2, isobutylene, and butadiene and are typically produced from petroleum oils. Therefore, the biological production of butenes is an opportunity. For example, butanediol, butadiene, butanol, aromatic hydrocarbons benzene, toluene and xylene (BTX) and three key nylon intermediates— hexamethylene diamine, caprolactone, and adipic acid—can be produced by biological routes. Besides, biobased isoprene, polyethylene, p-xylene, polyols, and polylactic acid offer sustainable processes that use biotechnology with cost models to increase or maximize efficient use of resources. Bio-based processes can provide deployment flexibility, greater price stability and lower safety and operations risks. PI with multienzymes creates new opportunities with various reactor configurations, depending on the characteristics of each enzyme, in isolated, immobilized, or contained forms (Figure 6.17). creatingconsiderable more pathways. Forreduction example, co-cultivation increases With recyclable by enzymes, cost in the production of chemicals can be to ethanol. With a coachieved. For instance, with immobilized enzymes, cost effective separation of high-value products such as pharmaceuticals is possible. As a specific example, using four-enzyme cascade reaction,

Biochemical process intensification

Multidisciplinary synergy

Enhanced reaction systems In-situ product removal and recovery Manufacturing sector

Enhanced substrate utilization Biotechnology biochemical technology Co-cultivation Co-fermentation Multienzyme systems

Enhanced biocatalytic processes with multienzymes Genetic engineering

Figure 6.17.  Biotechnology intensification strategies (Prado-Rubio et al. 2016).

Process Intensification  153

glycerol can be converted to phosphate esters as intermediate chemicals for the synthesis of imino sugars, which can inhibit glycosidases and be used therapeutically for some human disorders. Furthermore, multienzyme systems can facilitate the production of lactobionic acid, mainly used in pharmaceutical and food technology, from the enzymatic oxidation of lactose in a biorefinery platform (Prado-Rubio et al. 2016). Multiple microorganisms as consortia can be used to intensify a fermentation process for various products by creating more pathways. For example, co-cultivation increases the conversion of lignocellulosic biomass to ethanol. With a co-culture fermentation with organisms, it is possible to produce acetone, butanol, and ethanol. In dark fermentation, a series of biochemical reaction with microorganism consortia can produce 94% more biohydrogen, while the production of methane is inhibited (Prado-Rubio et al. 2016).

6.5.3 Bioproducts Bioproducts are derived from both mature industries such as the pulp and paper industry or producers of ethanol as well as emerging technology areas such as distributed biorefining with a wide range of biomass feedstocks converted to diverse products like fuels, energy, chemicals, and materials. The common goal is converting renewable feedstocks to value-added products with lower overall capital costs and higher energy efficiencies. The following set of building block compounds from biomass can be best suited to replace petroleum-derived chemicals (Table 6.8). These chemicals with multiple functional groups produced from biological conversions of plant-based feedstock can be converted to high value intermediate bio products using chemical conversions (NREL 2004). Figure 6.18 shows various bioproducts from sugars and other biomass resources in an integrated biorefinery. Biomass

Hemicellulose Xylose Xylose Tetrahydrofurfural Pentanol

.

C1 Methane Formic acid Syn gas

C2 Ethanol Acetic acid Glycol aldehyde

Cellulose

Glucose

C3 Lactic acid Glyceraldehyde

Lignin Guaiacols Guaiacols Phenol monomers Alcohols

C4 Maleic acid Erythrose

C5 Furfural Levulinic acid

C6 Sorbitol Fructose Mannitol

Figure 6.18.  Bioproducts from sugars and lignocellulosic biomass (Kohli et al. 2019).

6.6  Chemical Processes Chemical reactions in various processing and manufacturing sectors represent a broad field for PI, Bioproducts Source of renewable resources Chemicals and materials Key uses and products including catalytic conversion to value added chemicals and increased fuel Succinic acid efficiency.

154  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence Table 6.8.  Main bioproducts with key uses (NREL 2004). Bioproducts

Source

Chemicals and materials

Key uses and products

Succinic acid (plus fumaric and malic acids)

Bacterial fermentation of glucose chemical oxidation of 1,4-butanediol

1,4-Butanediol, tetrahydrofuran, y-butyrolactone, maleic anhydride, pyrrolidones

Solvents, polyesters, polyurethanes, nylon, food and beverage acidity control, fabrics, inks, and paints

2.5-Furan dicarboxylic acid

Chemical dehydration of glucose, oxidation of 5-hydroxymethylfurfural

2.5-Dihydroxymethylfuran, 2.5-bis(amino methyl) tetrahydrofuran

Polyethylene terephthalate analogs, polyamides such as nylon, plastic bottles and containers, fabrics, carpet fiber

3-Hydroxy propionic acid

Bacterial fermentation of glycerol or glucose

1.3-Propanediol, acrylic acid, methyl acrylate, acrylamide

Polytrimethylene terephthalate, acrylate polymers, carpet fiber, paints and adhesives, superabsorbent polymers for diapers, contact lenses

Glycerol

Chemical or enzymatic transesterification of vegetable oils

Propylene glycol, ethylene glycol, 1.3-propanediol, glyceric acid, lactic acid, acetol, acrolein, epichlorohydrin

Polyesters, butanol, soaps, cosmetics, food and beverages, antifreeze/deicing fluids, pharmaceuticals, coatings, carpet fiber

Sorbitol

Hydrogenation of glucose from corn syrup, bacterial fermentation (under development)

Isosorbide, propylene glycol, ethylene glycol, glycerol, lactic acid, alkanes

Sweeteners, mouthwash and toothpaste, sugar-free chewing gum, polyethylene terephthalate analogs, fuel ingredients, antifreeze/deicing fluids, water treatment

Xylitol/arabinitol

Hydrogenation of xylose, extraction from lignocellulose, bacterial fermentation (under development)

Propylene glycol, ethylene glycol, glycerol, xylaric acid, furfural

Sweeteners, sugar-free chewing gum, cough drops and medicines, antifreeze/deicing fluids, new polyesters

Levulinic acid

Acid-catalyzed dehydration of sugars

2-Metthyitetrahydrofuran, y-Valero lactone 1.4-pentanediol, acetyl acrylic acid, diphenolic acid, caprolactam, adiponitrile, pyrrolidones

Fuel ingredients, solvents, acrylate polymers, BPA-free polycarbonates, polyesters, polyamides, pharmaceuticals, herbicides, plastic bottles and containers the C4 and BTX building blocks

Itaconic acid

Fungal fermentation of glucose

4-Methyl-y-butyrolactone, 3-methyltetrahydrofuran, pyrrolidones

Styrene-butadiene copolymers, polyitaconic acid, rubber, plastics, paper and architectural coatings

3-Hydroxybutyrolactone

Multistep chemical synthesis from starch

3-Hydroxytetrahydrofuran, acrylate-lactone 3-aminotetrahydrofuran

Solvents, synthetic intermediates for pharmaceuticals, polyurethane fiber analogs, new polymers

Glutamic acid

Bacterial fermentation of glucose

1.5-Pentanediol, glutaric acid, 5-amino-1-butanol

Polyesters, nylon analogs, glutamate flavor enhancers, fabrics, plastics

Glucaric acid

Oxidation of starch or glucose by nitric acid or bleach

Lactones, polyhydroxy polyamides, adipic acid

Solvents, nylon analogs, branched polyesters, fabrics, plastics, detergents

Aspartic acid

Enzymatic amination of fumaric acid, fermentation route (under development)

2-Amino-1.4-butanediol, 3-aminotetrahydrofuran, aspartic anhydride amino-ybutyrolactone

Aspartame, polyaspartate, sweeteners, chelating agents for water treatment, super absorbent polymers for diapers

Process Intensification  155

6.6.1  Fischer-Tropsch Synthesis Fischer-Tropsch synthesis is a well-known indirect chemical process for producing biofuel from a syngas containing mainly CO and H2. Syngas is produced from gasification of a biomass or fossil fuels. A representative Fischer-Tropsch (F-T) reaction is (2n+1)H2 + nCO → CnH2n+2 + nH2O  −170 kJ mole–1 (at 250°C and 15 atm)

(6.20)

In the production of diesel fuel, “n” can be in the range of 12–25; therefore, a molar ratio of H2 to CO close to 2 is required. An iron-based catalyst and an operating temperature of 350°C will produce mostly gasoline, while a cobalt base and an operating temperature of 200°C will produce mostly diesel fuel (Demirel 2018b). F-T synthesis can intensify a biochemical conversion of biomass to liquid transportation fuels as it recycles the carbon dioxide captured by the plant. This can help promote the rural economy and hence sustainable development (Wang and Demirel 2018).

6.6.2  Methanol, Ammonia and Hydrogen Production PI can reduce waste, improve energy efficiency, and potentially enable the use of new feedstocks. Renewable resources such as wind and biomass can be used to produce methanol and ammonia using green hydrogen, carbon dioxide, and nitrogen, as seen in Figure 6.19. Here, the carbon dioxide would be recovered from the fermentation of an ethanol plant. Renewable hydrogen comes from the electrolysis of water using wind or solar photovoltaic power. Chemical recycling of CO2 to methanol provides renewable transportation fuels (Matzen et al. 2015a). Methanol synthesis needs a catalyst, usually Cu/ZnO/Al2O3. During the synthesis of methanol, the following reactions occur: CO OH + H ∆H°(298 K) = − 49.4 kJ/mole Only two of 3these reactions are independent. L 2 + 3H 2 = CH 2O

(6.21) -

formulations with fugacities CO + 2H2 = CH3OH ∆H°(298 K) = − 90.55 kJ/mole CO + H2O = H2 + CO2

(6.22)

∆H°(298 K) = − 41.12 kJ/mole

(6.23)

Only two of these reactions are independent. Langmuir-Hinshelwood Hougen-Watson (LHHW) kinetics formulations with fugacities are used for modeling renewable methanol production. The Wind

Water Electrolyte

Wind Farm

Transformer

Electricity Electrolyzer Oxygen

Air Separation Unit Oxygen

Ammonia

Biomass

Renewable Resources

Nitrogen Ammonia Synthesis

Hydrogen Compression Storage Delivery Hydrogen

Hydrogen

Ethanol Plant CO2

Ethanol

CO2 Capture & Compression

CO2 CO2

Methanol Synthesis

Methanol

Figure 6.19.  Green ammonia, methanol, hydrogen production from renewable sources, along with ethanol, carbon dioxide, and oxygen.

156  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence concept of a “Methanol Economy” mainly refers to carbon recycling (Lachowska and Skrzypek 2004, Bandose and Urukawa 2014) with improved new methods for the efficient reductive conversion of CO2 to methanol. Methanol is produced almost exclusively by the ICI, the Lurgi, and the Mitsubishi processes. These processes differ mainly in their reactor designs and the way in which the produced heat is removed from the reactors. Table 6.9 shows the reactor operating conditions for various reaction combinations (Olah et al. 2011, Matzen and Demirel 2016). The electrolytic hydrogen production cost is the largest contributor to the cost of the integral plant. Table 6.10 presents the sustainability metrics for methanol production, which show that the methanol facility requires 1.39 mt CO2/mt methanol. The environmental impact metrics show that the integral methanol facility reduces emission by around 0.84 kg CO2/kg methanol when utilizing it as a chemical feedstock and recycles 0.53 kg CO2/kg methanol after its complete combustion (Matzen et al. 2015b). A 110,000 ton/yr CO2-to-methanol plant can recycle over 160,000 ton/yr of CO2, which is equal to the emissions of nearly 60,000 cars (Ondrey 2019c). Ammonia is produced from N2 and H2 using the well-established Haber-Bosch process. The reaction is given below: N2 + 3H2 = 2NH3 (6.24) The N2 for this process is typically produced from an air separation unit. Ammonia is a promising hydrogen carrier and zero-carbon fuel. Ammonia does not require cooling to extreme temperatures for liquefaction and has a higher energy density than liquid hydrogen, making it more efficient to transport and store. A fully electrified ammonia plant with zero emissions can avoid emissions of 800,000 mt/yr of CO2, equivalent to the emissions from 300,000 passenger cars. Using ammonia as an energy carrier can help decarbonize the food chain, the nitrogen fertilizer industry and contribute to sustainable agriculture (Grad 2019, Bailey 2020a,b). Green hydrogen is generated via electrolysis and its use is in large part being driven by industry, including ammonia and methanol production, petroleum refining operations, and steel manufacturing. Traditional pig iron manufacturing processes usually use coke as a reducing agent and produce carbon dioxide. New production process use hydrogen instead of coke, which also reacts with the oxygen in the iron ore producing water and ultimately producing ‘green steel’. Conventionally, hydrogen is produced using natural gas via the steam methane reforming (SMR) process, which generates 10 to 12 units of CO2 per unit of hydrogen, on a mass basis. Electrolyzer Table 6.9.  Operational conditions of methanol synthesis with the catalyst Cu/ZnO/Al2O3. Reactions

T, oC

P, bar

Based on reaction (1) and (2)

215–270

50

Based on reaction (1) and (3)

180–280

51

Based on reaction (1) and (3)

250

30

Table 6.10.  Sustainability metrics for an integral methanol plant (Matzen et al. 2015a). Metric

Value for methanol production

Material intensity CO2 used/Unit product. mt/mt

1.39

H2 used/Unit product, mt/mt

0.19

Energy intensity Net duty/unit product, MWh/mt Net cost/Unit product, $/mt Environmental impact Total CO2e/Unit product, mt/mt * For an assumed carbon fee of $2/mt CO2.

  9.55 828.67   −0.85

Process Intensification  157

technologies that split water and green electricity can produce zero-carbon pure hydrogen. The cost of electricity from wind and solar energy is declining over time. Advancements in electrolyzer technology and capacity are contributing to reducing electrolyzer costs (Abanades et al. 2022, Bailey 2020a,b). Bio-based hydrogen peroxide is receiving increased interest. This is a new enzymatic process that can coproduce gluconic acid and hydrogen peroxide, replacing a complex conventional process with safety concerns and providing economic benefits. This engineered enzyme is stable in the highly oxidizing environment required for hydrogen peroxide production. Hydrogen peroxide can be used for water treatment applications in the upstream oil-and-gas sector. The same enzyme can also be used to coproduce acetic acid and hydrogen peroxide. Existing hydrogen pipeline networks can transport pure hydrogen (99.9%) over long distances for use in petrochemical industries, petroleum refineries, and other chemical industries including ammonia plants. As noted above, green hydrogen can be made by the electrolysis of water using renewable power, producing zero emissions. Electrolysis splits water into almost pure hydrogen and oxygen gases. The green hydrogen generates no direct carbon emissions and avoids 8.9-ton CO2 eq per ton of green H2. Green H2 contrasts with grey H2, which is typically produced by reforming natural gas and does result in CO2 emissions. An electrolysis system consuming around 50 MW of renewable power is expected to produce one ton per hour of green hydrogen. This would be sufficient to replace around 20% of the refinery’s current grey hydrogen consumption, avoiding around 80,000 tons of CO2 equivalent emissions per year (Matzen et al. 2015a). Renewable hydrogen must become cost competitive with fossil-based hydrogen to play a significant role in decarbonizing the electrical utility, industry, and transport sectors. Hydrogen and carbon capture, utilization and storage (CCUS) may lead to an integrated energy transformation. Integration of green H2 production, distribution, storage, and use may generate zero-carbon, and can drive a stationary fuel cell to provide clean, reliable power. This type of shift or transition requires support via policies and regulations, as well as improved economics (Matzen et al. 2015a).

6.7  Thermochemical Processes with Chemical Looping Systems Thermochemical conversion processes of combustion, gasification, and pyrolysis take place at high temperatures (450°C–1200°C) and can convert second-generation biomass feedstocks and wastes to useful fuels and chemicals. Thermochemical processes enhanced through integration with chemical looping systems can lead to sustainable designs with inherent carbon capture (Demirel et al. 2015).

6.7.1  Thermochemical Processes Consider a biomass substance, represented by CnH2m. The biomass is oxidized to CO2 and water and releases heat of combustion, which can be used to produce steam and electricity, for example, in a Rankine cycle. The oxidation reaction follows: CnH2m (n + 0.5m)O2 → mH2O + nCO2 (6.25) In a conventional gasification process, biomass (or another carbon-containing feedstock) reacts with limited oxygen (or air), CO2, and steam at high temperatures (750°C–1100°C) to produce a synthesis gas (bio syngas) containing mainly H2 and CO and small amounts of CO2, methane and other substances. The steam gasification and reforming reactions respectively follow: CnHmOp + (n – p)H2O → nCO + (m/2 + n – p)H2 (6.26) CnHm + nH2O = nCO + (m/2 + n)H2 (6.27) On a dry basis, H2 and CO contents of bio syngas are around 32 vol% and 29 vol%, respectively. The water-gas shift reaction can increase the hydrogen content from 6–6.5 vol% to 30 to 50 vol%, according to the following: CO + H2O = CO2 + H2 (6.28)

158  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence The overall yield and energy efficiency are 23%–41% and 32%–51%, respectively, for biomass-based hydrocarbon production processes. Purification of the syngas normally accounts for 60% to 70% of the total capital cost.

6.7.2  Chemical Looping Systems Chemical looping systems use oxygen carriers to transport oxygen for oxidation reactions to produce only carbon dioxide and water as outputs. From these, carbon dioxide is easily separated. The major chemical looping systems are (Noorman and Gallucci 2011, Dennis et al. 2011, Moghtaderi 2012, Demirel et al. 2015, Ruthwika et al. 2020, Kumar et al. 2022): • chemical looping partial oxidation and autothermal reforming, and • chemical looping CO2 acceptor reforming. Advanced, efficient, and low-emission energy technologies utilizing renewable and nonrenewable resources are important for a sustainable energy technology which will play a major role in addressing global climate change as well as international politics and trade. Well-designed and operated systems for chemical-looping combustion/gasification of fuel/biomass offer scalable, diverse, economical, and environmentally sustainable energy pathways with inherent carbon capture. Chemical-looping combustion (CLC) is a novel technology in which power production and CO2 capture are intrinsically combined using an oxygen carrier (OC) that transfers oxygen from the air to the fuel, preventing direct contact between them. The oxygen carrier is composed of a metal oxide as an oxygen source. The fuel may be coal, natural gas, or biomass. The OC can be alternately oxidized and reduced. The product gas contains mainly CO2 and water undiluted with nitrogen. Note that there is little production of nitrogen oxides (NOx) as the high temperatures associated with flame use are avoided. The oxidation of the OC is strongly exothermic and hence can be used to heat an air flow to high temperatures (1000–1200°C) and can drive a gas turbine. Interconnected fluidized bed systems can be used for chemical-looping technology (Figure 6.20) where OC particles are circulated between an air reactor where oxidation of the OC takes place a fuel reactor where the OC particles are reduced with fuel. The oxidation of the OC carrier is strongly exothermic and hence can be used to heat the air flow to high temperatures (1000–1200°C) and can drive a gas turbine to produce power. It is this inherent ability for the separation of CO2 that makes the CLC an invaluable tool in low-emission energy technology. The main advantages of CLC are: • Over 90% CO2 is captured at low cost. • Separation of water is based on cooling/compression of the product gas containing mainly CO2 and water at process pressure. • No or very little thermal NOx production occurs because of the low temperature. • The process is compatible with sulfur and mercury capture technologies. • Heavy metals may stay with the ash. • Higher thermodynamic efficiencies often are attained. • No hot spots exist with fluidized bed technology. Some disadvantages are:

• • • •

Increased complexity via operation of dual reactors. Requirement for oxygen carrier circulation between the reactors. Requirement for solids handling. Lower exhaust gas temperature/pressure for direct coupling with a gas turbine.

Process Intensification  159 N2/O2

Air Me Fuel

CO2/H2O

Depleted air (Mainly N2)

Air Reactor MeO Fuel Reactor

CO2, H2O Product gas

Fuel

Air/Steam

(a)

(b)

Figure 6.20. 6.20. ReactorReactor configurations for chemical-looping technology: (a) Chemical-looping combustion technology system, Figure configurations for chemical (b) Periodically operated chemical-looping technology in packed bed system.

An alternative to fluidized bed systems is packed bed chemical-looping reactor systems that contain stationary OC particles alternately exposed to reducing and oxidizing conditions by periodic switching of the fuel feed (generally natural gas) and air streams (Figure 6.20). With this packed bed reactor technology, the circulation and separation of gas and OC particles are avoided. This may lead to better utilization of OC with more efficient oxidation/reduction cycles (Noorman and Gallucci 2011). Note, however, that for solid fuels like coal and biomass, in situ or separate gasification is required. The main advantages of this reactor concept are:

• • • •

Avoiding cyclone operation and better utilization of the oxygen carrier Controlling the air temperature with the amount of active material in the bed Potentially high thermal energy efficiencies Ability for oxidation to be modeled similarly to an adsorption problem

Fixed bed reactors may produce large amounts of either tar and/or char due to low, nonuniform heat and mass transfer between the gas and solid. The temperature of the reactor is most influential on the product gas composition (Moghtaderi 2012). The product gas may need extensive cleaning. However, packed bed systems use high temperatures and high flow rates of streams with switching systems and require the use of OC particles of large size to avoid excessive pressure drops, which may slow the reduction of the OC particles. Figure 6.21 shows an iron (Fe)-based chemical looping steam gasification system. In this system, a H2/steam mixture is generated in a steam reforming reactor. Some or all the H2/steam mixture is fed to a biomass gasification reactor to produce a gas containing mainly H2, CO2, CH4, and CO. This gas is oxidized to CO2 and steam by hematite (Fe2O3) particles in the fuel reactor, while the Fe2O3 is mostly reduced to FeO particles. In the steam reforming reactor, the FeO particles are oxidized to magnetite (Fe3O4) and transported to the air reactor, where the Fe3O4 particles are oxidized back to Fe2O3. The oxidation and regeneration reactions are (Demirel et al. 2015): 3FeO + H2O → Fe3O4 + H2

+194.3 kJ/mol (steam reforming reactor)

(6.29)

4Fe3O4 + O2 → 6Fe2O3

+314.6 kJ/mol (air reactor)

(6.30)

4Fe2O3 + CH4 → 8FeO + 2H2O + CO2

−365.5 kJ/mol (total) (fuel reactor)

(6.31)

160  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence

Air

Depleted air

Power

Air reactor

Depleted Cyclone Fe2O3 air Power ,HO Fuel reactor CO2 2 Fe2O3

CO2 Condenser H2O

H2,2CO, CH4 CO

FeO

Product gas

Gasification Fe3O4 CO2, H2O reactor Condenser H2O Steam reforming Biomass H2, CO, CH4 reactor H2, H2O

Gas cleaning

Product gas

GasH2 cleaning Condenser Condenser

Water

Fischer-Tropsch synthesis

H2O Liquid transportation fuels Figure 6.21.  Chemical looping systems H2 for gasification for producing liquid transportation fuels through Fischer-Tropsch synthesis.

6.7.3  Hydrothermal Conversion

Hydrothermal2 conversion involves aqueous chemical reactions under high temperature (200 to Liquid transportation fuels biocrude containing organic acids, 350°C) and high pressure (around 15–20 MPa) to produce various ketones, phenols, and char from any kind of biomass (Figure 6.22). Liquid water at high HCOOH/H2 Lactic acid temperature can act as a catalyst and a reactant. As the water viscosity decreases, the solubility of organic substances increases (changing from hydrophilic toLactic hydrophobic), and theHCOOH dielectric acid constant decreases from 78 F/m at 25°C to 14.07 F/m at 350°C. The ionic product of water Kw Zero-valent metal increases 100 fold as the water viscosity decreases. This makes water an excellent medium for Mo Zero-valent metal levels of free solving organic compound sas well as for fast and efficient reactions since the 0high M

Metal oxide MOx

HCOOH/H2

Metal oxide Water MOx

Lactic acid

Lactic acid Zero-valent metal CO2/Steam Mo

Glycerin

CO2/Steam

Zero-valent metal M0

(a)

Metal oxide MOx

HCOOH

Catalyst

Methanol Glycerin

(b)

Metal oxide MOx

methanol and lactic acid. Glycerin

CO2/Steam

(a)

CO2/Steam

Glycerin

(b)

Figure 6.22.  Reactor configurations in periodically operated, packed bed chemical-looping technology: (a) Packed bed system to produce formic acid (HCOOH) and hydrogen, (b) Packed bed system to produce methanol and lactic acid.

Process Intensification  161

H and OH radicals catalyze many acid or base reactions for hydrogenation reactions of any carbon source (Jin and Enomoto 2009, Demirel et al. 2015). For example, Fe(OH)3, as a source of zero-valent metal, can be used in the following reduction/oxidation cycles, in which hydrogen is also produced with Fe0. The reactions follow: Fe0 + CO2 + H2O → FeCO3 + H2 Reduction

(6.32)

3FeCO3 + H2O → Fe3O4 + H2 + 3CO2 Oxidation

(6.33)

The hydrogen yield is around 50% (Jin et al. 2011). For example, glucose from any source can be converted to formic acid with a yield of 75% at a mild temperature of 250°C in the presence of an alkali as a basic output of the hydrothermal oxidation of carbohydrates, according to the above reaction. An alkaline hydrothermal reaction can also be used to convert crude glycerin into lactic acid, which can be converted to biodegradable lactic acid-based polymers (Jin and Enomoto 2009). Carbon dioxide to formic acid Figure 6.22a shows the reduction of CO2 to produce formic acid using the oxidation of a zero-valent metal (Zn, Al, Fe, Mn, Ni) under hydrothermal conditions in a periodically operated, chemical-looping packed bed system (Jin et al. 2011): M0 + CO2 + H2O → MO + HCOOH

Reduction

(6.34)

Zero-valent metals of Co and W also have catalytic activity in CO2 reduction at around 300°C. Oxidized metal can be regenerated by a chemical such as crude glycerin, which is converted to lactic acid (Demirel et al. 2015). Oxidized metal can be regenerated by a chemical such as glycerin (Figure 6.24): MOx + CaHbOc → M0 + CaHb–2xOc + xH2O Oxidation

(6.35)

Considering iron as zero-valent metal Fe , oxidation uses FeCl2 4H2O and glycerin in the presence of NaOH without water to avoid reoxidation of Fe0. The conversion of iron oxide and glycerin is around 100%. The lactic acid yield is around 82% (Jin et al. 2008, Jin et al. 2011): 0

The overall reaction is exothermic and with glycerin, for example, it becomes: CO2 + C3H8O3 → HCOOH + C3H6O3 (6.36) Carbon dioxide + Glycerin → Formic acid + Lactic acid Formic acid to methanol Methanol is widely used as a feedstock and fuel. Methanol can be separated from water more easily than formic acid. Using high temperature water (at 250–300°C) as a source of H2, which can be generated using inexpensive metals as reductants, formic acid can be converted to methanol (CH3OH) in a packed bed chemical-looping system as shown in Figure 6.22b (Yao et al. 2012). The overall reaction for such a conversion is: HCOOH + 2H2 → CH3OH + H2O (6.37) Many metals (Cu, Al, Cu+Al, etc.) can react with water to produce H2 efficiently under hydrothermal conditions. The H2 produced by the oxidation of metals could be active to reduce the formic acid into methanol. Cu can have a high potential for reducing formic acid to methanol under hydrothermal conditions. Further, with in situ production of H2, no storage or transportation of H2 is required. Also, the oxidative products of metals can catalyze the reduction of formic acid. Combination of chemical looping with hydrothermal conversion The highest yield of methanol at hydrothermal conditions using Cu as a catalyst in the presence of Al is about 30%. The reaction takes place at 300°C. Methanol may be formed by the synthesis of CO2

162  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence

Power Lactic acid

HCOOH

Methanol Methanol synthesis

CO2/H2O

Metal oxide, MOx

Zero-valent metal M0 Metal oxide MOx

Air

CO2/Steam

Glycerin

Figure 6.23.  Chemical-looping processes combining combustion of a fuel at the first stage and hydrothermal process of converting CO2 to methanol at the second stage (Demirel et al. 2015).

at theacid. second (Demirel al. 2015). 2 to methanol and H2 from the decomposition of formic Thisstage demonstrates theet possibility of converting CO2 to methanol directly starting with CO2 in a packed bed chemical-looping system. In an integrated withofchemical and hydrothermal tec CO 2 as feedstock process of chemical-looping combustion a fuel withlooping a hydrothermal process of converting 2 to formic acid, as shown in Figure 6.23. It may be possible to convert a fuel 2to methanol, electricity, and other chemicals with inherent carbon capture (Demirel et al. 2015). 2

Capturing and using CO2 as feedstock with chemical looping and hydrothermal technologies in CO2 emissions are a major greenhouse gas from the2 industrial sector. CO2 capture is a broad concept that includes different types of options, ranging from chemical absorption, adsorption by zeolite, The and cryogenic distillation. Chemical looping uses an oxygen carrier (OC) to transfer O2 from the air to the fuel, without any direct contact between them. The OC is a metal oxide that alternates ): between being in oxidized and reduced states, and the product should be mostly CO2 and water, capture at low cost, 2 with considerable elimination of nitrogen oxides. The OC is oxidated in an exothermic reaction, recoverypossible mostly energy without contaminants, 2 and water providing an additional source for O2-depleted air that could be used in turbines. Some of the advantages of chemical looping are (Demirel et al. 2015, Buecker and Ondrey 2019, Jenkins 2019, Ondrey 2019b): cability • overility 90% of CO2 capture at low cost, • CO2 and water recovery mostly without contaminants, • very little NOx production, which • process applicability to solid, liquid and gas fuels, • compatibility with mercury and sulfur capture technologies, • higher thermodynamic efficiency, • no hot spots in the fluidized reactor technology. There are some possible drawbacks, which include:

• • • •

complex operation of fluidized reactors, OC circulation between the reactors and solids handling, include continuous contact between OC and fuel, reduced reduction rate of OC after first cycle and further deactivation.

Process Intensification  163

Chemical looping gasification with Fischer−Tropsch synthesis A gasification process that generates syngas with minimal contaminants and inert gases is required for liquid biofuel production. Gas contaminants include sulfur and nitrogen compounds, alkali compounds, tars, and particulate matter. Cold-gas cleanup technology is a conventional cleanup approach, commonly applied because of its removal efficiency and reliability. The chemical looping gasification (CLG) process may be integrated with a Fischer−Tropsch synthesis (FTS) process to produce crude with low or net-negative CO2 emissions (Figure 6.24). CLG uses an interconnected circulating fluidized bed reactor and a solid-oxygen carrier, such as mineral ores rich in iron or manganese oxides (Matzen et al. 2017, Kumar et al. 2022).

Biomass

Chemical looping gasification

Syngas conditioning and cleaning

Fischer-Tropsch synthesis

Fischer Tropsch crude

Figure 6.24.  Chemical looping gasification for Fischer-Tropsch crude and fuels (Demirel et al. 2015, Kumar et al. 2022).

Kumar et al., 2022).

Chemical looping gasification for ammonia production Novel ammonia catalysts of alkali and alkaline earth metal hydrides such as the LiH/Li2NH pair can Chemical looping gasification for ammonia production Novel ammonia catalysts of alkali alkaline earth metal hydrides such as the LiH/Li can achieve 2NH pair achieve high reaction rates underand milder conditions leading to lower costs and energy requirements. high reaction rates under milder conditions leading to lower costs and energy requirements. Favorable kinetics within a chemical looping process with the materials acting as nitrogenFavorable carriers kinetics a chemical looping process withlooping the materials acting as nitrogen carriers can form ammonia can formwithin ammonia in a two-stage chemical reaction with the appropriate thermodynamics in a twoof the gas–solid reaction (Pereira et al. 2022). (Pereira et al. 2022).

6.8  Green Engineering Processes 6.8 Green Engineering Processes Green engineering (GE) environmental offers the ability redress the damage sometimes engineering, by improving healthtoand safetyor in avoid a sustainable way. Therefore, GEdone can bebya conventional engineering, by improving environmental health and safety in and a sustainable way. driver for increased safety, innovation, job growth, and competitiveness. Cost, energy product pressures Therefore, be aofdriver forasincreased safety, innovation, job growth, and competitiveness. from withinGE and can outside industry, well as increasing R&D investments, are causing positive shifts in how green chemistry is viewed by process otherand industries and Demirel Prado-Rubio et Cost, energy and product pressures fromand within outside(Nguyen of industry, as well 2010, as increasing R&D al. 2016, Prasetyo et al. 2020, Nandiwale et al. 2020). investments, are causing positive shifts in how green chemistry is viewed by process and other industries (Nguyen and Demirel 2010, Prado-Rubio et al. 2016, Prasetyo et al. 2020, Nandiwale et al. 2020). and production reliability. Green engineering accounts for all these concerns and leads to more sustainable Four key concerns in the processing and manufacturing sectors are safety, optimum production, processes and products by reducing the need for raw materials, conserving water, expanding renewable emissions, andand production reliability. materials Green engineering accounts for allexamples these concerns leads to energy usage, using non-hazardous (Kumar et al. 2010). Some of greenand engineering more are: sustainable processes and products by reducing the need for raw materials, conserving water, expanding renewable energysurfactants usage, andand using non-hazardous materials and (Kumar et al. 2010). Some • using more biodegradable eliminating use of phosphates alkalis examples of green engineering are: • •

developing rechargeable alkaline Zn-MnO batteries

2 • using more biodegradable surfactants and eliminating use of phosphates and alkalis • employing microbial fermentation for producing glucaric acid from bio-degradable, non-toxic biomass • • producing developing processes thecarbon carbonfree footprint as well energy water H2sustainable from organic waste, soforasreducing to produce renewable fuel as from food and waste and usage

Ondrey batteries Bennett etalkaline al. 2019,Zn-MnO • (Lozowski developing2019, rechargeable 2 Ascribing economic value to natural processes • employing microbial fermentation for producing glucaric acid from bio-degradable, non-toxic biomass benefits, services, goods we derive from nature. Naturalcarbon capital free accounting aimsfuel to assign economic • producing H2and from organic waste, so as to produce renewable froman food waste valueand to natural capital to quantify the ‘hidden’ economic value of those natural assets and services, which other organics and thereby not having them end up in landfills where they generate methane is a critical component of economic resilience. gas (Lozowski 2019, Bennett et al. 2019, Ondrey 2019c, 2020b, 2021d,c).

164  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence Ascribing economic value to natural processes Natural capital helps us to understand and ascribe economic value to natural processes, in the forms of benefits, services, and goods we derive from nature. Natural capital accounting aims to assign an economic value to natural capital to quantify the ‘hidden’ economic value of those natural assets and services, which is a critical component of economic resilience. Understanding biodiversity net gain Biodiversity net gain may be used for the optimisation of natural capital through the planning of resilient nature networks. Through ‘systems thinking’ more combined benefits can emerge from networks. Fully optimised investments link evaluation of natural capital stocks and flows to a clear understanding of when and where we can more readily attach economic benefit to restoration projects. Four possible steps for optimizing natural capital investments follow: 1) 2) 3) 4)

Align activities with investment opportunities, Identify priority investment areas and measures, Undertake natural capital valuation, Create a natural capital investment platform.

Flow chemistry with screening chemical reactionplatform. space helps increase chemical process 4) Create a naturalofcapital investment safety and sustainability by reducing the material and energy utilized. A suitable reaction space improves process design, performance, and efficiency, as well as facilitating process scale-up strategies (Figure 6.25). Dimensionlessdesign, numbers from conservation and rate equations are directly related to the performance, and efficiency, as well as facilitating process scaleperformance of processes through a smaller number of dimensionless quantities than the number of process variables. Dimensionless quantities include those for heat transfer (Nusselt, Prandtl, and Biot numbers), mass transfer (Sherwood, Schmidt, Peclet, and Biot numbers), as well as mixing and reaction rates (Reynolds, Damkohler, and Thiele numbers). Membranes can also remove additional post-reactor separation steps in many multi-phase reaction systems and can limit the formation of combustible mixtures of vapors in reactors. GreenMembranes chemistry includes algorithms, Damkohler, and Thiele numbers). can also predictive r machine learning,many and automation. The integration of flow reactors with predictive algorithms multioptimizes organic synthesis reactions in line with safely producing products with minimal time and waste material (LePree 2019a, 2020, Lozowski 2019). integration For certain pharmacological broad range time of synthetic routes require molecule safely producingpurposes, productsawith minimal and waste material discovery, reaction optimization, and manufacturing scale-up, with substantial investments of

Real-time process control

Unit intensification Enhanced reliability •Safety •Maintenance

Waste reduction Energy efficiency Real-time analysis Accident prevention Figure 6.25.  Some benefits of flow chemistry as a means of supporting green chemistry.

Process Intensification  165

resources. Machine learning and predictive models with accurate reaction datasets can identify the best synthetic routes and hence reduce chemical waste. Such datasets train and test accurate machine learning algorithms. Green chemistry principles (Lozowski 2019) are an important part of green engineering and include: • atom economy • avoidance of hazards in chemical synthesis • solvent safety • energy efficiency • renewable feedstocks • derivatization • catalysis • green solvents Once suitable synthetic routes are identified, it is possible to interface such technologies with reconfigurable flow reactors to help enable sustainable pathways utilizing renewable feedstocks. Decarbonization Decarbonization involves sequestering the emitted CO2 for long-term storage (e.g., in deep underground caverns), and conversion of CO2 to value-added products. Conventional processes for each of these options often have high-capital costs and kinetic limitations since CO2 is a thermodynamically stable molecule and difficult to react. Therefore, material and process development strategies can play a key role in these conversion methods (Adamu et al. 2020).

6.8.1 Biorefinery A biorefinery can produce chemicals, fuels, electricity, and heat using multiple feedstocks and multiple processes as seen in Figure 6.26. The multiple products can include methane, ethanol, biodiesel, methanol, dimethyl ether, urea, dimethyl carbonate, ammonia, and oxygen while the inputs can include biomass, flue gas, water, renewable energy, and air (Prasetyo et al. 2020). These multi-products are produced by various conversion processes with shared utilities and other resources (Figure 6.26). The flexibility of multi-biochemical production processes from a variety of feedstocks allows for economic stability and supports sustainability (Allen et al. 2018). A modern biorefinery concept with various inputs and multiple outputs was proposed by Prado-Rubio et al. (2016). The relevant processes include the following: • Enzymatic transformations – Pre and post bioreactor transformation to produce food additives, fine chemicals, and pharmaceuticals. • Multienzyme processes – Common in nature and can lead to multiple products and controllable yields in industry. Green hydrogenation processes, assessed with technoeconomic and sustainability analyses, are often the basis for a biorefinery. The required CO2 can come from many sources including biomass fermentation or biofuels. The H2 can come from biological processes or from electrolysis of water using wind energy. Chemicals that cannot saturate the market like fuels should be preferred. Dimethyl carbonate (DMC) is one such product; it is used as a fuel additive, as well as a nonvolatile organic component polar solvent. Dimethyl ether (DME) is another possible fuel additive and solvent (Matzen and Demirel 2016).

166  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence

Renewable Sources

Pre-processes

Intermediates

Biomass

Fermentation

Carbon dioxide

Biofuels

Carbon capture

Water

Reforming

Renewable energy

Electrolysis

Air

Air separation

Conversion Process

Biogas Biochemical fermentation

Hydrogen

Nitrogen, oxygen

Products

Chemical esterification

Residue /Waste Carbon dioxide

Ethanol Methanol

Animal feed

Dimethyl ether

Lignin

Biodiesel Ammonia

Residue water

Figure 6.26.  Block flow diagram of a biorefinery producing multiple chemicals and fuels from multiple feedstocks.

Figure 6.26. feedstocks. PI techniques can address the following difficulties and provide possible solutions: There are two main operations in a biorefinery. The first • Difficulties: chemical and thermodynamic limitations; substrate/product inhibitors; multiple its constituent chemicals such phases; co-factor dependency; unfavorable equilibrium constants.

• Possible solutions: control feeding process; in situ product removal; protein engineering, spatial-time arrangements in single reactors with multienzyme processing opportunities. -T) synthesis of biosyngas (mainly CO and H2 There are two main operations in a biorefinery. The first is the preparation of biomass processes refer to gasification, pyrolysis, and reforming, while hydrothermal conversion processes use h by separating it intoto its constituent such as carbohydrates, triglycerides, protein, and water and catalysts liquefy variouschemicals biomass feedstocks to biolignin. The second is their various commodities and specialty products, as well Hydrotreating requires H2 andconversion can convertinto biomass-based bioas electricity, heat.to Biochemical conversion density and thatand is ready blend with petroleum fuels.processes mainly refer to fermentation, and anaerobic and aerobic digestion using microorganisms. Chemical conversion processes mainly refer to transesterification of lipids, and Fischer-Tropsch (F-T) synthesis of biosyngas (mainly CO and H2) to biofuels. Thermochemical conversion processes refer to gasification, pyrolysis, and reforming, while hydrothermal conversion processes use hot water and catalysts to liquefy various integrating biofuels production therequires production of highbiomass feedstocks to bio-oil, with which refining to biofuels. Hydrotreating requires H2 and (Brennan and Owende 2010) can convert biomass-based bio-oil to a more stable fuel with a greater energy density and that is vitamins, and polysaccharides al. 2019). ready to blend with petroleum(Kohli fuels.etThis provides opportunities for refineries to be built in any location where adequate biomass can be produced to maintain their operations. The development Both, macroalgae and micr of integrated biodiesel and bioproduct production processes can have positive impact on global warming, environmental impact, and energy efficiency (Nazir et al. 2013). Third generation biofuels and energy recovery. The main challenges are high cost of biomass product specifically derived from microalgae are viable alternative energy resources. Sustainability can be achieved by integrating biofuels production with the production of high-value biomass fractions in a biorefinery concept nutrients from was (Brennan and Owende 2010). Algae based bioactives include lipids, carotenoids, proteins, phenolics, vitamins,microorganisms and polysaccharides et al. 2019). improve algae and anaerobic (Allen(Kohli et al. 2018). Both, macroalgae and microalgae are suitable renewable substrates for the anaerobic digestion Synthesizing biofuel and are examples green engineering (AD) process. sustainable AD can be integrated withbioproducts other conversion processesfor and, as a result, improve their sustainability and energy recovery. The main challenges are high cost of biomass production, limited For example, theofintegration microalgae cultivation conversion with CO2 of biomass to biogas, and high biodegradability algal cells,ofslow rate of biological sensitivity of methanogenic microorganisms. The optimization of algae cultivation may be achieved with recovered and recycled nutrients from wastewater treatment. The development and adaptation 52 of molecular biology tools can improve algae and anaerobic microorganisms (Allen et al. 2018).

Process Intensification  167

Synthesizing sustainable biofuel and bioproducts are examples for green engineering. Integrating bioproducts production with the sustainable production and usage of biomass can lead to green engineering. For example, the integration of microalgae cultivation with CO2 sequestration and bioremediation of wastewater treatment has the potential for mitigation of environmental impacts associated with energy conversion and utilization (Guo et al. 2019). It is necessary to assess the ‘best’ and most sustainable approach for producing base chemicals from the most sustainable biomass, while not detracting from the availability of sufficient food/feed worldwide. To combine separation and conversion to reduce costs and waste streams, one needs to study insolubility, energy, and separations to derive design rules/constraints. Desired and anticipated biomass conversion technologies are described in the following subsections. Biomass conversion processes Biomass conversion processes include biological, chemical, biochemical and thermochemical processes, as seen in Figure 6.27. PI in biomass-based production routes leads to mono propylene glycol, an established bulk chemical, hydrogen, and base chemicals from amino acids. Lignocellulosic biomass can be a feedstock to produce platform molecules of isosorbide and hydroxymethyl furfural. For sustainability, the production of biomass should utilize as little as possible of scarce resources such as fertile land, water, fertilizer, renewable and non-renewable energy. Biomass production also should contribute to the wellbeing of the local producers and avoid competition with food supplies. Small scales can help in making the agricultural sector more robust with less requirements to transport biomass. In biomass conversion, PI may allow for building of high-efficiency small plants (Sanders et al. 2012, Schuster 2016, Tusso-Pinzona et al. 2020). Further progress in PI for biomass conversion to chemicals has several challenges: • How to separate the main components from biomass at low capital and operational costs, with low energy consumption. • How to reduce the logistic costs of biomass or to process close to the origin of the biomass resource. • How to combine separation and conversion to reduce costs and waste streams. Figure 6.28 shows common biological, biochemical, and thermochemical conversion processes, subject to PI, for processing the first and second generations of biomass as feedstock. Improvements

Thermal conversion

Biological conversion Thermochemical conversion Biochemical conversion Products

• Combustion • Fermentation • Aeorobic digestion • Hydrolysis • Pyrolysis • Hydrothermal liquefaction • Steam methane reforming • Gasification • Transesterification • Depolymerization • Chemicals, heat, electricity, biofuels, bio-oil

Figure 6.27.  Biomass conversion pathways with primary products.

168  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence First generation biofuel Sugar biomass sugarcane, sugar beet

Second generation biofuel

Starchy biomass corn, wheat

Lignocellulosic biomass corn stover, wood, grass

Biochemical proceses Sugar extraction

Starch hydrolysis acid, enzymatic

Cellulose, hemicellulose, hydrolysis

Thermochemical processes

C6 and C5 sugars Biochemical Fermentation

Gasification biosyngas Bioethanol synthesis

Ethanol separation

Ethanol

Liquid fuels

Pyrolysis bio-oil Liquid fuels

Liquefaction bio-oil

Figure 6.28.  Biochemical and thermochemical routes for the first and second generation of biofuels.

in these processes can have multiple impacts on the environment, the economy, and society. Some of the impacts include process and product development, and rural economic activity toward a bioeconomy as well as toward a circular economy. In addition, such processes lead to either recycling or fixing carbon captured by plants (Unlu et al. 2020). Microwave processing Microwave processing helps convert cellulosic biomass to chemicals, and liquid and solid fuels with efficient control of decomposition and with a limited number of reaction pathways. This requires rapid separation of products into continuous microwave processing. Some PI processes include microwave processing and supercritical extraction technology. Microwave pyrolysis of biomass in a dynamic CO2 atmosphere can continuously extract valuable chemical species (Aguilar-Reynosa et al. 2017, Salih and Baqi 2020). Supercritical fluid extraction Supercritical fluid extraction technology may prevent the use of solvents in chemical processing due to concerns over atmospheric damage, greenhouse gas accumulation, cost, and health and safety. Some acceptable solvents are water, bioethanol, and carbon dioxide. The supercritical state of CO2 offers a wider range of solvent strengths with different combinations of temperature and pressure and with the products free of solvent residues. Biomass as a source of chemicals can be used in supercritical fluid extraction for the extraction of waxes and other chemicals (Demirel 2018b). Green steam crackers Green steam crackers are important processes in the petrochemical industry and used to thermally break down naphtha to ethylene, propylene, butadienes, and benzene-toluene-xylenes (BTXs) at around 850°C. These small compounds are later converted into rubber, other polymers, and chemicals. Crackers are large, complex and energy intensive, with multiple furnaces using mostly fossil fuels.

Process Intensification  169

Hence, they are a major source of carbon dioxide. New furnaces using electricity of high current and low voltage generated from renewable resources have been shown to improve ethylene yields by 35% and to90%, reducecompared CO2 emissions 90%, compared with conventional systems (Ondrey 2020b). with by conventional systems (Ondrey 2020b).

6.8.2 Fermentation 6.8.2 Fermentation Fermentation is a natural and anaerobic conversion process that has been known and used for a long produce various outputs including cheese, wine, beer 6.29). (Figure 6.29 time totoproduce various outputs including cheese, wine, and beerand (Figure In fermentation an converts a carbohydrate as starch starchororsugar sugar to alcohol an An acid. An organism, organism converts a carbohydratesuch such as to alcohol or anoracid. organism, yeast for example, performs fermentation for its energy needs by converting sugar feedstock into alcohol. bacteriabyconverts into lactic et al. et2019). Fermentation bacteria carbohydrates converts carbohydrates intoacid lactic(Bobak acid (Bobak al. 2019). Ethanol 2.8 US gallon

Fermentation Biomass

Yeast

Carbon dioxide

(milled corn, 47.6 lbs)

17.6 lb

Sugars

Dried distillers grain and solubles, 16.5 lb Figure 6.29.  Fermentation of corn and its products.

Ethanol fermentation In ethanol fermentation, glucose is converted to ethanol and carbon dioxide by yeast and bacteria, Ethanol fermentation according to the following reaction:

following C6H12to O6 the (glucose) → 2Creaction: 2H5OH (ethanol) + 2CO2 (6.38) The glucose fermentation process produces ethanol, carbon dioxide, and dried distillers grain C6H 12O6 2 and solubles. Lactic acid fermentation The pyruvate molecules produced during glucose metabolism may be converted into lactic acid, Lactic acid fermentation according to the following reaction: C6H12to O6 the (glucose) → 2CH following reaction: 3CHOHCOOH (lactic acid)

(6.39)

In yogurt production, lactic acid fermentation converts lactose into lactic acid:

C6H12O6

C12H22O11 (lactose) + H2O → 4CH3CHOHCOOH (lactic acid)

(6.40)

6.8.3  Anaerobic Digestion

C12Hdigestion + H2O biogas 4CH3using CHOHCOOH (lacticthat acid) Anaerobic (AD) produces microorganisms break down biodegradable 22O11 (lactose) biomass in the absence of oxygen. Acidogenic bacteria then convert the sugars and amino acids into carbon dioxide, hydrogen, ammonia, and organic acids. Next, acetogenic bacteria convert 6.8.3 Anaerobic Digestion these organic acids into acetic acid, along with additional ammonia, hydrogen, and carbon dioxide. Finally, theseAcidogenic products to bacteria methane and CO2 (Demirel 2018b, 2021). inmethanogens the absence convert of oxygen. Four key biological and chemical stages of hydrogen, ammonia, and organic acid anaerobic digestion, as shown in Figure 6.30, are as follows: hydrolysis, acidogenesis, acetogenesis, and methanogenesis. A simplified representative reaction for the overall processes is: C6H12O6 → 3CO2 + 3CH4 (6.41)

0

170  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence Carbohydrates Fats Carbohydrates Proteins Fats Proteins

Sugars Fatty acids Sugars Amino acids Fatty acids Amino acids

Carbonic acids and alcohols Hydrogen Carbonic acids and alcohols Carbon dioxide Hydrogen Ammonia Carbon dioxide Ammonia

Hydrogen Acetic acids Hydrogen Carbon Acetic acids dioxide Carbon dioxide

Biogas Methane Biogas Carbon Methane dioxide Carbon dioxide

Hydrolysis→Acidogenesis→Acetogenesis→Methanogenesis Figure 6.30.  Schematic of anaerobic digestion, showing its key biological and chemical stages and material flows.

Operational temperature levels for AD processes are determined by the species of the methanogens: flows. • Mesophilic, which takes place optimally around 30–38°C or at ambient temperatures between 20–45°C, where mesophiles are the primary microorganisms present. • Thermophilic, which takes place optimally around 49–57°C at elevated temperatures up to 70°C, where thermophiles are the primary microorganisms present. Table 6.11 shows a typical composition of biogas. The electricity produced by anaerobic digesters is predominantly renewable energy as it does not contribute to increasing atmospheric CO2 concentrations. The digestate is nutrient-rich and can be used as fertilizer. AD can be integrated into other conversion processes to improve their sustainability. As part of an integrated waste management system, AD reduces the emission of landfill gas to the atmosphere. Anaerobic digestion facilities are less capital intensive than large power plants. Utilizing anaerobic digestion technologies can lead to various benefits (Abanades et al. 2022):

• • • •

Replacing fossil fuels Reducing or eliminating the energy footprint of waste treatment plants Reducing methane emissions from landfills Displacing industrially produced chemical fertilizers Table 6.11.  Typical composition of biogas. Constituent

% by volume

Methane, CH4

50–75

Carbon dioxide, CO2

25–50

Nitrogen, N2

0–10

Hydrogen, H2

0–1

Hydrogen sulfide, H2S

0–3

Oxygen, O2

0–2

Natural gas pyrolysis This process pyrolyzes natural gas into hydrogen and solid carbon (carbon black) without combustion to replace steam-methane reforming (SMR) for hydrogen generation. The H2 purity is typically suitable to the various requirements of numerous applications, and the pyrolysis reaction can manage the contaminants in natural gas. Capital and operating costs for the process can be lower than those for SMR, which is important for competitive purposes, when an effective cost of $35–60/ton of CO2 is assigned in a cap-and-trade or carbon-tax scheme (Jenkins 2021).

Process Intensification  171

Propylene carbonate and polypropylene carbonate production Cyclic propylene carbonate (PC) is an intermediate in the production of drugs and pesticides and is frequently used in lithium batteries. The reaction between propylene oxide (PO), which is a reactive epoxide, and CO2 requires expensive salen or ionic liquid catalysts, 1-n-ethyl-3-methylimidazolium, at 212°F and 114.7 psi. The CO2 from a bioethanol plant is compressed to 114.7 psia. Poly(propylene) carbonate (PPC) is soluble in polar solvents, a biologically degradable polymer, and a substitute for thermoplastic polymers (Demirel 2015). Table 6.12 shows sutainability metrics for propylene carbonate and polypropylene carbonate production. Cellulose/KI is a very active, selective, stable, and recyclable catalyst for the cycloaddition reactions of CO2 and epoxides. A highly efficient method for the synthesis of cyclic carbonates from carbon dioxide and epoxides uses polymer-supported quaternary onium salts (PS-Q+X−) and an aqueous solution of metal salts under mild conditions. Immobilized tributylmethyl ammonium chloride (PS-TBMAC) is a recyclable, effective catalyst. The presence of an aqueous solution of zinc iodide as a co-catalyst significantly enhances the catalytic activity of PS-TBMAC. Using this two-component catalyst system (polymer-supported quaternary onium salts and aqueous solution of metal salts), the reaction of CO2 with selected epoxides can be conducted with high product yield (71–91%) and selectivity (97–99%) under mild reaction conditions (temperature 110°C and initial CO2 pressure 0.9 MPa) (Bomgardner 2014, He et al. 2014). Table 6.12.  Sustainability metrics for propylene carbonate and polypropylene carbonate production (Demirel 2015). Parameter

PC

Total heating duty, Btu/hr

PPC

2.67 × 10

6

2.92 × 106

Total cooling duty, Btu/hr

1.58 × 10

7

4.20 × 107

Total heating cost, $/hr

40.9

231.70

Total cooling cost, $/hr

3.5

9.4

Net used CO2, lb/hr

–9976.8

–14523.2

Utility produced CO2, lb/hr

2587.8

4172.6

Reduced CO2, lb/hr

–7389.00

–10350.6

* US-EPA-Rule-E9-5711; natural gas

Carboxylation of glycerol production Direct carboxylation of bioglycerol and CO2 is the simplest route for synthesis of glycerol carbonate at 80°C and 3.5 MPa using 1 mol% of n-dibutyltin(IV)oxide (n-Bu2SnO) as the catalyst and methanol as the solvent. However, direct carboxylation of glycerol and CO2 is thermodynamically limited. So, indirect use of CO2 by another reaction may be considered (Nguyen and Demirel 2013). The synthesis of glycerol carbonate from bioglycerol and CO2 can be represented as follows: Catalyst

Bioglycerol + CO 2  → Bioglycerol carbonate + Water (6.42)

Ionic liquids Chloroaluminate ionic liquids have strong acid properties and may replace the hydrofluoric and sulfuric acids used in alkylation, where alkylation reacts isobutane with olefins like butene and propylene to create octane and other fuels. Ionic liquid salts are made by pairing organic cations with organic or inorganic anions. The strong acidity means that only about 3–6% of the contents of the alkylation reactor is an ionic liquid, compared with 50% for conventional acids. This significantly reduces the handling and volume requirements in the reactor. Less ionic liquid is consumed in alkylation than hydrofluoric or sulfuric acid is in conventional processes. The ionic liquid is also safer to handle and cheaper to regenerate on-site (He et al. 2014, Tullo 2021a).

172  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence Ionic liquid catalysts include tetrabutylammonium bromide, functionalized and simple imidazolium ionic liquids. A plethora of supported ionic liquid systems can be used for propylene carbonate production from carbon dioxide and epoxides. Metal ions used in combination with ionic liquids, particularly ZnBr2, can enhance conversions, and hydroxyl, carboxyl, and other functional groups capable of hydrogen-bonding can be incorporated to improve catalysis (He et al. 2014). Production of caprolactam The production of caprolactam is a two-step process. First, N2O is broken down into N2 and O2 using a regenerative thermal oxidation process at 1,000°C. Then, the oxides of nitrogen (NOx) react with ammonia in a selective catalytic reduction unit operating at 250–450°C to produce N2 and water. The two-step process has an overall 90% efficiency. The plant can integrate heat with a specific ceramic heat exchanger that captures and stores the heat used in the thermal oxidation process. When the heat exchangers have stored the heat from the clean gas, the process flow changes direction, and the heat exchangers preheat the incoming exhaust gas. This change of direction then takes place recurrently and reduces external energy usage in the process (Ondrey 2021b). The production of caprolactam results in emissions of approximately 500 mt of nitrous oxide (N2O) per year and leads to a climate impact of 150,000 mt/yr of CO2e. Separation of xylene isomers The separation of xylene’s isomers is an energy intensive process due to the isomers’ overlapping identical molecular weights, close boiling points and similar structures. Xylene isomers are usually derived from the catalytic reforming of crude oil, and require distillation, fractional crystallization and adsorption at high-temperature and high-pressure for separation. In a new process, the unique properties of cucurbiturils are used to separate the isomers. Cucurbiturils are organic macrocyclic molecules made of glycoluril monomers linked by methylene bridges. They have a hydrophobic central cavity able to hold smaller molecules. An aqueous solution of cucurbituril has a strong binding affinity with xylene isomers in water. Using liquid-liquid extraction at room temperature and pressure, the hole in the middle of cucurbituril can selectively host o-xylene from mixtures of xylene isomers and one extraction cycle can separate o-xylene with a selectivity of 92% (Ondrey 2021c). Switchable solvents Switchable solvents can reversibly switch on and off polar/apolar, volatile/non-volatile, and protic/aprotic properties. As a practical application, an equimolar mixture of DBU (1,8-diazabicyclo-[5.4.0]-undec-7-ene) and an alcohol, for example, behaves as a slightly polar solvent, like chloroform, enabling to dissolve apolar compounds such as hydrocarbons, whereas the salt DBU alkylcarbonate, after CO2 treatment, is a polar liquid, very similar to dimethylformamide and immiscible with hydrocarbons (Berglund et al. 2017). Oxidative dehydrogenation of propane Oxidative dehydrogenation (OD) of propane with CO2 for propylene production is a CO2 utilization process. However, the process is highly endothermic, usually leading to considerable CO2 emissions. Therefore, a novel eco-friendly propylene production that integrates ODP−CO2 with chemical looping combustion (CLC) is advantageous, as it provides the dual benefits of inherent CO2 capture as well as its utilization. A rigorous sensitivity analysis helps optimize the operating temperature, pressure and molar flow rates of the oxygen carriers, CO2 and propane, resulting in a maximum propylene yield of 79.1% with a net thermal efficiency of 73.5% (Ruthwika et al. 2020, Ghasemi et al. 2022). Oxidative dehydrogenation of ethane The catalyst FeCrOx/C for OD of ethane with carbon dioxide to ethylene at temperatures of 600–700°C undergoes in situ regeneration with CO2 to give a stoichiometric amount of CO by

Process Intensification  173

the Boudoir–Bell reaction. The heterogeneous catalytic OD of light hydrocarbons is a highly energy efficient process that is an alternative to the industrial production of olefins by pyrolysis of liquid petroleum distillates. The use of CO2 as a reagent in the OD of hydrocarbons addresses process safety issues and, in part, the CO2 utilization problem. However, dehydrogenation involving CO2 occurs at temperatures above 600°C where complete dehydrogenation results in coking and deactivation of the catalyst surface. Chromium oxide-based systems have been used as catalysts for the OD of ethane with carbon dioxide. The addition of Fe, Co and Mn oxides markedly improves the selectivity for ethylene in the OD of ethane process. Catalyst reoxidation becomes possible due to the involvement of CO2, which is active at the high temperature of the Boudoir–Bell reaction. In situ CO2 reactivation of the FeCrOx/C catalyst is possible in the OD of ethane to ethylene (Mishanin et al. 2018, 2020). A conceptual design has been proposed of an integrated renewable production system for ETBE from ethanol and i-butene after switchgrass pretreatment with dilute acid, and ammonia fiber explosion (AFEX). Only glucose is fermented to produce i-butene, while xylose is fermented to ethanol. Ethanol is purified using a multi effect column followed by molecular sieves and a PSA-membrane system is used to recover the i-butene. Dilute acid is the selected pretreatment due to the largest yield of sugars and the possibility of adjusting the production of both i-butene and ethanol for the needs of ETBE (Galana et al. 2019). Better methane reforming For sustainable routes to syngas, better methane reforming plays a critical role. That should permit the process to become greener via improvements in reformer design, heat integration and the catalyst. Methane-based syngas has a lower carbon footprint and is more cost competitive than coal-based syngas. Applications of reforming methane to syngas are growing to produce ammonia, methanol, dimethyl ether, and many other chemicals. Four technologies for methane reforming to syngas are: steam-methane reforming (SMR) at around 900°C, heat exchange reforming, autothermal reforming, and partial oxidation. SMR accounts for about 50% of the H2 produced globally, in an array of catalyst-filled tubular reactors through an endothermic reaction. High pressure steam is usually produced as a by-product of steam reformer plants. In modern reforming processes, efficiency is increased at lower emissions and steam generation, mainly because recovered steam from excess heat is reused in the reformer tubes with heat integration, where high pressure steam is produced by the cooling process gas from the reformer. The gas cooler has an integrated bypass system and is maintenance free; the steam pressure and degree of superheating can be adjusted as required. This furnace size and reactor size are reduced, and hence the annual OPEX is reduced. For large capacity with low production cost and low emissions of methanol, autothermal methane reforming (ATR) and distillation processes can be utilized. ATR uses pure oxygen to partially oxidize methane to syngas at around 1000°C in an exothermic process. An ATR scales up the diameter of the reactor and hence CAPEX is lower than that of SMR for large systems. Methane dry reforming with CO2 produces a CO-rich syngas (CO:H2 – 1:1), which is optimal for producing DME, and is more economic and safer with a considerably lower carbon footprint. Another way of reducing carbon emissions is to use renewable electricity for heating with nearly constant heat flux and keeping the gas mixture at near equilibrium and hence better utilizing the reactor volume. Electric furnace technology can also be used for energy efficient methane pyrolysis of H2 and carbon (Ondrey 2021c,d).

6.9  Energy Technology and Management Affordable energy supplies have often been the central driver for improved social and economic well-being. Harnessing energy efficiently, using sustainable energy carriers, reducing environmental impact, and improving socioeconomic acceptability may lead to energy sustainability. This requires community involvement and social acceptability, economic affordability and equity, good lifestyles,

174  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence appropriate land use and an understanding of the importance of aesthetics. Globally, renewable energy is the fastest-growing energy source although it is expected that fossil fuels will still account for 78% of energy use until 2040 (IRENA 2020). Natural gas is the fastest-growing fossil fuel. Energy conservation is of great importance in sustainability, in economic development, and living standards. When choosing energy resources and associated technologies for energy production, storage, and use, it is important to account for sustainability. For example, it is important to support national grids with renewable resources (Demirel 2018a, Yamaguchi 2021). Energy analysis consists of (1) acquiring energy services and their costs, and (2) analyzing data to identify energy management (EM) measures to reduce energy use and cost, often while increasing efficiency and mitigating environmental impact. Based on the cost and operation type, one needs to prioritize the EM measures. In analyzing energy cost, it is important to consider avoided cost, demand, and power factor, which is the ratio of real power (kW) to apparent power kilovolt-amps (kVA). Depending on the accuracy sought, sometimes it is adequate to consider seasonal averages, while at other times it is important to use finer time intervals (Demirel 2021, Dincer and Rosen 2021a).

6.9.1  Energy Technology Energy technology is involved energy production, conversion, conservation, and storage to provide industry and society with secure, affordable, and sustainable energy services. Below are some case studies with improvements in the energy technologies. Cement production Cement production accounts for about 8% of global CO2 emissions. Approximately 90% of these emissions come from the production of clinker used as the binder in ordinary Portland cement (PC). PC relies on the use of calcium carbonate (limestone), which is calcinated at high temperatures in a cement kiln to produce lime (CaO), along with a release of CO2 from the decarbonation of limestone. As an alternative, magnesia cements absorb CO2 from the atmosphere at ambient temperatures at a rate of 21.6 lb/yr per ton over their lifetimes (Kara et al. 2021). Ammonia as fuel Ammonia can serve as a fuel with potential for decarbonizing the energy sector. This is particularly true in the marine market, where a two-stroke, ammonia-fueled engine can be used for maritime shipping. Ammonia is of interest because it is an energy carrier that does not contain carbon, and whose combustion therefore does not produce CO2. Similarly, its production from electricity does not require a carbon-based source, while its production is largely scalable. Large quantities of ammonia can be transported and stored with well-established technology and infrastructure. Thus, using ammonia to power ships could be a step for decarbonization (Bailey 2020a). Advanced biofuels Advanced biofuels in a refinery support the circular economy as a tool for efficient resource use and emissions reduction. Advanced biofuels can include a hydrogen plant for new hydrotreatment. As the global focus on CO2 emissions intensifies, petroleum refineries are reassessing producing renewable diesel to reduce CO2 emissions. For example, producing 650,000–950,000 cubic meters of renewable diesel annually can avoid emissions of 1.7 million tons CO2 annually (Demirel 2018b). In a circular economy model, the reuse of carbon in agriculture for accelerated crop production may accelerate the natural photosynthesis process and increase farm efficiency by reducing water and soil utilization per unit mass of vegetable production. This approach can potentially offset 700,000 metric tons of CO2 emissions and achieve 100% decarbonization of the plant (Demirel 2018b).

Process Intensification  175

Carbon capture and utilization systems simulator CCUS is an important component in the transition to a low-carbon economy. Developing and growing the biopolymers and biochemicals markets can lead to a sustainable circular bioeconomy. This system uses renewable hydrocarbons produced from biomass, such as waste and residue oils and fats. Nitrogen restricts CO2 capture from diluted effluent streams by solvents, such as amines, which require a substantial amount of energy for recovery and reuse. The cost of CO2 sequestration with chemical-looping may be small (around $30–50/tonne carbon) compared to the cost of separating CO2 from typical flue gases (around $100–200/tonne carbon). Some disadvantages of chemical looping include dual reactor operation, oxygen carrier circulation between the reactors, and solids handling. Also, particle separation at high pressure and temperature may be difficult. Fluidized bed reactors provide excellent gas/solid mixing and operate at lower temperatures (around 800–900°C), reducing nitrogen oxide (NOx) emissions. Fluidized bed reactors offer short residence time, low char/tar content, and reduced ash-related problems. In solvent-based carbon capture system, a CO2-rich exhaust gas contacts gravity-driven solvents in packed absorber towers. The solvent is recovered in a distillation column and recycled back to the absorber column. This conventional process, although mature, is not environmentally friendly and usually not economically feasible, mainly due to use of expensive solvents and its high values of CAPEX and OPEX. With process intensification, a carbon capture system can be enhanced using, for example, a rotating horizontal packed bed employing centrifugal force to enhance contact between the gas and the liquid. The high gravitational forces created by the rotation improves mass transfer and helps decrease the size of the units, leading to lower CAPEX and 45% energy savings compared with conventional carbon capture by solvent technology using monoethanolamine. By using highly concentrated solvents, the energy required for regeneration of the solvent by stripping CO2 declines. Solvent is pumped into the center of the rotating cylinder, and the centrifugal force from the rotation pushes the solvent outward through the packed-bed gas-liquid contactor. The gas containing CO2 enters the exterior of the cylinder against the flow of solvent. The system can reduce the levelized carbon-capture cost toward the target of $30/ton or less. The rotating packed-bed intensification system could be applied to other processes with gas-liquid contact (Ondrey 2019b). Steam and condensate-return systems can be susceptible to unique forms of corrosion. Often steam is used for process heating and most of the steam condensate is returned to boilers. Depending on the process in which the steam is used, the condensate may have impurities such as acidic compounds and organics, and mineral salts that cause corrosion and failures of piping and other equipment like steam generators. High pressure steam generators require high purity makeup water. A popular makeup treatment for boiler units operating under 300 psi is basic sodium zeolite softening to exchange mainly calcium and magnesium for sodium, minimizing the formation common scale deposit, which is mostly calcium carbonate. The reaction for scale formation follows: Ca2+ + 2HCO3– + heat → CaCO3 + CO2 + H2O (6.43) However, on reaching the bicarbonate alkalinity, 2HCO3– in large quantity is converted to carbon dioxide, according to the following reactions: 2HCO3– + heat → CO32– + CO2 + H2O (6.44) HCO3– + heat → CO2 + OH– (6.45) The combined reactions above may attain 90% conversion of CO2. When CO2 flashes off with the steam and dissolves in the condensate, acidity rises and hence significant carbon steel corrosion can occur, according to the following: CO2 + H2O = H2CO3– = 2H+ + CO32– (6.46)

176  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence For example, 3 ppm CO2 in steam condensate will lower the pH to 5.26. Dissolved oxygen, if any, would increase the corrosion. Often makeup water is upgraded to remove or capture alkalinity with a sodium softener and strong acid cation exchanger. However, this process will not remove other impurities, e.g., chloride, sulfate or silica. These impurities need to be controlled by blowdown when concentrated in the boiler. Reverse osmosis (RO) is also an effective method for makeup water treatment; a single pass RO unit can remove 99% of impurities (Buecker and Ondrey 2019). For carbon steel, dissolved oxygen and acids are the most corrosive agents, while stainless steel is the best unless mineral salt anions like chlorides are not present. Dissolved oxygen and ammonia can be harmful for copper-alloy tubes. Film forming products to protect the surface are also promising alternatives. Air cooled condensers (ACCs) are replacing cooling towers to reduce water usage. However, ACC units require high levels of carbon-steel piping where a large amount of iron oxide particulates are observed in the condensate. If iron oxide is not removed before the steam generator, it accumulates on boiler tubes and reduces heat transfer and leads to corrosion. Monitoring the condensate return chemistry may include pH, specific and cation conductivity, and sodium and iron or Millipore testing. Such testing is not only vital for the power sector but also for other industries. Precision alignment to reduce energy costs Energy costs can be a major part of expenses (87%), in addition to maintenance (8%) and purchase (5%) costs over the service life of equipment such as pumps and compressors, where the values given are examples for typical processes. Some devices are positioned together and aligned. Typically, alignment positions two or more machines to transfer power from one to another, such as motor and pump combination where their shafts are colinear and their axes of rotation turn precisely together along a single unified line. This may be achieved by precision laser alignment of equipment. With proper alignment, the rotational power is efficiently transferred from shaft to shaft, reducing energy costs by around 10% and increasing the service life of the equipment (Matthews 2014). Cost estimation and risk assessment Risk is an uncertainty or condition affecting a project objective or business goal. Cost risk in budget estimates of a project may be critical. Inadequate definition of scope may be the main driver of cost uncertainty, in addition to level of complexity and execution strategy. A cost estimator must relate the project risks to the range of possible consequences. However, optimism bias is common for decision makers and creates pressure for lower cost estimates, while cost over-runs may lead to conservative and inaccurate estimates (Demirel 2021).

6.9.2  Energy Management Effective energy management (EM) includes numerous steps: management commitment, data analysis, analysis of energy conservation options, implementation of energy conservation options, and continued feedback and analysis. EM provides organizations with the information, tools, and assistance to reduce energy, water, and nonrenewable energy use, as well as GHG and other emissions. This helps prepare sustainability/energy score cards and offers expertise in project and policy implementation to the reduce energy intensity of operations (Golušin et al. 2010, Dincer et al. 2017, Demirel 2018a). Internet of things (IoT)‑based energy management Internet of things (IoT)-based energy management with edge controllers can provide a better match between renewable energy outputs with electricity requirements. Renewable energy systems, including biomass, geothermal, solar, and wind, usually generate small and intermittent power, often operating remotely, leading to grid instability in matching output to demand. Renewable power tends to depend on environmental conditions and becomes somewhat unpredictable and

Process Intensification  177

difficult to control. Local power storage can help match renewable output to grid demand. However, power storage may be expensive, even though battery storage costs are becoming affordable. This is allowing battery technology to applied more widely, and will be even more prevalent if battery degradation and disposal issues are addressed properly. Therefore, close coordination is necessary among renewable power plants, the grid, and power storage to match outputs with demands (Bailey 2019, Custeau 2019, Farsi and Rosen 2022a, b, Yamaguchi 2021).

6.10  Process Safety Process safety has a high priority in process industries to prevent accidents by avoiding or minimizing risks, rather than by only placing protective layers. Process safety therefore involves: (i) minimization of hazardous inventories, (ii) substitution of hazardous materials or conditions, (iii) moderation of the strength of an effect, and (iv) simplification to reduce sources for errors. PI, with the creation of smaller processes, reduces the inventory of dangerous substances and the consequences of a hazardous process failure. This often involves several aspects: (i) standardized measurement approach to quantify safety performance for existing chemicals, operating conditions, and process equipment; (ii) incorporation of proper safety metrics into process models; and (iii) revision and improvement in design for enhanced safety (Castillo-Landero et al. 2019). New extended metrics include safety (and sustainability) as a relevant performance criterion. A multi-criteria decision matrix used for processes often contains safety and sustainability. For example, a risk analysis approach compares the cost and safety performance of azeotropic distillation versus extractive distillation for bioethanol dehydration, and the former is seen to outperform from both economic and safety perspectives. Here, risk assessment was incorporated as a constraint in the synthesis model and in the superstructure-based optimization process. Several industrial software programs have been developed to help companies achieve safety compliance. Process hazards The hazardous process stream index helps identify hazards in the early stages of a process design. This can lead, for instance, to efforts to intensify biofuels and bioproducts, which involve handling flammable aqueous mixtures and combustible materials. This index uses simulation modeling and public databases to calculate the hazards of every process stream using as input data pressure, molar flow, density, heat of combustion, and flash point (Lopez-Molina et al. 2020). When retrofitting a process, such as a storage for new hazardous materials, new potential fire and safety hazards may arise. Structural fires are not uncommon and cause deaths, injuries, and direct property damage. Some of the top fire and life safety hazards are flammable and combustible liquids (alcohols, gasoline, oil derivatives), dust hazards, and group A plastics that have a high burning rate and high heat of combustion, such as polyethylene terephthalate (PET), natural rubber, polypropylene, acrylic, and polyvinylchloride. Hazard and operability (HAZOP) studies or what-if analyses can be effective in preventing various problems and hazards. To mitigate safety risks, fire sprinkler systems and proper fire walls with necessary ventilation are required, and these must be accompanied by regular testing, inspections, and maintenance, which includes testing of fire alarms, smoke and heat detectors, and fire exits. It is important to operate in compliance with international codes and national standards for the safety of people and processes (Pearson 2020). Process safety in industrial and manufacturing sectors Work in the chemical process industry (CPI) has the potential for disaster if employee training on process safety is inadequate. Effective process safety programs identify and assess hazards and manage them with metrics, so that process performance and safety are tied together. A question that guides leading metrics may be: “Are process safety systems working the way they should be on a day-to-day basis?” Answering this question can help identify riskier parts of a process and facilitate brainstorming of ways to inform personnel of this risk and to mitigate it. A safety program should be

178  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence carried out in conjunction with production objectives: (i) to prevent serious incidents relating to the process and (ii) to ensure that personnel have the proper leading and lagging statistics to assess the safety program’s effectiveness and predict all risks, with management oversight and review. While companies typically have some specific concerns, they should also adhere to industry standards to at least meet minimum requirements (Klein 2019, 2020, Pearson 2020). The current safety performance level may be classified as poor, average, or high for a company. Deviations from this performance level, whether positive or negative, can be described as follows: • Entropic: This is a gradual decrease in performance over time. Major causes of this performance decrease are a lack of metrics indicating the need for assessment, or a higher priority work leading to neglect of a particular process component. • Systematic: These performance levels operate with continuous and organized maintenance schedules. If a company is not operating around this performance level, this is a strong indication that management issues are not addressing essential plant needs. • Catalytic and anti‑catalytic: Events like these either lead to quick improvements or rapid deterioration of a process. Some causes of these events are a safety near-miss, new management, new company mergers where company cultures are very different, market conditions, new regulations, audits that cause actionable items to improve, and lawsuits that require a complete renovation of a process. • Traumatic and catastrophic: These events are caused by rapid changes in performance usually due to changes in a specific facility, company, or a related industry (along the supply chain). Facility incidents may lead to significantly decreased performance and sometimes closure, enabling other plants within a company to focus on better process safety in case of a similar event. In traumatic events, there is an opportunity for rapid performance correction where a catastrophic incident would require complete process redesign. Adopting a baseline of company performance helps assess how to reach the desired level through process-safety programs, annual program goals, monitoring systems, and review systems in line with regulatory agencies (Ricardo et al. 2020). Hydrogen fires Hydrogen has many uses including producing polymers, fertilizers, high-quality gasoline and should have safety regulations and sustainability goals. Hydrogen is non-toxic but has a flammability rating of “4” on the NFPA scale, which means that it requires safe handling because of its ability to ignite spontaneously with a flame that is invisible to the human eye. Hydrogen flames also emit less infrared radiation, meaning that workers do not feel such a flame’s heat. With this hazard, new sensing systems are needed to protect workers in chemical plants and to prevent disastrous outcomes (Hosch and Paterson 2020). One type of detector that is compatible with H2 is the catalytic bead sensor, which attracts a combustible gas that reacts with oxygen. This reaction occurs on one of two resistors wrapped with platinum wire and covered with a catalytic bead. However, only one contains the treated catalytic bead that will react with the H2 gas. When a gas like H2 is present, it becomes oxidized on the treated catalytic bead and releases heat, changing the resistance of one bead while that of the other remains the same. By comparing the difference in resistance between a treated and untreated resistor, operators can determine leak concentrations in a closed system such as a flame-proof sinter. However, the sensor can fail due to clogs and show a resistance comparable to the untreated catalytic bead, so regular inspections and testing are needed (Hosch and Paterson 2020). Hydrogen safety Flame sensors should also be present in a plant in case H2 gas ignites in an unwanted manner or location. A thermal heat detector can be used and does not provide an alert until the temperature

Process Intensification  179

reaches a predetermined threshold. This threshold makes the placement of these detectors important, and they should occupy a space most likely to encounter a H2 flame. An optical detector is another option and can detect the UV spectrum emitted by H2 flames. UV detectors work very well but can be sensitive to other UV emitting processes such as welding, lightning, and arcing equipment, and thus may cause unnecessary plant shutdowns. A multi-spectrum infrared (MIR) system can be useful and can have setpoints to detect H2 flames specifically. This type of “smart sensor” does not sound an alarm for other UV sources (Hosch and Paterson 2020). Hydrogen is quite flammable and reactive, but nevertheless is widely used in chemical process industries, including in the production of polymers, reforming processes in the refining sector, removing sulfur from petroleum, and ammonia production. A H2 flame cannot be easily detected by human senses and workers cannot see it and feel the immense heat, as H2 flames typically emit little infrared radiation and little visible light. Therefore, plants that use H2 need fire and gas safety systems with flame and gas detection equipment that uss IR and catalytic beads as these can provide early warnings and protect people. Other options for H2-flame detection are the thermal heat detector and an optical detector that can detect ultraviolet (UV) radiation emitted by a hydrogen flame, as well as multi-spectrum infrared flame detection. Any detection system should be able to communicate with the process control system effectively to mitigate dangers in advance (Hosch and Peterson 2020). Improved safety Smaller plants have smaller contained volumes and, if all other risks remain equal, a reduced plant inventory typically increases the safety of a plant and the consequences of a safety incident. Sometimes smaller plants have greater reliability, as small-scale equipment may have fewer and simpler moving parts, reducing the risk of problems (Pearson 2020). For sufficiently small systems, the contained energy may be small enough that an explosion or reaction run away can be contained within the vessel without the need for relief valves. Increased safety may also provide the opportunity for the improvement of processes. Note that some hazardous processes using conventional equipment, such as nuclear reprocessing, are carried out at uneconomically low concentrations for safety reasons. A safety instrumented system uses top-quality field instrumentation or sensors as the primary enabling component that helps engineers design and maintain the established protocols and adhere to regulatory requirements. One of the standards commonly applied for designing and implementing of safety systems is International Electrotechnical Commission (IEC) 61511 (www.iec.ch). This protocol applies to many industrial sectors, including chemical, petroleum, petrochemical, pharmaceutical, pulp and paper, and power. A safety system is different from a control system and is the final layer of protection of a plant for the safety of people and the environment. Engineers need to consider that a safety system must meet the required risk-reduction levels and establish the correct probability of failure, known as safety integrity level (SIL) requirements. SIL results and the standards set in IEC 61511 can determine sensor choices. These sensors are the first devices to be exposed to and thus able to detect the unsafe conditions that can trigger safety shutdowns (Klein 2019).

6.11  Process Intensification and Sustainable Engineering Intensification is a key step for sustainable processing and manufacturing sectors as it incorporates engineering fundamentals, economics, and safety in design and operation. Through identification and implementation of needs in modeling, PI can lead to sustainable engineering practices being accepted and used by the processing and manufacturing sectors. For example, decomposition-based solutions identify, compare and screen PI options on a rigorous and quantitative basis. The methodology is generic and may be applied to any chemical and/or biochemical processes. Applications can be extended from the PI knowledge base and the model library for various industrial sectors (Lutze et al. 2012). A systematic computer aided hybrid model-based synthesis and design methodology

180  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence

Social

2D

3D Sustainable

Environment

2D

Economic 2D

Figure 6.31.  Targeting the larger triangle in environmental, economic, and societal dimensions for improved sustainability by process intensification.

incorporating PI may lead to sustainable processes based on available data. A hybrid synthesis method uses (a) thermodynamic insightsphenomenafor the analysis of the process; (b) knowledge for the identification In a bottom-up of potential PIsuch unit operations; and (c) mathematical programming to identify the best feasible PI as heati option from the set of potential PI unit operations (Figure 6.31). Thus, PI clearly can contribute significantly to sustainable engineering. As an example from biotechnology, it is pointed out that using intensified biorefinery technologies may be a primary goal. By integrating data and knowledge of molecular interactions, PI becomes a useful sustainability tool. The first step is to generate data through experimentation to develop models for new intensified equipment and processes. The next step is to develop software to provide PI retrofit solutions. For example, in biomass pyrolysis, the number of processing steps and the various biomass feedstocks et al. 2019, Bailey 2019,biomass 2020, Allen et al.and 2021). provide a goodBielenberg start to create a database of various substances their conversions, as well as to develop thermodynamic and physical models. This includes both product intermediates, Energy and industrial/manufacturing sectors waste, byproducts, and final products correlated with kinetic models (Chen et al. 2020). Process intensification, as a part of sustainability efforts with strategies regarding equipment, methods, and (plant) design, leads to: • Reduced costs of production, energy consumption, and waste • Improved product quality and reliability, and safety • More efficient use of raw materials, and easier scale-up Process intensification represents an important enabler of a shift towards sustainability through needs to its potential to address numerous relevant and important factors: • Water supply • Food production 2021). • Housing and shelter • Sanitation and waste management • Energy development • Transportation • Industrial processing • Development of natural resources

Process Intensification  181



• • • • • • •

Cleaning up polluted waste sites Reducing environmental and social impact Restoring natural environments such as forests, lakes, streams, and wetlands Providing medical care to those in need and others Minimizing and responsibly disposing of waste Improving industrial processes to eliminate waste and reduce consumption Recommending appropriate and innovative use of technology

As Figure 6.31 shows, PI efforts can improve sustainability, represented by the extended overlapping region of the social, environmental, and economic dimensions of sustainability. Research in PI moves toward minimizing equipment size and energy use, waste, cost, and emissions in the processing and manufacturing sectors, while maintaining production targets. PI research includes efforts at process optimization for minimizing costs and maximizing production, as well as process synthesis of the tools and techniques for improved production. PI aims to do the work of both process optimization and process synthesis in a full-scale process while incorporating environmental and safety concerns using a holistic approach. The main methods for process synthesis and PI are heuristics that are based on known and tested rules for plant design. Mathematical programming optimizes an entire process. Hybrid methods use the framework of a heuristic approach but also use thermodynamics to improve equipment and methods. In a bottom-up phenomena-based holistic approach, engineers organize processes into smaller components such as heating and cooling, mixing and reactions. After, the intensified component is modeled and optimized. Retrofit approaches are a heuristic method that analyzes a base case and seeks to improve it without additional capital investment and with integrated equipment. Other retrofit methodologies identify limiting steps and use a database to select a PI option with environmental and sustainability focuses. In a combined photoreactor with agitation process as a PI, for instance, light initiates the reaction, and mixing promotes the homogeneity of the product. There are now improved control technologies and when a certain mixing speed is not required, processors can adjust the parameters to save energy (LePree 2017, 2019b, Bielenberg et al. 2019, Bailey 2019, 2020b, Allen et al. 2021). Energy and industrial/manufacturing sectors Both energy and chemical sectors are focusing on more sustainable initiatives to decrease carbon emissions in the future. 35% of the energy sector finds sustainability extremely important, whereas 47% of the chemical manufacturing industry finds sustainability to be extremely important. However, the energy sectors focus on alternative energies, while the chemical sector focuses on recyclable products. This difference can be seen in chemical manufacturers’ efforts to provide recycled and biodegradable products and is considered a business plan as the demand for these products increases. These initiatives are the result of both governmental regulations and consumer demands. Sustainability needs to be reevaluated by those sectors to work towards a more desirable outcome. The 2015 UN Sustainable Development goals contain all aspects of sustainability, including economics, social, and environmental. There is more to sustainability than environmental and social aspects (Alhajji and Demirel 2015, 2016, Dincer et al. 2017, Demirel 2018a, 2021). Some barriers to sustainability There are some apparent barriers to meeting sustainability objectives. The biggest concern is that the workforce does not have the skills or is not afforded the resources. The next is that the capital investment needed is infeasible mainly because it is difficult to secure the investment. Another barrier is that assets are aging and cannot be effectively retrofitted to meet sustainability needs. The lesser barriers are that customers are not demanding a sustainable product and that corporate strategy is not based on, or inclusive of, sustainability initiatives (Rentschler and Shahani 2019).

182  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence Safety aspect It is widely acknowledged that process safety is a critical aspect of sustainability. A company cannot grow or improve if it does not have sufficient safety infrastructure; however, many plants do not have the money for capital improvements. Additionally, a skills gap arises as the average experience level is dropping and hazards are not efficiently recognized (Klein 2020). Digitalization and I4.0 Digitalization is trying to get ahead of this skills gap, with supply chain optimization, advanced process control, energy management, utility optimization, and predictive maintenance. Additionally, investments need to focus on digitalization and innovative, targeted investments to optimize processes and improve maintenance schedules (Sanders et al. 2016, Vaidya et al. 2018, LePree 2019b, Custeau 2019). Process intensification in carbon dioxide capture and conversion Possible strategies involve sequestering the emitted CO2 for long-term storage deep underground, and conversion of CO2 into value-added products. Conventional processes for each of these approaches often have high associated capital costs and kinetic limitations in different process steps. Additionally, CO2 is thermodynamically a very stable molecule and difficult to activate. Despite such challenges, several methods for CO2 capture and conversion have been investigated including absorption, photocatalysis, electrochemical and thermochemical methods. Conventional technologies employed in these processes often suffer from low selectivity and conversion and lack energy efficiency. Therefore, suitable PI techniques based on equipment, material and process development strategies can play a key role at enabling the deployment of these processes (Adamu et al. 2020). Table 6.13 shows some possible intensified processes for CO2 conversion. For example, in the case of CO2 reduction, electrochemical reduction can be incorporated with photocatalysis, which provides the driving force to initiate the process. Replacing an intensive energy source with a more efficient and ideally renewable source can lead to intensification of CO2 reduction. For such combinations or substitutions to be effective, it is important to understand the important properties in material and/or devices that will efficiently and affordably reduce CO2 to value-added products. With multifunctional units, such as membrane-integrated reactors, combining two functions into one unit should reduce the capital cost of the single unit compared to the individual reactor and membrane separation unit. However, this technology suffers from limitations which include operating under high pressure, high membrane cost, cathode flooding, fuel crossover, and membrane Table 6.13.  Conversion of carbon dioxide with possible process intensifications (Adamu et al. 2020). Process

Driving force

Main products

Process intensification technique

Photocatalytic reduction

Light

Methanol, carbon dioxide, methane, hydrogen

Photocatalytic modification Monolith reactor Microreactor

Electrochemical reduction

Electricity

Methanol, formic acid

Microfluidic devices Coupling with photocatalysis Gas diffuse electrode Membrane reactor

Thermochemical reduction

Heat

Carbon dioxide, carbon monoxide, hydrogen

Plasma Reactive coupling Membrane reactor Chemical looping

Hydrothermal liquefaction

Heat Pressure

Hydrocarbons, formic acid, hydrogen

Oxidation Reduction Chemical looping of zero valence metal

Process Intensification  183 Plasma

Membrane reactorboth chemical degradation in electrochemical systems. Membrane-based gas absorption integrates Chemical looping absorption and separation. A liquid mass transfer coefficient increases of 5 times when compared Hydrothermal to a conventional packed Heat column for CO2Hydrocarbons, absorption informic water at aOxidation superficial liquid velocity of liquefaction Pressure acid, hydrogen Reduction 1.25 cm/s has been reported; hence it is classified as a promising PI strategy (Adamu et al. 2020).

Chemical looping of zero valence metal

Plasma conversion of carbon dioxide Plasma conversion of carbon dioxide The high-cost thermal energy required for CO2 reduction through thermochemical routes can be Thebyhigh-cost thermal energygenerated required for CO2 electric discharge by high average energetic replaced cold plasma technology through electrons (1–10 eV) with an average temperature of 104–105 K while the gas temperature remains eV) with an average temperature of 104– near ambient (Figure 6.32). Compared to thermal plasma where operating temperatures can reach over 6.32 1000 K, non-thermal plasma is significantly more energy efficient and therefore more cost effective. Cold plasma enables quick start-up and shutdown cycles. An increase in CO2 hydrogenation by more start-up and shutdown cycles. An increase CO2Ohydrogenation by more than 30% than 30% is possible upon incorporating plasma within Ni/Al 2 3 catalyst. The electron temperature of incorporating plasma with Ni/Al O 2 3 plasma can be increased by reduction of the discharge gap upon addition of packing material. The reduction of the materials dischargewith gap upon ofconstant packing can material. The the addition of a packing addition of a packing a highaddition dielectric transform discharge nature because of a decrease in discharge gap. This can lead to a significant enhancement of discharge characteristics (Adamu et al. 2020).

Change of nature of reactants

Enhance energy efficiency Improve selectivity Activate Activatedthermal thermalcatalysis catalysis Adsorption on catalyst surface Plasma generation

Plasma charge on catalyst

Increase concentration of active particles Improve catalyst activity and durability

Figure 6.32.  Impact of plasma and catalyst on each other.

Summary Process intensification (PI) focuses on improvements in manufacturing and processing by remodeling existing operation so they are more precise and efficient. PI often involves combining Summary separate unit operations into a single piece of equipment, resulting in a more efficient, cleaner, and economical processes. At the detailed level, PI technologies often improve mass and heat transfer, reaction kinetics,into yields, and selectivity. These improvements reduce device numbers and/or sizes, operations a single piece facility footprints, process complexity, and costs and risks in industrial facilities. This chapter provides an understanding of anddevice ability to apply significant techniques for PI, selectivity. These improvements reduce numbers and/or sizes, describes and illustrates PI in the industrial and energy sectors, and explains the relation of PI and complexity, and costs and risks in industrial facilities. sustainability. Particular attention is paid to PI fundamentals and details, intensification methods and modeling, intensification in understanding units and plants, processes and bioproducts, chemical This chapter provides an ofbiochemical and ability to apply significant techniques for PI processes. Related topics are also described, including green engineering processes, energy illustrates PI in the industrial and energy sectors, and explains the relation of PI technology andismanagement, and process safety. Finally,intensification the relation between andmodeling, sustainable attention paid to PI fundamentals and details, methodsPIand engineering is discussed, bringing together the many covered inc this chapter and highlighting units and plants, biochemical processes andtopics bioproducts, the importance PI to sustainable engineering, sustainable development, and sustainability. described,ofincluding green engineering processes, The bottom line of this chapter is that PI can help achieve considerable improvements in manufacturing, processing and other areas, as well as enhancements to their sustainability, by any 69

184  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence means of modifications of existing operations or new designs so as to make them more productive, precise, efficient, economical, and safe. Thus, PI can not only play an important role in making or keeping the manufacturing and processing sectors competitive, it can also support and foster sustainable design, leading to systems and processes that support sustainable development in general and the environmental, economic, and societal dimensions of sustainability in particular. The various elements of PI, including improvement and/or optimization of equipment, methods and plants, can all support enhanced sustainable engineering as well as broader sustainability. The many examples of benefits derived from the application of PI support this statement, in such areas as separation and distillation, purification, mixing, petrochemical processes, chemicals production, heating and cooling, and heat transfer and evaporation.

Nomenclature A Availability AD Anaerobic digestion AI Artificial intelligence ATR Autothermal methane reforming BTX Benzene, toluene and xylene c . Specific cost of exergy C Cost rate CFCI Fixed capital investment COP. Operating cost CAPEX Capital expenditures CCUS Carbon capture, utilization and storage CGCC Column grand composite curve CLC Chemical-looping combustion CPI Chemical process industry CTT Column targeting CVD Chemical vapor deposition Da Damköhler number DMADV Define, Measure, Analyze, Design and Verify DMAIC Define, Measure, Analyze, Improve and Control DMC Dimethyl carbonate DME Dimethyl ether DMEDI Define, Measure, Explore, Design, and Implement DPMO Defects per million opportunities DSD Definitive screening design DWC Divided wall column EM Energy management ETBE Ethyl tertiary butyl ether . Ex Rate of exergy transfer f(x) Probability function Fa Value after the modifications leading to intensification Fb Current value G Gibbs energy GE Green engineering GHG Greenhouse gas H Enthalpy HAZOP Hazard and operability HCOOH Formic acid HENS Heat exchanger network system

Process Intensification  185

HIDiC Heat integrated distillation column HiGee High centrifugal force ICI Imperial Chemical Industries IF Intensification factor IIoT Industrial internet of things k Thermal conductivity LCL Lower critical level LHHW Langmuir-Hinshelwood Hougen-Watson kinetics formulations with fugacities MIR Multi-spectrum infrared ML Machine learning MLRD Multi-level reactor design NHx, min Minimum number of heat exchangers needed in a HENS design NFPA National Fire Prevention Association OC Oxygen carrier OPEX Operating expenditures p Potential intensification strategies PC Propylene carbonate PET Polyethylene terephthalate PFA Process flexibility analysis PI Process Intensification PLA Polylactic acid PO Propylene oxide POA Process operability analysis PPC Poly(propylene) carbonate PSA Pressure swing adsorption PSE Process systems engineering QC Condenser duty QR Reboiler duty RD Reactive distillation RDWC Reactive divide wall column RFR Reverse flow reactor S Entropy . Sprod Rate of entropy production SDR Spinning disc reactor SIS Safety instrumented system SMB Simulated moving bed SMR Steam methane reforming To Environmental temperature TY Throughput yield UCL Upper critical level UV Ultraviolet Wloss Lost available work Wm Minimum separation work x Critical to quality variable

Greek letters Θ Viscous dissipation function µ Viscosity σ Sigma

186  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence

Problems 6.1 Process intensification is used by some but not by others. Identify reasons for the reluctance to use process intensification some engineers in industry. 6.2 Some claim that the main reason process intensification is not used to a great extent by industry is that the results of process intensification are difficult to attain or difficult to interpret. Do you agree? Explain. 6.3 Search for a paper on the application of process intensification methods to a specific industry. List and describe the ways in which PI helps identify potential improvements in that industry. 6.4 Apply process intensification to a specific industry that has not been examined previously in the literature using process intensification. What are the improvements attained by applying PI? Describe them qualitatively and quantitatively. 6.5 Explain how process intensification is a useful tool in policy making related to sustainability. Provide examples. 6.6 Develop ideas on how process intensification concepts and methods can be usefully applied to enhance each of the sectors of the economy, including (a) transportation, (b) industry, (c) agriculture and forestry, (d) residential, commercial and institutional, (e) utility, and (f) buildings. 6.7 Select an existing or new engineering process or device or system, and analyze it using advanced methods for process intensification. Relevant factors should be considered, and can include performance, efficiency, resource (including energy) use, economics, environmental impact and sustainability. Utilize appropriate and reliable information sources, possibly including books, journals, conference proceedings, reports, direct communications with relevant experts, and the internet. 6.8 Design and analyze an improvement to a chemical process for producing benzene using process intensification. Consider in your work the resource requirements for the chemical process. The design of the improvement should involve at least conceptual system development and more a detailed design where possible. The analysis of the design improvement provides information that can be useful in evaluating its merits. You may wish to utilize some or all the following steps (or others): • Design the improvement to the chemical process for producing benzene. • Perform an analysis of the design improvement. In this step, you may wish to consider resource usage for the process and its subprocesses, and evaluate performance relevant parameters considering technical, economic, environmental and sustainability factors. • Compare the results with those for the original chemical process for producing benzene. • For important design parameters, perform a parametric study to show how the design improvement performance is affected by varying the operating conditions. • Discuss the results and findings, verify and validate the results, draw conclusions, and make recommendations (if any can be drawn from your results and conclusions). Throughout, make reasonable and appropriate assumptions and approximations, list properties, and data, utilized and/or assumed, and use relevant software to assist where appropriate and useful.

Process Intensification  187

Research Projects 1. Modularity means a change from discrete and batch operation to flexible, continuous, and standardized modules that combined satisfy production demands and needs. This principle enables multiple modules of each unit operation to satisfy the production demand with easy scale up. Articulate how the modularity may lead to sustainable production and business. 2. Decentralization creates the ability of a process to make localized data-driven decisions where different subsystems or operations coordinate independently of a specific decision center. Investigate how decentralization can lead to sustainbility.

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Chapter 7

Energy Analysis INTRODUCTION and OBJECTIVES This chapter focuses on topics such as energy production and conversion, energy conservation and efficiency, energy storage, energy analysis and advanced tools like exergy analysis. Coverage is also provided of broader energy-related topics, such as energy economics, the food energy water nexus, life cycle assessment of energy systems, and energy analysis and sustainability. Case studies are included to illustrate energy issues. The main objectives of the chapter are:

• • • • •

Energy production and conversion Energy conservation and efficiency Energy storage Energy analysis Exergy analysis and sustainability

7.1  Energy Production Energy exists in many forms and is convertible from one form to another. Energy production mainly involves converting one form of energy to a form that is needed. For example, the chemical energy in fossil fuels such as coal or natural gas can be used in steam power plants to produce electricity. Hydroelectric power is based on the kinetic energy of flowing water. Polygeneration is the production of more than one useful form of energy from the same energy source. Cogeneration is a polygeneration process in which two energy products are produced simultaneously known as cogeneration (such as process heat and electricity) (Haghghi et al. 2019). The world’s energy demand is expected to rise considerably in the future for several reasons. First, the global population is expected to rise to perhaps double its present level by the end of the century. Also, energy use is expected to rise dramatically in developing countries as they industrialize and raise standards of living (Dincer and Rosen 2021a). There are two main types of energy: renewable and nonrenewable. The characteristics of each follow: • Renewable energy encompasses energy received directly and indirectly from the sun (like hydraulic energy, wave energy, ocean thermal energy, wind energy, much biomass, and ambient geothermal energy). Renewable energy also includes energy derived from other natural forces (like tidal energy and geothermal energy from internal heat of the earth). • Nonrenewable energy includes nonrenewable energy resources as well as energy forms that do not exist naturally but are produced by people. The main types of renewable energy include fossil fuels (e.g., coal, oil, natural gas) and non-fossil fuels like uranium and fusion material.

Energy Analysis  195

7.1.1  Nonrenewable Energy Production A fossil fuel-based power plant produces electricity by converting coal, petroleum, or natural gas and integrated gasification to electricity, typically using either a steam cycle or gas turbine cycle. The steam cycle normally is to generate electricity based on the Rankine cycle in which high pressure and high temperature steam produced in a boiler , raising efficiency and enhancing the is expanded through a turbine that drives an electric generator. The discharged steam from the turbine . gives up its heat of condensation in a condenser to a heat sink such as cooling water. The condensate is pumped back into the boiler to start a new cycle. Steam turbines produce most of the electric power in the world, using a variety of heat sources (Demirel and Gerbaud 2019, Demirel 2021). heat engine Natural gas turbine cycles and integrated gasification combined cycle gas turbines are examples TH to a lower temperature state TC. Figure 7.1 shows typical pressure of low carbon technology to generate electricity. In the latter case, the waste heat from the gas and temperature-entropy TS diagrams of a Carnot cycle. On both the PV and TS diagrams turbine is used to make steam to generate additional electricity, raising efficiency and enhancing the the sustainability of electricity generation.

(

ure 7.1: Steam power generation • AProcess 1-2: Isothermal heat addition at constant TH.energy by bringing a working Carnot heat engine performs the conversion of heattemperature to mechanical • fluid Process 2-3: Isentropic expansion at constant entropy S = S . 2 3state T . Figure 7.1 shows typical from a high temperature state TH to a lower temperature C • pressure-volume Process 3-4: Isothermal heat rejection at constant temperature TC. cycle. On both the PV and PV and temperature-entropy TS diagrams of a Carnot • TS Process 4-1:the Isentropic compression at constant S4 = Srepresents 1. diagrams area enclosed by the process curvesentropy of the cycle the net heat transfer to The added and rejected heat rates per unit mass flow(Dincer rate ofeta al. working fluid, as2021). seen in 7.1, are: be converted to mechanical energy by the engine 2017, Demirel A Figure Carnot cycle

consists of four totally reversible processes shown in Figure 7.1: = qin TH ( S 2 − S1 ) • Process 1‑2: Isothermal heat addition at constant temperature TH. = qout TC ( S 4 − S3 ) • Process 2‑3: Isentropic expansion at constant entropy S2 = S3.

Process 3‑4: Isothermal at while constant , • qin is the unit heat receivedheat by rejection the steam, qouttemperature is the unit TC. • Process 4‑1: Isentropic compression at constant entropy S4 = S1.

The added and rejected heat rates per unit mass flow rate of a working fluid, as seen in W= qout −7.1, qin are: out Figure = qin TH ( S2 − S1 ) (7.1) = qout TC ( S4 − S3 ) (7.2)

, sometimes Here, qin is the unit heat receivedincrease by the steam, while qby the unit heat by pressure the cooing out isreducing A significant thermal efficiency is possible the received condenser medium, for example, cooling water. The net power output per kg/s steam flow rate is: reflected by the increased area representing power output in Figure 7.1

qH

1'

1

2

qC

3

x3 3' x3'

4 4' Entorpy (S)

(a)

Temperature (T)

Temperature (T)

W= qout − qin (7.3) out

qH 2

1

2'

qC 4

3 3' Entorpy (S)

(b)

Figure 7.1.  Possible improvements in energy conversion efficiency in Rankine cycles: (a) decreasing the condenser pressure, (b) increasing the boiler temperature.

196  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence Possible improvements in steam‑power generation There are several modifications and retrofits for existing steam power generation technologies that yield improvements, sometimes considerable (Dincer et al. 2017, Demirel and Gerbaud 2019, Demirel 2021): • A significant thermal efficiency increase is possible by reducing the condenser pressure. This is reflected by the increased area representing power output in Figure 7.1a. However, the quality of the discharged steam decreases, which is not desirable for the blades of the turbine. • The thermal efficiency can be improved by increasing the boiler temperature (see Figure 7.1b). The quality of the discharged steam also increases and helps protect the turbine blades. The thermal efficiency of a Carnot cycle operating between temperature limits of Tmin and Tmax is the possible maximum efficiency and can be expressed as follows: η th,Carnot = 1 − (Tmin / Tmax ) (7.4)

• The thermal efficiency can be improved considerably by increasing the boiler pressure, leading to more useful superheated steam for use in the turbine. • An organic Rankine cycle (ORC) uses of an organic high molecular mass working fluid with a boiling point at a lower temperature than that of water. Such a working fluid allows a Rankine cycle to exploit heat recovery from lower temperature sources such as biomass combustion. • A cogeneration plant generates electric power and process heat from the same heat source and hence utilizes more available energy and reduces waste heat. Cogeneration can reduce the utility costs which are typically 10 to 20% of the total cost of such a process. The utilization factor for a cogeneration plant is the ratio of the energy used in producing electricity and process heat to the total energy input. An increased overall energy efficiency is not only advantageous thermodynamically but is also better for environmental protection by reducing greenhouse gas (GHG) and other emissions. • Combined heat and power (CHP), a type of cogeneration, generates both electric power and useful heat and helps reduce carbon emissions and use energy sources efficiently. Common CHP plant types follow: ○ Gas turbine CHP plants use the waste heat in the flue gas of gas turbines. The fuel used is typically natural gas. ○ Gas engine CHP plants use a reciprocating gas engine which is generally more competitive than a gas turbine up to about 5 MW. The gaseous fuel used is normally natural gas. ○ Biofuel engine CHP plants use an adapted reciprocating gas engine or diesel engine, and reduce carbon emissions. ○ Combined cycle power plants can be adapted for CHP. ○ Steam turbine CHP plants use the heating system as the steam condenser for the steam turbine. ○ Molten carbonate fuel cells and solid oxide fuel cells have hot exhausts, which are quite suitable for heating. ○ Nuclear power plants can be fitted with steam drains after the high, mid, and/or low-pressure turbines to provide heat to a heating system. Brayton cycle A Brayton cycle is the basis of a gas-turbine engine (Figure 7.2). The gas leaving the compressor can be heated in a regenerator (recuperator) by the hot exhaust gases, leading to an increase in thermal

Energy Analysis  197 Fuel T

3 qin

2

Wturb. out 4

Wcomp. in

Combustion

Compressor 1

qout

1

5

3 W

2

Regenerator

Turbine Exhaust gasses

Ws

4

Air S

(a)

(b)

Figure 7.2.  Gas turbine power plants. (a) Simple Brayton cycle, (b) Brayton cycle with regeneration (where the condition for regeneration is T4 >> T2).

efficiency and consequently reducing emissions. The effectiveness ε of an adiabatic regenerator is

Heat recovery generators defined as seen steam in Figure 7.2 as follows (Demirel 2021):

 H 5 − H 2 and/or  toε heat =  water  (7.5)  H4 − H2  mainly through convection (Demirel 2021). Regeneration is possible only when T4 >> T2. The effectiveness of most regenerators used

in practical engine operations is below 0.85. The thermal efficiency ηth of a Brayton cycle can be

Nuclear energy expressed as: Conventional nuclear power

 T1  (γ −1) / γ (7.6)  ( rp )  T3 

ηth = 1 − 

power

where rp is the compression ratio (P2/P1) and γ = Cp/Cv.

Heat recovery steam generators A heat recovery steam generator (HRSG) uses hot exhaust gases from gas turbines or reciprocating Blue hydrogen engines to is heat water and/or generate steam. The heated fluid, in turn, drives a steam turbine and/or Hydrogen a zero-emission (or near zero-emission) is used in Since the exhaust gas temperature is relatively low, heat amount of industrial nitrogen processes. oxides (NO ). Steam methane reforming is the most used andtransmission most x is accomplished mainly through convection (Demirel 2021). currently of producing hydrogen in large scales. It operates Nuclear energy Conventional nuclear power plants produce steam through nuclear fission in which the nucleus of improve an proven atom splits into smaller parts,other oftenones. producing free neutrons and photons in the form of gamma are technologies beside rays (γ). Fusion power, on the other hand, is generated by nuclear fusion reactions where two light atomic together to form a heavier nucleus and doing so, release a large amount of Table 7.1nuclei Somefuse possible technological improvements for in nonrenewable power energy. Nuclear fusion requires precisely controlled temperature, pressure, and magnetic field Nonrenewable energy Possible technological improvements parameters to generate net energy. technology

Steam power Blue hydrogen generation Hydrogen is a zero-emission (or near zero-emission) fuel with combustion products of water and Organic Rankine cycle Select an organic fluid with the best boiling point trace amount of nitrogen oxides (NOx). Steam methane reforming is the most used and most economic Brayton cycle Use regenerative heating process currently of producing hydrogen in large scales. It operates with an energy efficiency of Cogeneration oxy-combustion around 85%. Blue hydrogenUse is produced from natural gas with carbon capture. Sustainable energy Combined gasification Implement technology improvements, add syngas solutions need to integrate environmentalgasification impact assessment tools (Demirel 2018b, 2021). Table 7.1 for power shows some technological improvements for nonrenewable energy productions. Cogeneration and combined gasification for power are proven technologies beside other ones.

(γ)

198  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence Table 7.1.  Some possible technological improvements for nonrenewable power production technologies. Nonrenewable energy technology

Possible technological improvements

Steam power generation

Generate power close to ideal operation such as with Carnot cycle, generation with reheat, regenerative heating, and reheat and regenerative heating

Organic Rankine cycle

Select an organic fluid with the best boiling point

Brayton cycle

Use regenerative heating

Cogeneration

Use oxy-combustion

Combined gasification for power

Implement gasification technology improvements, add syngas cleaning

7.1.2  Renewable Energy Production Biogas, geothermal power, wind power, small-scale hydropower, solar energy, biomass power, tidal power, and wave power all fall under the category of renewable energy. Some definitions of renewable energy also include energy derived from the incineration of waste. Hydropower offers flexible power grid operation and often is among the lowest cost options. Higher percentage of wind turbine usage per year would improve the affordability of the power. Solar power is the conversion of sunlight into electricity, either directly using photovoltaics, or indirectly using concentrated solar power. Nuclear power may contribute significantly to future low carbon power with new plant designs, depending on such issues as national and regional politics, and fuel availability. Tidal power is a form of hydropower that converts the energy of tides to electricity or other useful forms of energy. Tides are more predictable than wind energy and solar power (Demirel 2021). Renewable energy technologies contribute to sustainable energy and to world energy security, reducing dependence on fossil fuel resources, and creating opportunities for mitigating GHG emissions. Loosely, there have been three generations of renewable energy technologies (IRENA 2020a, Demirel 2021): • First-generation, including hydropower, biomass combustion, and geothermal power and heat. • Second-generation, including solar heating and cooling, wind power, modern forms of bioenergy, and solar photovoltaics. • Third generation, including advanced biomass gasification, biorefinery technologies, concentrating solar thermal power, hot dry rock geothermal energy, and ocean energy. Renewable energy sources vary and often require energy storage or a hybrid system to accommodate daily and seasonal variations and fluctuation. Energy storage by various means often helps renewable energy to be more practical, affordable, and sustainable (Demirel 2021). Hydropower Hydropower is the largest source of renewable energy. Although it avoids many environmental impacts of fossil fuel energy systems, some aspects of it are not necessarily environmentally benign as there can be significant environmental impacts associated with its operations. The construction and operation of large hydroelectric reservoirs sometimes destroys forests, agricultural land, and wildlife habitats, and sometimes requires the relocation of entire communities. Hydroelectric systems also can negatively affect aquatic ecosystems, and fish kills of 5 to 10% by the turbines are common. The decomposition of organic material in reservoirs may release carbon monoxide and methane. However, reservoirs can help with flood control, agricultural irrigation, and recreation. Designing hydro turbines with remodeled blades can enable sustainable, climate-resilient, and marine life-safe turbines. Restoration hydropower may support habitat creation, improved water quality, and sustained increases of ground water by restoring watersheds (IRENA 2020a, 2020b, IEA 2019).

Energy Analysis  199

Hydro energy is generated from the potential energy of dammed or flowing water. The quantity of energy obtained depends on the volume and the amount of potential energy in the water. The electric power production can be approximated as:  ρ g ∆z= mg  ∆z (7.7) W = Q

. where W is the power, ρ is the density of water (~ 1000 kg/m3), ∆z is the height, Q is the volumetric flow rate, and g is the acceleration due to gravity. Hydropower permits avoidance of the use of fossil fuels and hence GHG emissions. Hydroelectric plants also tend to have longer economic lives (50 years or more) than fuel-fired power plants. A hydroelectric plant rarely operates at its full capacity over a full year. The ratio of annual average power output to installed capacity is the capacity factor for a hydroelectric power plant. Wind energy Wind power is the result of conversion of the kinetic energy in wind to the mechanical energy of a rotating shaft. The kinetic energy of the wind is proportional to the mass flow rate of air through the blade span area: π v3 D 2 W wind η= = (constant)v3 D 2 (7.8) wind ρ 8

where

η ρπ constant = wind (7.9) 8 Here, ρ is the density of air, v is the speed of air flow, D is the diameter of the blades of the wind turbine, and ηwind is the efficiency of the wind turbine. Therefore, the power produced by the wind turbine is proportional to the cube of the wind speed and the square of the blade span diameter. Betz’s law states that the power output of a wind turbine is a maximum when the wind is slowed down to one-third of its initial velocity. The actual efficiency of wind power is (Demirel 2021): η wind turbine =

Shaft power out of turbine into gear box (7.10) Wind power into turbine blades

The efficiency ranges between 20% and 40% and becomes the part of the constant in Eq. (7.8). Large-scale wind farms can be connected to the electric power transmission network, while smaller facilities are often used to provide electricity to isolated locations. In a wind farm, individual turbines are interconnected in a power collection system. The ratio between annual average power and installed capacity rating of a wind-power production is the capacity factor. Typical capacity factors for wind power vary between 20% and 40%. Wind power density is the effective power of the wind at a particular location. Solar energy Solar energy is derived from the energy of solar radiation. There are two main technologies for electricity generation: photovoltaic and solar-thermal electric (IRENA 2020a). Details on these are as follows: • Photovoltaic conversion produces electricity directly from sunlight in a solar cell. • Solar-thermal electric production is usually based on concentrating solar power systems and use lenses or mirrors and tracking systems to focus a large area of sunlight onto a small collector. The concentrated solar energy heat is used to produce steam to drive turbines and produce electricity.

200  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence Of course, solar energy can also be used for heating, particularly domestic hot water heating and space heating in buildings. Hydrogen energy Renewable hydrogen energy can be produced in many ways and from various energy sources. Hydrogen energy can be derived from the electrolysis of water using hydropower, or wind power, or solar photovoltaic power, among others. Note that hydrogen energy is not an energy source, but rather an energy carrier (Granovskii et al. 2007, Jianu et al. 2016, Demirel 2018b, 2021, Sadeghi et al. 2020, Valente et al. 2021). Figure 7.3 shows a schematic of wind energy-based hydrogen production. Alkaline electrolysis technologies are the most mature commercial systems. These electrolyzers typically have energy Chp.7. efficiencies of 57%–75%, and current densities of 100–300 mA/cm2. Wind Energy Wind Turbine A

Electricity

Transformer /Thyristor

O2/KOH Gas Separator

KOH

Deoxidizer

Electrolyzer B Water 9 kg/h KOH

Oxygen

H2/KOH Gas Separator

Dryer Hydrogen Compression 1 kg/hr Storage Delivery C

Figure 7.3. Alkaline electrolysis of water for hydrogen production with compression, storage, and delivery (Matzen et al. 2015a).

Green energy Green energy can be harnessed with little pollution and includes anaerobic digestion, geothermal power, wind power, small-scale hydropower, solar energy, biomass power, tidal power, wave power, and power derived from the incineration of waste materials. Solar, wind, and local hydropower are some of the more common types of green energy systems. Solar and wind energy usually need proper energy storage facilities. Recycled Geothermal heat pump systems use the constant temperature of the fatty acids earth, which is around 7Acid/ to 15°C a meter or two underground, are an option and save money over methanol conventional natural gas and petroleum-fueled heat approaches. Solar heating systems may be used Dilute acid esterification to heat domestic hot water, for space heating and for industrial applications or as an energy input for other uses such as cooling equipment. In many climates, a solar heating system can provide a Vegetable oils very high percentage (50 to 75%) of domestic hot water energy (Demirel 2018b, Goldmeer 2019). Methanol

Transesterification

Geothermal energy Geothermal energy uses a hot geothermal fluid and a suitable working fluid with a much lower boiling point than geothermal fluid. TheMethanol working fluids mayWater be isobutene, Biodiesel isopentane, n-pentane, or recovery power plants: washing purification ammonia. There are various types of geothermal • In a dry steam power plant, the geothermal steam goes directly to a turbine, where it expands Biodiesel and produces power. The expandedGlycerin steam is injectedCatalyst into the geothermal well. removal Glycerin purification • In a flash steam power plant, geothermal fluids above 360°F (182°C) can be flashed in a tank at low pressure causing some of the fluid to vaporize rapidly. The vapor then expands in a turbine. Figure 7.6. Biodiesel production from fatty acids and vegetable oils. • In a binary-cycle power plant, moderate-temperature geothermal fluids between 85 and 170°C are commonly used. The term “binary” refers to dual-fluid systems, wherein hot geothermal brine is pumped through a heat exchange network to transfer its energy to a working fluid driving a power train.

Energy Analysis  201

• In the case of a “dry fluid”, the cycle can be improved using a regenerator: since the fluid has not reached the two-phase state at the end of the expansion, its temperature at this point is higher than the condensing temperature. This higher temperature fluid can be used to preheat the liquid before it enters the evaporator by a heat exchanger, so the power required from the heat source is reduced and the efficiency is increased. At temperatures below about 200°C, binary power systems are favored for relative cost effectiveness. Above about 200°C, flashing geothermal fluids to produce steam and directly driving turbine/generator is preferred (Rosen and Koohi-Fayegh 2017, Demirel 2018b, Javanshir et al. 2020, Farsi and Rosen 2021, Mahmoudan et al. 2022). Bio energy Biomass from agriculture, forestry, and households can be used for biofuel, heat, and chemicals production in various conversion processes including: • anaerobic digestion to produce biogas, • gasification to produce syngas (H2 + CO), and • direct combustion. Biomass can be fermented directly to produce ethanol. Certain photosynthetic microbes produce hydrogen from water in their metabolic activities using the energy of light. Energy from biofuel including ethanol and biodiesel is derived via biological carbon fixation. According to the IEA (2019), biofuels have the potential to meet more than a quarter of world demand for transportation fuels by 2050. First-generation biofuels use sugar, starch, and vegetable oil as feedstock. Biodiesel is the most common biofuel produced from oils or fats using transesterification and is a liquid similar in composition to fossil/mineral diesel. Second generation biofuels (advanced biofuels) are produced from sustainable feedstocks such as lignocellulosic biomass. Sustainability of a feedstock is measured by its availability, affordability, impact on GHG emissions, biodiversity impact, and land use (Kumar et al. 2010, Demirel 2018b, 2021, NCBI 2014). Biogas Biogas can be produced either from biodegradable waste materials or by the use of energy crops fed into anaerobic digesters to supplement gas yields. Anaerobic digestion involves a series of processes with microorganisms breaking down biodegradable material in the absence of oxygen. Acidogenic bacteria convert the sugars and amino acids into carbon dioxide, hydrogen, ammonia, and organic acids. Acetogenic bacteria convert these resulting organic acids into acetic acid, along with additional ammonia, hydrogen, and carbon dioxide. Finally, methanogens convert these products to methane and carbon dioxide: C6H12O6 → 3CO2 + 3CH4 (7.11) The three principal products of anaerobic digestion are biogas, digestate, and water. Biogas contains around 60% methane and 40% CO2, by volume. Digestate is the solid residue of the biomass used in digesters and contains fibrous residue, liquor, or a sludge-based combination of the two fractions. The nutrient-rich digestate may be used as fertilizer (Demirel 2018b). Anaerobic digestion facilities have been recognized by the United Nations Development Programme as one of the most useful decentralized sources of energy supply. A co-digestion or co-fermentation plant is typically an agricultural anaerobic digester that accepts two or more input materials for simultaneous digestion. Utilizing anaerobic digestion technologies can help reduce: • The usage of fossil fuels. • The energy footprint of waste treatment plants.

202  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence • Methane emissions from landfills. • The need for industrial chemical fertilizers. Biomass‑to‑liquid fuels Biomass-to-liquid (BTL) fuel exhibits flexibility with regard to feedstocks. Various types of BTL processes exist, for example, lignocellulosic biomass is first converted into synthesis gas known as syngas (containing mainly carbon monoxide and hydrogen) and later the syngas is converted to liquid fuel using a catalytic process known as the Fischer-Tropsch process. The biomass feedstock can also be converted to bio-oil in decentralized units close to the place of biomass production. The bio-oil is then gasified under high pressure (30 bar) and temperatures (1200–1500°C) to a high-quality clean synthesis gas. Bio-oil is as a valuable intermediate to produce other products or for use as a fuel in boilers and furnaces for heat production and in static engines for heat and electricity generation. The yield of bio-oil from a solid biomass feedstock is about 75%. Its volumetric energy density is about 20 GJ/m3 (Demirel 2018b, 2021). A biofuel fuel pathway includes three critical components: (1) feedstock (a biomass), (2) conversion process, and (3) fuel type derived from renewable feedstock sources (Figure 7.4). Biofuels biohydrogen, bioethanol, biobutanol, feedstock biomethanol, biooil, (Figure biogas, and andinclude (3) fuel type derived from renewable sources 7.4).biodiesel. Some details on biofuels follow (Demirel 2018b): • Advanced biofuels (D5) are produced from any type of biomass except corn starch and sugar Advanced biofuels produced from is any type 50% of biomass except corn starch cane. The life cycle (D5) GHG are emissions reduction at least compared to a petroleum baseline. The life cycle GHG emissions reduction is at least 50% compared to a petroleum baseline. • Biomass-based diesel (D4) includes biodiesel and renewable diesel produced from biomass. • Biomass-based diesel . The life cycle GHG emissions reduction is at least 50% compared to a diesel baseline. cycle GHG emissions reduction is at least 50% compared to a diesel baseline. • Cellulosic biofuel (D3 or D7) is eligible for D7 RINs the fuel must be cellulosic diesel. The life • Cellulosic biofuel (D3 or D7) is cycle GHG emissions reduction is at least 60% compared to a petroleum baseline. GHG emissions reduction is at least 60% compared to a petroleum baseline. • • Ethanol derived from corn starch, or any other qualifying renewable fuel. The life cycle GHG emissions reduction is at least 20% compared to a fossil-fuel baseline.

•

-

Ethanol Biomass

Feed handling/ drying

Gasification

Flue gas

Gas cleaning

Alcohol synthesis

Separation Higher alcohols

Figure 7.4.  A block flow diagram for biomass gasification for alcohol production.

-Tropsch diesel Fischer‑Tropsch diesel -Tropsch (F-T) Fischer-Tropsch (F-T)diesel dieselisis produced when a gaseous fuel is converted to a liquid and refined fuel. diesel F-T diesel offers reduced and is compatible with advanced emissionto make fuel. F-T diesel offersemissions reduced emissions and is compatible with advanced emissiongreen production, fats, production, algal oils, waste oils,oils, or virgin oils or arevirgin converted low- to controldiesel devices. In green diesel fats, algal waste oils, oils aretoconverted low-sulfur diesel by hydrogenation and hydrodeoxygenation (Demirel 2018b). Ethanol production thanol production

The most common processes to produce ethanol today use yeast to ferment sugars (glucose) and starch. Sugar cane and sugar beets are common sources. Fermentation is a natural microbiological

Energy Analysis 203 CO2 Enzyme

Corn/ wheat

Milling

CO2 scrubbing

Distillation/ dehydration

Enzyme

Liquefaction Liquefaction

Sacharification

Fermentation

Ethanol

Dewatering /drying

Yeast Coproducts/DDGS

Figure 7.5. Main steps for the first-generation biofuel production process producing bioethanol, dried distillers grain and solubles (DDGS), and CO2.

process where sugars are converted to alcohol and carbon dioxide by yeast (Saccharomyces cerevisiae—a type of fungi) in about 24 to 36 hours. The overall reaction for fermentation is: C6H12O6 → 2(CH3CH2OH) + 2CO2 Sugar → Alcohol + Carbon dioxide gas

(7.12)

The resulting solution is distilled to obtain fuel grade ethanol. For each pound of simple sugars, yeast can produce approximately 1/2 pound (0.15 gallons) of ethanol and an equivalent amount of carbon dioxide. Corn consists of 72% starch, which is broken down into simple sugar by adding an enzyme (glucoamylase) so it can be fermented with yeast. Byproducts include high fructose corn syrup, food additives such as citric acid, corn oil (cooking oil), and livestock feed (Figure 7.5). With advanced technology, cellulosic biomass, such as trees and grasses, can also be used as feedstock for ethanol production by using acids or enzymes to create sugars that can be fermented. Cellulosic ethanol involves a more complicated production process than conventional ethanol made from fermentation of starches or sugars. However, this process can avoid interference with the food supply chain with increasing ethanol production. Lignocellulosic biomass requires less energy, fertilizers, and water than corn grains. Lignocellulosic biomass can also be grown on lands that are not suitable for growing food. Lignocellulosic biomass from grasses, for example, can produce two harvests a year for multiple years without the need for annual replanting (Demirel 2018b). Biodiesel production Biodiesel is produced from transesterification of a feedstock of vegetable oils or animal fats catalytically with an alcohol, typically methanol. Biodiesel consists mostly of fatty acid methyl (or ethyl) esters (FAME). Animal and plant fats and oils are typically made of triglycerides which are esters containing three free fatty acids. One hundred pounds of soybean oil typically react with 10 pounds of methanol in the presence of a catalyst to produce 10 pounds of glycerin and 100 pounds of biodiesel. The base catalyzed reaction occurs at low temperature and pressure and yields high conversion (98%) with minimal side reactions and reaction time. Products of the reaction include not only biodiesel, but also glycerin, excess alcohol, and some water. Residual methanol is typically removed through distillation and recycled to the reactor. Free fatty acids first undergo esterification with dilute acid before transesterification, as seen in Figure 7.6 (Nguyen and Demirel 2010, 2013, Demirel 2018b). Algal biomass is another feedstock for biodiesel production. The main steps are shown in Figure 7.7, which shows a process flow diagram to produce algal biodiesel and biopower. Harvesting of algae follows the cultivation of an algal culture. Extraction of lipids and transesterification of the lipids produce fatty acid methyl ester and crude glycerol. In an integrated system, the residue of algae may be fed to an anaerobic digestion system to co-produce biogas and power through a steam power generator (Nguyen and Demirel 2013, Allen et al. 2018).

204  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence Acid/ Acid/ methanol

Recycled fatty acids Dilute acid esterification Vegetable oils Transesterification

Methanol

Methanol recovery

Water washing

Glycerin Glycerin purification

Biodiesel purification Biodiesel

Catalyst removal

Figure 7.6.  Biodiesel production from fatty acids and vegetable oils. RECY-SOL M101

SOLVENT

LIGHT WATER/NUTRIENTS CARBON DIOXIDE ALGAE

ALGAL BIOMASS PRODUCTION IN PHOTOBIOREACTOR

BIOMASS RECOVERY

S7

BIOMASS EXTRACTION

H2O-RECY BROTH

H2O-NUT CO2

BIOREFINARY: BIODIESEL BIOPRODUCT

OIL

LIGHT

1

R101

2

SEP1

3

6

EFF-CO2 POWER

S2

SOLVENT RECOVERY

SEP2

5

S3

SEP3 S8

BIOGAS

S5 TURBINE

S6

BOILER

POWER GENERATION

R102 ANAEROBIC DIGESTION

EFFLUENT

4 ANIMAL FEED

FERTILIZER IRRIGATION

Figure 7.7.  Algae biomass feedstock preparation to produce triglyceride acids, bioproduct, and biopower. R101: Reactor; SEP1: Separator (centrifuge); SEP2: Extractor; SEP3: Distillation column; R102: Reactor.

Green diesel production Green d diesel is produced by removing the oxygen from triglycerides and fatty acids, producing a paraffin-rich product, water, and carbon oxides by catalytic reaction with hydrogen. Triglycerides and fatty free acids both contain long, linear aliphatic hydrocarbon chains, which are partially unsaturated and have a carbon number range like the molecules found in petroleum diesel fuels. Biodiesel has around 11% oxygen, whereas petroleum-based diesel and green diesel has no oxygen. like the molecules found in petroleum diesel fuels. Petroleum diesel has around 10 ppm sulfur and biodiesel and green diesel have less than 1 ppm sulfur. Therefore, green diesel has a heating value equal to conventional diesel and is fully compatible for 1 ppm sulfur. blending with the standard mix of petroleum-derived diesel fuels. Feedstocks rich in saturated fats, such as palm and tallow oil, require less hydrogen than feed/stocks higher in olefin content, such as soybean or rapeseed oil. The diesel yield depends on both the feedstock type and the level of hydro /stocks higherrequired in olefintocontent, such as soybean or rapeseed oil. The diesel isomerization achieve the product cloud point specification (Demirel 2018b, Singh et al. the 2018b, Douvartzides et al. 2019). Green diesel production requires large volumes of H2 and a catalyst to hydro deoxygenate (HDO) triglycerides into a high-cetane diesel fuel by removing all the oxygen and saturating all the double bonds in the fatty acids. This is the ‘ecofining’ technology green diesel production from catalyst to hydro deoxygenate 2 and a for

Energy Analysis  205 Hydro CO2 Capture Bio-oil Upgrading Deoxygenation

Fast Pyrolysis

Feed Drying/ Size Reduction

Biomass

Naphta

CO2

Light Gases Fast Pyrolysis

Feed Drying/ Size Reduction

Separation

Hydro CO2 Capture Bio oil Upgrading Deoxygenation

Separation

Kerosene Naphta

CO2

Light Gases

Green Diesel Kerosene

Biomass

Zinc powder Formic acid/ Alcohols

Heat

Hydrogen

Water Green Diesel

Figure 7.8.  Ecofining process for producing green diesel using mainly vegetable oil. Zinc powder Hydrogen Water Heat Formic and animal fats (Figure 7.8). Theacid/ technology reduces cost and risks of Alcohols

T

vegetable oils compliance, minimizes feedstock usage produces green diesel with high yield. HDO, isomerization, and separation are the main processes, as shown in Figure 7.8. In the HDO process a free fatty acid Figure 7.8. Ecofining process for producing green diesel using mainly vegetable oil. (R-COOH) is converted to a long chain hydrocarbon and water by the following reactions: -(CH + H2O (7.13) Butanol 2O)- + H2 → -(CH2)-Hot

total cost

By a

Cost Butanol By aanaerobic fermentation process, sugars can be converted into butyric, lactic, and acetic acids. Butyric process using E.coli Minimum approach temperature acidal. is 2017, converted into butanol. Gas Demirel 2018b). stripping can be used to extract the butanol. Isobutanol can be produced by an anaerobic process using E. coli strains and continuous vacuum stripping for butanol fermentation (Xue et al. 2017, Demirel 2018b).

Methanol Cold a carbon-rich feedstock, H2 Methanol synthesis needs Methanol involves three needs fundamental steps: Methanol synthesis a carbon-rich feedstock, H2, and a catalyst, mainly Cu/ZnO/Al ∆Tmin2O3. The hydrogen to carbon [(H − CO CO2)] =reforming 2 achieved with thesyngas water-gas2 2)/(CO process involves three fundamental steps: (i)+biomass to produce with an optimal L

ratio of hydrogen to carbon [(H2 − CO2)/(CO + CO2)] = 2 achieved with the water-gas-shift reaction, Demirel 2018). The keycrude chemical reactions involved follow: (a) (b) et al. (ii) conversion of syngas into methanol, and (iii) distillation of crude methanol (Matzen 2015a,b, Wang and Demirel 2018). The key chemical reactions involved follow:

∆Tmin∆Ho(298K) CO2 + 3H2 = CH3OH + H2O = − 49.4 kJ mole-1 –1 ∆H°(298K) = − 49.4 kJ mole (7.14) o ∆T on tradeCO + 2H2 = CH3OH ∆Hmin(298K) = − 90.5 kJ mole-1

CO2 + 3H2 = CH3OH + H2O CO + 2H2 = CH3OH

∆H°(298K) = − 90.5 kJ mole–1 (7.15)

Methanol producedfrom from green an ethanol plant have a 2 and 2 captured Methanol produced green H2 H and CO2CO captured form form an ethanol plant can havecan a positive sustainability, as shown in Figure 7.9.7.9. impact on sustainability, as shown in Figure Wind electricity

Transformer

Electrolysis Electrolyte solution

Methanol

O2 H2

H2 compression

H2

Methanol synthesis Water

Water

CO2 Ethanol plant

Figure 7.9.  Methanol production facility based on feedstocks of renewable hydrogen and CO2. 2.

206  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence Dimethyl ether (DME) Dimethyl ether is the simplest ether (CH3OCH3) and can be produced by catalytic methanol dehydration, according to the following reaction: 2CH3OH → CH3OCH3 + H2O (7.16) The properties of DME are similar to those of liquefied petroleum gas. For instance, DME has a lower heating value of 28.4 MJ/kg and a density of 0.67 kg/liter. DME exhibits the conventional diesel fuel equivalency of 0.59 as the lower heating value of diesel is 43.1 MJ/kg. DME is not a greenhouse gas and can be used as a substitute for diesel fuel and domestic gas (Matzen and Demirel 2016, Demirel 2018b). Energy from solid waste Garbage, often called municipal solid waste, is the source of about 12% of the total biomass energy consumed in the United States. Municipal solid waste contains biomass like paper, cardboard, food scraps, grass clippings, leaves, wood, and leather products, and other non-biomass combustible materials, mainly plastics and other synthetic materials made from petroleum (Kumar et al. 2010, Demirel 2018b). Recycling and composting programs may reduce the share of biomass in municipal solid waste that is land filled or burned. Solid waste can be burned in special waste-to-energy plants, which produce heat to make steam to heat buildings or to produce electricity. Such plants also help reduce the amount of solid waste to be buried in landfills. In addition, there are solid waste incinerators that simply burn the solid waste without electricity production. Property values and energy values for various types of biomass are presented in Tables 7.2 and 7.3. The data in these tables could help predict energy needed from biomass sources. Table 7.2. Typical calorific values in lower heating value (LHV) and other properties of various types of biomass (Ptasinski 2016, Demirel 2018b). Biomass

Moisture, %

(LHV) kcal/kg

Density, kg/m3

Bark fir

50

1,840

280

Briquettes

20

4,214

660

Forest wood chip, dry

40

2,511

240

Forest wood chip, fresh

55

1,720

310

Miscanthus

10

3,780

140

Rapeseed

9

5,870

700

Sawdust

6

3,629

160

Stover rapeseed

15

3,580

115

Sunflower

9

4,780

600

Wheat 

15

3,580

700

Wheat straw

15

3,440

100

Wood chip

20

3,629

175

Wood granulate

8

3,81

600

Wood logs ash 

45

2,245

650

Wood logs ash dry

20

3,509

400

Energy Analysis  207 Table 7.3.  Energy and material data for selected types of biomass (Demirel 2018b, 2021). Product Bagasse sugarcane Coconut husks

Moisture, %

Approx. ash content, %

Lower heating value (LHV), MJ/kg

18

4

17–18

5–10

6

16.7

Coffee husks

13

8–10

16.7

Corn stover

5–6

8

17–19

Corncobs

15

1–2

19.3

Cotton husks

5–10

3

16.7

Groundnut shells

3–10

4–14

16.7

Miscnathus

14

1–3

19–20

Oil-palm fibres

55

10

7–8

Oil-palm husks

55

5

7–8

Poplar wood

5–15

1.2

17–19

Rice hulls

9–11

15–20

13–15

Rice straw and husk

15–30

15–20

17–18

Switchgrass

8–15

6

18–20

Wheat straw and husk

7–15

8–9

17–19

12

1–5

17–19

Willow wood

7.1.3  Implications of Energy Use Energy use is the utilization of energy to provide services and perform tasks and requires energy-conversion processes in addition to distribution, transportation, and storage for energy. Energy-conversion technologies convert energy from one form to another, to produce energy carriers from energy sources. Common energy-conversion technologies include: • Electrical generating stations (fossil fuel, nuclear, hydroelectric, etc.) • Polygeneration plants that produce more than one energy product from a single plant (e.g., cogeneration and trigeneration) • Petroleum refineries • Lights • Engines • Motors • Heaters • Chillers and refrigerators Compact fluorescent bulbs reduce electricity consumption by 75% relative to incandescent bulbs but come with a dash of mercury. Biobased fuels reduce greenhouse gas emissions but contribute to air, water, and soil quality impacts in the agricultural stage. Reusable cloth diapers, while made of a renewable, natural material (cotton), require hot water, energy, and detergents for washing. Part of corn crop production has been converted to ethanol worldwide in many jurisdictions. Ethanol and biodiesel are the most widely used biofuels. The benefit of biofuels is that they lead to the recycling of carbon captured by those energy crops; the carbon is either stored and not released or it is cycled in the form of biofuels with no net carbon release to the atmosphere. For instance, a hectare of corn can capture about 3 tons of carbon dioxide per year (Kumar et al. 2010). However, more detailed examinations on the impacts of biofuels on GHG emissions show that the overall emissions from ethanol are larger when compared with a unit energy of gasoline. One

208  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence needs to consider the whole process of production of ethanol and compare this to what happens when corn is grown and when corn is not grown. The land used to grow corn for ethanol is the first factor to consider in terms of if it is used to grow crops or not; this is known as the opportunity cost of the land. For example, if that same hectare of land is allocated to reforest it would capture around 7.5 to 12 tons of carbon dioxide per year. Therefore, converting forest land to energy crops increases GHG emissions in some cases. Only abandoned land used for cellulosic ethanol production would lead to break-even case. Overall, land-use change means increased GHG emissions in many cases, habitat loss, and less food production (Demirel 2018b, 2021, Rezaie et al. 2018).

7.1.4  Energy Production Assessments Figure 7.10 shows global primary energy consumption, broken down by type, with projections to year 2050. According to the projections, renewable energy consumption will reach the level of coal consumption by 2050 and will be the dominant energy consumed together with petroleum and other liquids (IEA 2019). Natural gas and nuclear energy consumption are expected to remain flat (Rosen 2013a,b, Alhajji and Demirel 2015, Theising 2016). The global renewables outlook (IRENA 2020b) shows the path to create a sustainable future energy system. Comprehensive policies should consider energy, climate goals, and socio-economic challenges supported with decarbonization efforts. The following actions can help reach the overall goals: • A careful transition to renewables and electrification, with the corresponding benefits to socio-economic development. • Energy efficiency measures, with the benefits of reduced resource use and increased system • flexibility and employment

economic development. • Energy storage, interconnected hydropower, green hydrogen, and other technology investments can help in achieving energy and climate sustainability (Demirel 2021). flexibility and employment Primary energy consumption for power production is anticipated to keep increasing steadily. As Figure 7.11 shows, the share of renewables in power generation will likely increase, while most other sources including coal, nuclear and natural gas are expected to remain flat.

Figure 7.10.  Primary energy consumption by energy source world-wide with projections - to 2050 (IEA 2019, IEO 2019).

likely

Energy Analysis  209

In quadrillion Btu

Figure 7.11.  Primary energy consumption by energy source for electric power generation worldwide (IEA 2019, IEO 2019).

Energy production assessments involve various aspects. These are considered in various methods, including the following (Demirel 2018b, IEO 2019): • Environmental Investment Risk Assessments may be based on non-monetary and monetary methods for evaluating the risk of investing in sustainable energy. Non-monetary methods often focus on environmental priorities. Monetary methods are based on the use in conventional cost-benefit analysis for investment and risk assessments with sustainable energy management as a goal. • Market Price Methods estimate the economic value of the ecosystem product using standard economic techniques to evaluate the amount of energy used for obtaining each product. • Productivity Methods estimate the economic value of the ecosystem product that supports production of commercial market goods. For example, the use of alternative energy resources in some production processes improves the value of the final product and thus becomes environmentally acceptable. • Hedonic Pricing Methods are mostly used to determine value of the products whose energy characteristics have been improved, making them environmentally acceptable. Issues with biofuel production Numerous issues are associated with biofuel production and use (Searchinger et al. 2008, Mohr and Raman 2013, Demirel 2018b). These include:

• • • • • •

Impact of petroleum oil prices Food versus fuel debate Poverty reduction potential, GHG emissions levels, sustainable biofuel production Deforestation and soil erosion Loss of biodiversity Impact on water resources

Possible improvements in renewable energy Table 7.4 shows some possible improvements in renewable energy production. One of the pressing improvements needed is enhanced energy storage. Also, improvements are necessary in energy efficiency, environmental and technological issues. In biofuel production one must analyze energy balances and efficiencies, as well as energy return on investment (EROI). EROI represents the

210  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence Table 7.4.  Some possible improvements for renewable energy production technologies. Renewable energy technology

Possible technological improvements

Hydropower

Need to consider ecological and social implications of large dam projects in early stage of design and construction

Solar and wind power

Need integrated and reliable energy storage

Hydrogen energy

Need to increase efficiency of electrolysis and other production methods. Needs improvements in storage and transportation

Biogas

Require carbon capture and sequestration and conversion to electricity and chemicals

Ethanol

Need to avoid interference in food supply chain with the first-generation ethanol production

Biodiesel

Need to avoid interference in food supply chain with the first-generation biodiesel production

Green diesel

Require safe and affordable green hydrogen supply, hydro isomerization Table 7.5.  Sustainable biofuels principles (RSB 2009, Demirel 2018b, 2021).

Concern

Description

GHG emissions

A true analysis is necessary for the overall GHG emissions from the usage of biofuels compared to fossil fuels for the same unit of energy.

Economic and social development

Biofuels may lead to rural economic activity and create jobs, contributing to a positive social impact of increased productivity. At the same time, lost habitat because of cropland creation from forestland may create adverse impacts. A transparent reporting process is necessary for stakeholders, including consumers and policy makers.

Food cost and security

Converting the first generation of biomass including corn, sugar beet and soybean may lead to food security problems.

National liabilities

Major liabilities include labor, water, and land rights.

Ecological balance

Biofuel production should be in line with protection of ecology and ecosystems, including soil, water, and air.

Creating technology

Biofuel production processes should promote safe, economical, and sustainable designs and technologies.

ratio of the useful energy produced to total energy required to produce that useful energy (Hall et al. 2014). Not all biofuels perform equally in terms of their impact on climate, energy security, ecosystems, and hence on sustainability. Table 7.5 displays some important considerations in biofuel use. The environmental, economic, and social impacts of biofuels need to be assessed throughout the entire life cycle, preferably using life-cycle assessment. Life cycle energy efficiency (LCEE) refers the ratio of total energy produced to the total energy consumed. Land use intensity (m2/(MJ fuel/year)) is the ratio of area per unit of energy produced. The contribution to GWP (kg CO2-eq/MJ fuel) includes the emissions from extraction, production, and usage of nonrenewable energy sources (RSB 2009, IRENA 2020a,b, Demirel 2018b, 2021, Kool et al. 2018, Zhang et al. 2018). Figure 7.12 shows the standards, development, impacts, and system performance for sustainable biofuel production, as reported in a prior study (RSB 2009). The gap remains high between expectations of sustainable energy transitions and the reality of today’s energy systems in which reliance on fossil fuels. Energy decision makers need to consider evidence-based assessments and the implications of the choices they make. Sharp emission cuts are achieved across the board thanks to multiple fuels and technologies providing efficient and cost-effective energy services for all. But the momentum behind clean energy technologies is not enough to offset the effects of an expanding global economy and population. The rise in emissions slows but the world falls far short of shared sustainability goals.

Energy Analysis  211 •Environmental, economic, social, technical, operational, and local requirements for standards

•Stakeholder agreement, policy making, compliance with international norms, equity, diversity, inclusion, and conflict resolution

Contents of standards agreed

Major impacts

Governance

Execution

•Long-term, short-term, primary, secondary, direct, indirect, positive, negative, and resilience indicators of biomass scenarios •Willing to pay, and willing willing to to accept , and the level of gap between them

•Concensus and capacity building, internal and external audit, cerification, accrediation, reporting, consistency, tansparency, and being part of bioeconomy

Figure 7.12.  Sustainable biofuel production standards, development, impacts, and system performance (RSB 2009).

Bioenergy systems assessment For assessing bioenergy systems, several sustainability indicators were developed by the Global Bioenergy Partnership for Sustainability task force (GBEP: https://iea.blob.core.windows.net/assets/ imports/events/301/05_malavelle.pdf). The indicators are not all measurable, while metrics scan and quantify the sustainability of emerging technologies. Metrics in bioenergy technologies can express CO2-eq emissions and avoided carbon emissions because of energy substitution by renewable energy. Domestic biomass potential, for example, shows the amounts of available biomass. Bioenergy can add extra pressure on biodiversity, scarce water resources and food security if it is not sustainably produced. If land use is not well planned and enforced, it can lead to negative impacts on climate change (Hayashi et al. 2014, Demirel 2018b, 2021).

7.2  Energy Conservation Energy conservation (EC) focuses on reducing energy consumption and increasing efficiency in energy usage for the same useful energy output and often is an important part of sustainable development. EC may lead to increased energy security, financial gain, and environmental protection (Demirel 2018a). For example, electric motors consume a considerable amount of electrical energy and operate at efficiencies between 70 and 90%. Efficiency is defined by: W η = Used (7.17) WElect . . where WUsed is the power used by the motor and WElect is the electric power supplied to motor. Using an electric motor operating with higher efficiency will conserve energy throughout its useful life. For example, if no transmission losses occur: • A motor with an efficiency of 80% will draw an electrical power of 1/0.8 = 1.25 kW for each kW of shaft power it delivers. • If the motor is 95% efficient, it will draw 1/0.95 = 1.05 kW only to deliver 1 kW of shaft work. • Therefore, between these two motors, electric power conservation is: 1.25 (ηmotor = 95%) kW – 1.05 (ηmotor = 95%) = 0.20 kW (per kW of shaft power delivered).

212  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence High efficiency motors are more expensive, but they save energy, which can be determined as follows:

Welect.saved

 1 1  (Rated power)(Load factor)  −  (7.18)  ηstandard ηefficient 

where the rated power is the nominal power delivered at full load of the motor, which is the fraction of the rated power at which the motor normally operates. Annual saving is estimated by Annual energy saving = (Welect.saved)(Annual operation hours). For example, a compressor that operates at partial load causes the motor to operate less efficiently. Using cold air for the compressor intake lowers the compressor work and conserves energy (Demirel 2021). Residential energy efficiency and conservation programs typically focus on space heating and cooling as well as water usage. Energy recovery also may be a part of energy conservation through captured and hence reduced waste energy. Some examples of energy recovery practices are energy and water recycling, and use of heat recovery steam generators (HRSGs). Energy conservation of compression or expansion work It is possible to conserve energy in the compression work by minimizing friction, turbulence, heat motorThetospecific operate less ofefficiently. for temperature the transfer and other losses. volume the inlet gas Using would becold smallair at low leading to a reducedconserves cost of compression work in a multistage compression system with intercooling, energy (Demirel 2021). as seen in Figure 7.13. Energy recovery by intercooling may be considerable at higher compression ratios (Demirel 2018a, 2021). Residential energy efficiency programs Figure 7.13 shows that the work saved varies with the and valueconservation of intermediate pressure Px. The total work input for a two-stage compression process is:

Wcomp

(γ -1)/ γ   P (γ -1)/γ  γ RT1 use γ RT1  P2   (7.19)  xof  +    MW (γ -1)  P1    MW (γ -1)  Px     

Energy conservation of

where γ = Cp/Cv, R is the gas constant, and MW is the molecular weight. Px is the only variable and can be optimized by differentiation of Eq. (7.19) with respect to Px and setting it equal to zero: other losses. The specific volume of the inlet Px = (P1P2)1/2 or Px/Pand 1 = P2/Px. Therefore, energy conservation will be a maximum when the pressure cost of compressor compression work inidentical a multistage compression ratio across each stage of the is the same with compression work at each stage: Wcomp1 = Wcomp2. P P2

Px

Work saved 2

Polytropic

Polytropic

Intercooling Isothermal

P1

1

Figure 7.13

V

Figure 7.13.  Energy conservation in the compression work by intercooling; the work saved appears between two polytropic compressions starting at the second stage with the pressure Px.

Energy Analysis  213

Example for energy conservation with a throttle valve and a turbine Consider cryogenic liquid methane production at 115 K and 5000 kPa at a rate of 0.3 m3/s. In the plant a throttling valve reduces the pressure of liquid methane to 1000 kPa. Replacing the throttling valves with a turbine produces power and hence conserves electricity. Using the data in Table 7.6 for the properties of liquid methane, we can estimate the power produced and the savings in electricity usage per year if the turbine operates 360 day per year with a unit cost of electricity at 0.09 $/kWhr (Çengel and Boles 2019, Demirel 2018a, 2021). Assumptions: steady state and reversible operation; adiabatic turbine, methane behaves as an ideal gas; kinetic and potential energies are negligible. The following data are from the property tables for methane. Turbine input (State 1): P1 = 5000 kPa, T1 = 115 K,

Q1 = 0.30 m3/s,

H1 = 232.3 kJ/kg,

ρ1 = 422.15 kg/m3

Turbine output (State 2): P2 = 1000 kPa, T1 = 110 K, H2 = 209.0 kJ/kg Unit cost of electricity = $0.09 kWhr; Mass flow rate: 422.15 kg/m3 (0.3 m3/s) = 126.6 kg/s Power produced: . . Wout = m(H1 – H2) = 126.6 kg/s (232.5 – 209.0)kJ/kg = 2949.8 kW Annual power production: . Wout Δt = (2949.8 kW) (360) (24)h/year = 25,486,100 kWhr/year Saving in electricity usage: (25,486,100 kWhr/year)($0.1 kWhr) = $2,549/year Table 7.6.  Cryogenic manufacturing plant data. T, K

P, kPa

H, kJ/kg

S, kJ/kgK

Cp, kJ/kgK

ρ, kg/m3

110

1000

209.0

4.875

3.471

425.8

110

2000

210.5

4.867

3.460

426.6 429.1

110

5000

215.0

4.844

3.432

120

1000

244.1

5.180

3.543

411.0

120

2000

245.4

5.171

3.528

412.0

120

5000

249.6

5.145

3.486

415.2

Energy conservation in the manufacturing sector The manufacturing sector, including pulp/paper, iron/steel, non-metallic mineral, chemical/ pharmaceutical, non-ferrous metal, petroleum refinery, food/beverage, and machinery activities, accounts for considerable energy use. Industrial energy use also includes non-manufacturing activities, like agriculture, construction, mining, and wastewater treatment (Chan and Kantamaneni 2015, Demirel 2018a). Table 7.7 presents improvements after possible energy conservation measures (ECMs) for various energy intensive end uses in the industrial sector. The average industrial energy saving potential is approximately 20–23% of final energy consumption based on application of ECMs currently available. Further reduction with PI requires innovative technologies. Table 7.8 provides a summary of the economical ECMs satisfying the payback periods of 2 and 5 years along with projected energy saving impact across the sector groups. Simple payback periods are considered. High efficiency motors, variable speed drives, economizers, leak prevention, and reducing pressure drop are cost-effective measures. Table 7.9 shows possible ECMs for energy-intensive manufacturing sectors. The ECMs can lead to efficient energy usage and hence reduced GHG emissions (Demirel 2018a).

214  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence Table 7.7.  Possible energy conserving measures for various end uses (Demirel 2018a). End use

Energy efficiency improvement opportunity

Chillers/compressors

High efficiency and optimized systems, floating head pressure controls, efficient refrigeration control systems, optimized chilled water and/or condenser temperature and pressure, preventative refrigeration/cooling systems, variable speed drives on chiller compressors, compressor heat recovery, compression ratio optimization

Indirect heating (boilers)

Boiler sizing and load management, advanced boiler controls, high efficiency burners, economizers, process heat recovery, automated blowdown control, condensate return, steam trap survey and repair, deaerator vent loss minimization, boiler water treatment, insulation, preventative maintenance, condensate recovery, efficient boiler system, flue gas monitoring

Pumps/fans/blowers

High efficiency motors, impeller trimming, optimization, control with adjustable speed driver (ASD), preventative maintenance, optimized duct design to improve efficiency, premium efficiency control, synchronous belts (fans), preventative fan maintenance

Motors

Efficiency control with adjustable speed drive (ASD) motors

Machine drive

High efficiency and correctly sized motors, optimized motor control, synchronous belts, preventative motor maintenance

Ventilation

Optimization, high efficiency and demand-controlled ventilation control with variable speed drive (VSD)

Packaged heating ventilation and air conditioning (HVAC)

Seasonal temperature settings adjustments, ventilation heat recovery, automated temperature control, reduced temperature settings, destratification fans, warehouse loading dock seals, air curtains, preventative packaged HVAC maintenance

Lighting

High efficiency lighting design and controls, on/off timer settings, occupancy sensors

Other systems and operations

Sub and interval metering, integrated control systems, heat exchanger dry-type transformers, electricity demand management control systems, process integration, pinch analysis

Table 7.8. Some economic energy conserving potentials that are more than 5% across the sector groups (Chan and Kantamaneni 2015). Energy conserving measures

Percentage of total energy saving potential by 2030 [%]

ECMs with < 2-year payback period: Integrated control system

17.3

Sub-metering and interval metering

13.8

ECMs with 2–5-year payback period: Efficiency controls with automatic speed drives (pumps, fans, motors)

5.7

Table 7.10 shows the projected percentages of technical improvements with energy conservation in various industrial sectors in years 2030 and 2050 over business as usual (BAU) cases. Machinery, food and beverage sectors are the candidates for the highest possible improvements with energy conservation measures.

7.3  Energy Conversion Energy comes in numerous forms, including fossil fuels, fossil fuel derivatives (e.g., gasoline, diesel fuel), uranium, electricity, work, thermal energy, and electromagnetic radiation. In discussing energy conversion, it is useful first to consider energy forms, sources, and carriers (Demirel 2021): • Energy sources (or primary energy) are found in the environment. Some are finite quantities (e.g., fossil fuels) while others are renewable. The raw forms of energy sources are often processed to facilitate their use. • Energy carriers (or energy currencies) are the energy forms that we distribute, store, and utilize, and include both energy sources and processed energy forms (e.g., electricity). Energy carriers

Energy Analysis  215 Table 7.9.  Manufacturing sector specific energy conservation measures (Chan and Kantamaneni 2015). Industry

Specific energy conservation measures

Iron/steel

State-of-the-art power plant, coke dry quenching, basic oxygen furnace waste heat and gas recovery

Chemicals/ pharmaceuticals

Distillation column optimization, improved controls, energy efficiency, catalysts, optimized heating in furnace, waste heat recovery, advanced process operation, membranes and other process developments, novel separation processes, improved naphtha cracking technologies, inter-plant and process integration

Pulp and paper

More efficient thermomechanical pulping refiner with heat recovery, efficient screening of recovered fibers, paper process refiners, vacuum systems for dewatering, thermo compressors, combined heat and power, and heat recovery

Non-metallic minerals

Replacement and retrofit of furnace/kiln/dryer with optimized design, recovery of excess heat from kilns, conversion to reciprocating grate cooler for clinker making in rotary kilns, using high efficiency equipment, more efficient thermal energy consumption, low temperature heat recovery, improved design for more efficient manufacturing, and advanced oxyfuel combustion technologies

Non-ferrous metals

Optimized heating operating practices, waste heat recovery, selection of optimal furnace design, improvements to alumina production from bauxite, prevention and minimization of salt slag, use clean scrap, inert anode technology

Petroleum refineries

Optimization of distillation columns, advanced distillation column design, heat integration, combined heat and power, integrated gasification combined cycle, advanced maintenance and control systems, catalytic reforming, improved water treatment systems, operation and design, improved catalysts, novel hydrogen production and desulphurization technologies

Food/beverage

Increased combined heat and power (CHP) use, adsorption chillers and trigeneration to meet cooling requirements, fuel switching, optimized distillation, drying, evaporation, refrigeration, improved energy efficiency, cleaning, washing, and sterilizing

Machinery

High efficiency grinding for mechanical pulp, enzymatic pre-treatment for thermomechanical processing (TMP) refiner, black liquor gasification, high efficiency process equipment (electrical), lean manufacturing system, optimized process re-design, optimized techniques for efficient equipment operation

Table 7.10.  Technical potentials in various manufacturing sectors in percentages of million tonnes of oil equivalent (MTOE) over business as usual with energy conserving measures (Chan and Kantamaneni 2015).

Iron/steel

2030 BAU*

Technical potential improvement (%)

2050 BAU*

Technical potential improvement (%)

67.5

16.3

72.8

18.9

Chemicals/pharmaceuticals

66.4

16.5

80.1

17.8

Pulp and paper

37.3

19.0

32.9

17.0

Non-metallic minerals

36.9

19.0

36.1

18.0

Non-ferrous metals

8.6

22.0

7.8

21.0

Petroleum refineries

42.5

25.0

36.7

8.3

Food/beverage

26.4

26.0

23.5

23.0

Machinery

19.8

27.0

19.0

25.0

* BAU: Business as usual

should be distinguished from energy sources. The latter are the original resource from which an energy carrier is produced. Confusion can result here (e.g., hydrogen is an energy carrier not an energy source, even though it is sometimes referred to as one). Newer, more innovative, and less common energy-conversion technologies include: • Fuel cells • Solar photovoltaics and solar thermal energy

216  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence • Combined-cycle systems • Biofuel use and conversion systems. Green building Green building refers to a structure and using process that is environmentally responsible and resource-efficient throughout a building’s life cycle: from siting to design, construction, operation, maintenance, renovation, and demolition (IRENA 2020b). This includes: • Efficiently using energy, water, and other resources. • Protecting occupant health and improving employee productivity. • Reducing waste, pollution, and environmental degradation. Rebound effect Many energy efficiency improvements may not reduce energy consumption by the amount predicted by simple engineering models. This is because they sometimes make energy services cheaper, and so consumption of those services sometimes increases. This is known as the rebound effect, may range from 5% to 40%. A rebound effect of 20% implies that improvements in energy efficiency should achieve 80% of the initially predicted reduction in energy consumption (Demirel 2021). Fuel cells A fuel cell oxidizes a fuel, such as hydrogen or methane, electrochemically to produce electric power. It consists of two electrodes separated by an electrolyte. The fuel and oxygen are continuously fed into the cell and the products of reaction are withdrawn continuously. The type of electrolyte characterizes the type of fuel cell (Ehyaei and Rosen 2019, Haghghi et al. 2019, Mahmoudi et al. 2019). When the electrolyte is acidic, the half-cell reactions occurring at the hydrogen electrode (anode) and at the oxygen electrode (cathode) are: (7.20) H 2 → 2H + + 2e− (anode)

1 O 2 + 2e− + 2H + → H 2 O(g) (cathode) 2

(7.21)

The sum of the half-cell reaction is the overall reaction taking place at the fuel cell:

1 H 2 + O2 → H 2 O(g) (7.22) 2 A thin solid polymer known as proton exchange membrane serves as an acid electrolyte in the hydrogen/oxygen fuel cell. For each mole of hydrogen consumed, 2 moles of electrons pass to the external circuit. Therefore, the electrical energy (work) is the product of the charge transferred and the voltage V of the cell: We = –2FV = ΔG (7.23) where F is Faraday’s constant (F = 96,485 coulomb/mol) and ∆G is the Gibbs free energy. The electric work of a reversible and isothermal fuel cell can be written as: We = ΔH – q = ΔG (7.24) Example: Fuel cell Consider a hydrogen/oxygen fuel cell operating at 20°C and 1 bar with pure hydrogen and oxygen as reactants and water vapor as the product. The standard heat of formation and Gibbs free energy for water are: o ΔH = ΔH f,H2O = −241,818 J/mol

(7.25)

Energy Analysis  217 o = −228,572 J/mol ΔG = ΔGf,H2O

(7.26)

Therefore, for the hydrogen/oxygen fuel cell, the electric work and the voltage are: We = −228,572 J/mol and V =

−∆G = 1.184 volts 2F

(7.27)

Using the air instead of pure oxygen in a reversible and isothermal fuel cell, we have: We = −226,638 J/mol and V = 1.174 volts (hydrogen/air fuel cell)

(7.28)

In practice, the operating voltage of hydrogen/oxygen fuel cell is around 0.6−0.7 volts, because of internal irreversibilities, which reduce the electric work produced and increase heat transfer to the surroundings. Fuel cells are very efficient, but expensive. Small fuel cells can power electric cars. Large fuel cells can provide electricity in remote places with no power lines (Demirel 2021).

7.3.1  Energy Efficiency Energy conservation and recovery reduce energy consumption and increase energy efficiency. Some EC issues are (Rosen et al. 2008, Rosen 2013a, Chan and Kantamaneni 2015, Ptasinski 2016, Demirel 2018a): • The use of telecommuting by major corporations. • Electric motors consume more than 60% of all electrical energy generated and are responsible for the loss of 10 to 20% of all electricity converted to mechanical energy. • Consumers are often poorly informed of the savings of energy efficient products. • Technology needs to be able to change behavioral patterns by allowing energy users, business and residential to see the impact their energy use. The heat recovery steam generator (HRSG) is a steam boiler that uses hot exhaust gases from gas turbines or reciprocating engines in a CHP plant to heat water and generate steam. Greenhouse gas saving for a heat recovery system may be estimated by:

GHGsaving

  FI EI − HL   (7.29)  AFUE × 1000 kg/ton COP × 3600 s/hr 

where HL is the seasonal heat load, FI is the emissions intensity of fuel, which is around 50 kg CO2/GJ for natural gas, 73 for heating oil, 0 for 100% renewable energy, Annual Fuel Utilization Efficiency (AUFE) is the annual fuel utilization efficiency (around 95%), COP is the heat pump coefficient of performance (around 3.2 seasonally adjusted), and EI is the emissions intensity of electricity (approximately 200–800 ton CO2/GWhr). Energy efficiency is the goal of efforts to reduce the amount of energy required to provide products and services. According to the IEA (2019), improved energy efficiency could reduce the world’s energy needs in 2050 by one third and help control global GHG emissions. Energy efficiencies of biofuels The energy efficiency for biomass to biofuel conversion can be determined as follows:  LHV of biofuel   External energy       produced per 1 kg  −  used in 1 kg biomass    Energy efficiency     to biofuel conversion   (7.30)       of biomass = of biomass to biofuel   e 1 kg biomass LHV of th    conversion       used in the convesion 

218  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence Table 7.11.  Energy ratio and EROI for bioethanol production from various feedstocks (Ptasinski 2016). Feedstock

Energy ratio*

EROI

GHG emissions change** (%)

Sugarcane

≈8

0.8–10

−87 to −96

Sugar beets

≈2

Sweet sorghum

≈1

Corn Wheat Lignocellulosic Gasoline

≈ 1.5

−35 to −56 0.84–1.65

≈2 ≈ 2–36

−21 to −38 −19 to −47

0.69–6.61

−37 to −82

≈ 0.8

* Energy from biofuel/fossil energy used in production of biofuel ** Approximate avoided GHG emissions because of the biomass feedstock used in bioethanol production

The energy return on investment (EROI) shows the ratio of energy of a fuel to the total energy invested to produce that fuel. Examples are given for the biofuel bioethanol in Table 7.11. The values of EROI for biofuels are generally lower compared with those of conventional fossil fuels (Hall et al. 2014). The emissions of GHGs from transportation sector are around 25% of global energy related emissions. Combustion of biofuels recycles CO2 captured during photosynthesis. Besides values of EROI, Table 7.11 also shows approximate avoided GHG emissions because of the biomass feedstock used in bioethanol production. Pure ethanol is completely miscible with conventional gasoline. The higher heating values (at 20°C) for ethanol and gasoline are 29.8 MJ/kg and 47.2 MJ/kg, respectively. This suggests that a blend of bioethanol and gasoline will have lower total energy in a vehicle compared to gasoline only. With 10 vol% ethanol the fuel consumption is around 3.3% higher compared with the pure gasoline. Flex-fuel engines can utilize a higher percentage (85 vol%) of ethanol. Since the ethanol is an oxygenated fuel (oxygen: 35 wt%), its combustion is cleaner (Ptasinski 2016, Demirel 2021).

7.3.2  Energy Efficiency Standards Energy efficiency standards (EESs) are based on technoeconomic, and environmental analyses of products consisting of equipment price, energy use, consumer life-cycle cost and payback period, emission impact, and employment impact. These analyses create standards that achieve maximum improvement in energy efficiency that are feasible and lead to considerable energy savings. Thermal efficiencies of residential furnaces and boilers are measured in various ways, including by the annual fuel utilization efficiency (AFUE), which is the ratio of heat output to the total energy consumed over a typical year. AFUE does not account the circulating air and combustion fan power use and the heat losses of the distributing systems of duct or piping. An AFUE of 90% means that 90% of the energy in the fuel becomes heat for the home and the other 10% escapes up the chimney and elsewhere. The Energy Efficiency Ratio (EER) of a cooling device is the ratio of output cooling (often in Btu/hr) to input electrical power (usually in W) at a given operating point. The efficiency of air conditioners is often rated by the Seasonal Energy Efficiency Ratio (SEER), which is the cooling output in Btu during a typical cooling-season divided by the total electric energy input in Whr during the same period. The coefficient of performance (COP) is an instantaneous measure (power divided by power), whereas both EER and SEER are averaged over a duration of time. The time duration considered is several hours of constant conditions for EER, and a full year of typical meteorological and indoor conditions for SEER (Demirel 2021).

Energy Analysis  219

Comparison of energy‑efficiency standards Many countries have mandatory minimum energy efficiency standards and labeling programs beside voluntary standards. Energy Star programs are used in many countries. These programs: (1) establish specifications, testing procedures and verification requirements for various consumer appliances and commercial products, (2) utilize research into residential energy use to promote energy-efficient homes, and (3) develop commercial building energy asset rating programs to assess building energy usage (Demirel 2018b, 2021).

7.4  Energy Storage A closed-loop, long-duration energy-storage system can provide a carbon-neutral path to industrial heating as it generates carbon free discharge heat, if it is charged with renewable energy. For example, a 100-MW system can be designed for 10 h of charging and discharging per day. The discharge cycle generates 100 MW of electricity and 70 MW of 100°C heat over 10 hours. The thermal daily output equates to 700 MWh of usable heat, e.g., for industrial drying applications. Energy storage can provide several advantages for energy systems, such as permitting increased penetration of renewable energy and better economic performance. Also, energy storage is important to electrical systems, allowing for load leveling and peak shaving, frequency regulation, damping energy oscillations, and improving power quality and reliability (Demirel and Ozturk 2006, Koohi-Fayegh and Rosen 2017, Demirel 2021, Dincer and Rosen 2021b). Energy storage systems can be categorized based on storage period, e.g., short-term storage (hours to days), medium-term (days to weeks) and long-term (months). A Ragone plot displays storage characteristics differently, in terms of specific energy and specific power. Selected types of energy storage are considered here, based mainly on commonality of usage. Much of the information presented here is drawn from Koohi-Fayegh and Rosen (2020) and Rosen (2012b, 2015).

7.4.1  Energy Storage Types Electrical energy can be stored electrochemically in batteries and capacitors. Batteries are mature energy storage devices with high energy densities and high voltages. Batteries can store up to 30 times more charge per unit mass than supercapacitors. This high energy density is achieved by storing charge in the bulk of a material. However, supercapacitors can deliver up to thousands of times the power of a battery of the same mass as they only store energy by surface adsorption reactions of charged species on an electrode material. Electrochemical capacitors can be cycled more than batteries. Various types exist including lithium-ion (Li-ion), sodium-sulphur (NaS), nickel-cadmium (NiCd), lead acid (Pb-acid), lead-carbon batteries, and flow batteries. Among the various battery types, lithium batteries are playing an increasingly important role in electrical energy storage because of their high specific energy (energy per unit weight) and energy density (energy per unit volume). A charged Li-air battery provides an energy source for electric vehicles rivalling that of gasoline in terms of usable energy density. Capacitors store and deliver energy electrochemically, and can be classified as electrostatic capacitors, electrolytic capacitors, and electrochemical capacitors. Among these three types, electrochemical capacitors, also called supercapacitors or ultracapacitors (UCs), have the greatest capacitance per unit volume due to having a porous electrode structure (Rosen 2012b, 2015, Koohi-Fayegh and Rosen 2020, Demirel 2021). Thermal energy storage Thermal energy storage (TES) can be categorized as sensible, latent and thermochemical. Sensible TES stores energy by raising the temperature of a medium (e.g., hot water storage, underground storage and rock filled storage). Latent heat storage involves changing the phase of a storage material (phase change material) and thus is an isothermal storage process and typically is more

220  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence compact. Typical phase change materials (PCMs) include paraffin waxes, esters, fatty acids and salt hydrates, eutectic salts, and water. Thermal energy storage is examined extensively by Dincer and Rosen (2021b). A thermal energy storage (TES) system may incorporate a heat pump in charging with renewable energy running a charging turbine, which sends hot air through a compressor into a heat exchanger, transferring the air’s thermal energy to molten salt in a hot tank, while the cooled media flows between a pair of coolant tanks. During discharging, the temperature difference between the system’s tanks is converted to thermal energy, and hot air runs through a second turbine, sending electricity back to the grid in a closed-loop Brayton cycle (Demirel and Ozturk 2006, Anderson et al. 2021, Demirel 2021). Thermochemical energy storage Thermochemical energy storage systems utilize chemical reactions that require or release thermal energy. They have three operating stages: endothermic dissociation, storage of reaction products, and exothermic reaction of the dissociated products. The final step recreates the initial materials, allowing the process to cycle. Thermochemical energy storage systems can be classified in various ways. Thermochemical energy storage systems exhibit higher storage densities than sensible and latent TES systems, making them more compact. Suitable materials or combinations of materials are needed that store energy with low heat loss and release it readily when it is needed (Ozturk and Demirel 2004, Demirel 2021, Nazari et al. 2021). Flywheel energy storage Flywheel energy storage, or kinetic energy storage, facilitates smooth operation of machines and provides high power and energy density. In flywheels, kinetic energy is transferred in and out of the flywheel with an electric machine acting as a motor or generator depending on the charge/discharge mode. Charging energy is input to the rotating mass of a flywheel and stored as kinetic energy. This stored energy can be released as electric energy on demand. The rotating mass is supported by magnetic bearings which operate in a vacuum to eliminate frictional losses during long-term storage. Achieving high rotational velocity, with high power density, in flywheels is desirable since the energy stored is proportional to the square of the velocity but only linearly proportional to the mass (Demirel 2021). Compressed air energy storage Compressed air energy storage (CAES) involves air compression and storage in a large vessel like an underground cavern. There are two types of CAES: diabatic and adiabatic. The thermal energy resulting from the charging compression process is dissipated in the diabatic type and needs to be provided during discharging but is retained in a thermal storage in the adiabatic type for use during discharging (Demirel 2021). Pumped energy storage Pumped hydro energy storage (PHES) stores electric energy in the form of hydraulic potential energy by moving water from a water body at a low elevation through a pipe to a higher water reservoir. Locations are needed for PHES with a difference in elevation and access to water. Disadvantages of pumped hydroelectricity storage are large unit sizes as well as topographic and environmental limitations (Demirel 2021). Magnetic energy storage Superconducting magnetic energy storage (SMES) can be accomplished using a large superconducting coil which has almost no electrical resistance near absolute zero temperature and is capable of storing electric energy in the magnetic field generated by dc current flowing through it. The superconducting coil is kept at a cryogenic temperature by using liquid helium or nitrogen

Energy Analysis  221

vessels. Some energy losses are associated with the cooling system that maintains the cryogenic temperature, but energy losses in the coil are almost zero because superconductors offer no resistance to electron flow. SMES coils can discharge large amounts of power almost instantaneously and can undergo an unlimited number of charging and discharging cycles at high efficiency (Demirel 2021). Chemical and hydrogen energy storage A reversible chemical reaction that consumes a large amount of energy may be considered for storing energy. Chemical energy storage systems are sometimes classified according to the energy they consume, e.g., as electrochemical energy storage when they consume electrical energy, and as thermochemical energy storage when they consume thermal energy. In hydrogen energy storage, hydrogen is produced, stored for a period of time, and then oxidized or otherwise chemically reacted to recover the input energy (Matzen et al. 2015a,b, Demirel 2021). One common method of hydrogen production is by splitting water, which can be accomplished electrochemically or thermochemically. The energy required for this process can be provided from fossil fuels and renewable or other energy sources. The storage of hydrogen is a substantial challenge, especially for automotive applications. Hydrogen has a low energy density on a volume basis compared to the other fuels, requiring a much larger fuel tank for a vehicle operating on hydrogen rather than petrol/diesel. Hydrogen can be stored in its pure form as a compressed gas or as a cryogenic liquid or in a mixed-phase (hydrogen slush). Studies on hydrogen storage have been reported, e.g., transient energy and exergy analyses were reported by Al-Zareer et al. (2018) for a multistage hydrogen compression and storage system.

7.4.2  Energy Storage Applications Energy storage applications are broad and continuously expanding, often necessitating the design of versatile energy storage and energy source systems with a wide range of energy and power densities. Energy storage systems have varied applications. Thermal energy storages, both sensible and latent using PCMs, are useful in solar water heating, solar air heating, solar cooking, solar greenhouses, space heating and cooling in buildings, off-peak electricity storage, and waste heat recovery (Ozturk and Demirel 2004, Demirel 2021). Flywheels are useful for efficiency improvement and pulse power transfer for hybrid electric vehicles, and for assurance of power quality. The use of energy storage systems in utility networks has become increasingly important and focused on as more storage options become available. Energy storage devices can help balance customer demand and generation. Intermittent power generation, such as that provided by many renewable energy sources, results in power instability, which can damage grid equipment such as generators and motors. Regarding district energy utilities, it is noted that thermal storage can be effective. For instance, Anderson et al. (2021) have improved the sustainability of a district cooling system by adjusting cold thermal storage and chiller operation. Renewable energy use is growing rapidly, especially in the electricity sector. However, the variability of these resources creates technical and economic challenges for their operation and use when integrated on a large scale. An important means of addressing the intermittency of renewable energy sources is energy storage. A higher penetration of renewable energy generation is typically achieved with storage, as it permits excess energy produced from renewable energy sources to be stored and dispatched later when needed. As an example, a hybrid system for renewable energy including battery and hydrogen storage was assessed and optimized by Zhang et al. (2018). Energy storage can play an important role in buildings and communities. With increasing interest in net-zero energy buildings and communities, energy storage is increasingly important, as it helps facilitate the integration of renewable energy into buildings. Thermal energy storage is a relatively common storage technology for buildings and communities. It can shift electrical loads from peak to off-peak hours, a demand-side management technique. The optimization of a residential building photovoltaics and battery storage was investigated by Bingham et al. (2019).

222  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence Meanwhile the optimization of seasonal storage for community-level energy systems was examined by Koohi-Fayegh and Rosen (2017). Developments in transportation have raised the need for energy storage. Compared to conventional transportation technologies, hybrid electric vehicles use onboard energy-storage systems such as batteries and hydrogen storage tanks for fuel cells. The requirements for the energy storage devices used in vehicles are high power density for fast discharge of power, especially when accelerating, large cycling capability, high efficiency, easy control and regenerative braking capacity. The primary energy-storage devices used in electric ground vehicles are batteries. Also, there is a need to store hydrogen onboard vehicles at high volumetric and gravimetric densities when using hydrogen as a fuel (Demirel 2021).

7.5  Energy Economics Heavy industries such as refineries use large quantities of hydrogen in their manufacturing processes. Replacing the current grey hydrogen with green hydrogen can help these industries lower their CO2 footprint. But first, green hydrogen must become cost competitive with fossil-based hydrogen. This has the potential to play an important role in the development of a hydrogen economy. Hydrogen can play a critical role in decarbonizing the power industry and transport sectors, especially those that are hard to electrify or expensive to electrify. The development of businesses in emerging technologies such as hydrogen and carbon capture use and storage is an integral part of energy sector strategy of transforming to an integrated energy sector. For example, green H2 can power a stationary fuel cell to provide clean, reliable power (Demirel and Ozturk 2006, Rosen 2011, Demirel 2018b, 2021, Jenkins 2019).

7.6  Energy Analysis For an open steady state system energy is conserved and energy balance can be written as:  + Q + W S ) − ∑ (mh  + Q + WS ) = 0 (7.31) ∑ (mh out in . . where m is the mass flow rate, Ws is the shaft work rate, h is the specific enthalpy, and Q is the heat flow rate. Energy analysis consists of total net power efficiency indicators and the environmental consequences. To measure all energy flows in an economy, energy analysis can be used, or the quantities can be converted from energy to exergy, which indicates of the available energy. Energy counts the exploitation of natural resources and the direct and indirect solar energy requirements of biofuel production, capturing the contributions of nature in economic evaluations. Therefore, intensification in energy analysis can have a large impact on sustainability (Demirel 2004a,b, 2012, 2021, Dincer and Rosen 2021a, Demirel 2021). Minimum approach temperature Figure 7.14 shows the minimum approach temperature ∆Tmin between hot and cold process streams, which is a key design variable for heat exchanger design and optimization. ∆Tmin has an impact on lost work associated with the heat transfer. The net lost work (LW) for a heat flow of Q between a high temperature T1 and a low temperature T2 can be expressed as:

 T   T   T −T   ∆T  Lost work = 1 − o  Q + 1 − o  (−Q) =  1 2  QTo =   QTo (7.32)  T1   T2   T2T1   T2T1  Here To is the absolute temperature of the environment. Equation (7.34) shows that, for a given heat load, lost work is directly proportional to ∆Tmin and should be optimized to control the amount of lost work. The hot and cold process streams can only exchange heat up to a minimum allowable value of ∆Tmin. Increased values of ∆Tmin lead to lower available recoverable heat and

Energy Analysis  223 T Hot

total cost Cost energy cost

Minimum approach temperature

capital cost

Cold L

(a)

∆Tmin

∆T

(b)

hotprocess and cold process L of Figure 7.14.  (a) Minimum approach temperature ∆Tmin ∆T between hot and cold streams with Lstreams showing with the length min between heat exchanger: (b) impact of ∆Tmin on trade-off between the capital cost and energy cost. ∆Tmin on trade-

hence increase the energy cost of utilities, while decreasing the capital cost of heat exchangers (Figure 7.14b) (Demirel 2021).

7.6.1  Energy Targets Energy targets are the minimum amounts of utilities necessary to satisfy the process stream requirement for energy economics based on practical near minimum thermodynamic condition achieved after operation with less process irreversibility (Dhole and Linnhoff 1993, Demirel 2006a,b, 2013a,b, Demirel and Gerbaud 2019). The energy target values are estimated based on utility load allocation method and pinch analysis that are briefly discussed below. Utility load allocation method Load practically refers to the recoverable heat for a value of ∆Tmin as seen in Figure 7.15a. In the utility load allocation method, the composite curves are used to allocate utilities. In this method, it is assumed that a utility is cheaper based on its temperature not based on its cost and expensive utilities and number of heat exchanger units at a value for ∆Tmin are minimized. Retrofits of a plant search for improvements (process intensifications) that involve modifying, relocating, or adding heat exchangers and help bring the plant closer to its energy target. For example, modifying heat exchangers involves increasing area of existing exchangers to increase heat recovery and decrease utility need. This leads to an optimized total cost consisting of capital cost and energy cost. This would be based on a tradeoff between saving energy and investment of additional capital cost with constraints of ∆Tmin and maximum extra area (Demirel and Gerbaud 2019). Pinch analysis Pinch analysis produces temperature-heat diagrams called composite curves (Figure 7.15) and grand composite curves at a pinch point and pinch temperature. The pinch point occurs at the minimum temperatures difference ∆Tmin between of hot and cold streams with the same heat flows. Composite curves and pinch temperature represent the thermal characteristics of hot and cold process streams, as well as the recoverable amount of heat called the load, as seen in Figure 7.15. Composite curves are plots of temperatures against the amount of heat Q transferred: . Q = mCp(ΔT) (7.33)

224  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence Hot utility

T

Minimum hot utiliy Hot utility

Hot composite curve Pinch point

Investment cost Size

∆Tmin

Cold composite curve

Cold utility

Optimum

Hot utility

Cold utility ∆Tmin

Recoverable heat

Cold utility

Q

Minimum cold utility

(a)

Operating cost Exergy loss

(b)

Figure 7.15.  (a) Hot and cold composite curves with the pinch point and minimum hot and cold utilities, (b) impact of

Figure 7.15. (a) approach Hot andtemperature cold composite with the pinch(Demirel point and minimum hotGerbaud and cold utilities, minimum ∆Tmin. oncurves the utility requirements 2018a, Demirel and 2019). (b) impact of minimum approach temperature ∆Tmin. on the utility requirements (Demirel 2018, Demirel and Gerbaud 2019). Assuming that there is not any phase change and the product of mass flow rate and heat capacity

mCp remains constant, then a plot of T versus Q becomes a straight line with a constant slope .   1  P  :  mC   p

 1 dT =   mC •   p

  dQ (7.34)  • . Many exist to estimate • No coldmodeling utility usemethods above the pinch point. the composite curves when the slope 1/(mCp) is not Phaseheat change may occur when thean value of ∆Ttemperature is large. Composite enable engineers •constant. No process exchanger should have approach less thancurves the specified ΔTmin. to identify the minimum amount of hot and cold utilities and their temperatures, as well as the • Minimize the number of heat exchangers that are needed for recoverable heat. design of a heat exchanger network system to recover available heat (Turton et al. 2018, Demirel Table Optimum values of ∆Tmin and7.12. Gerbaud 2019). Industrial sector activity for a feasible ∆Tmin, and ºC near optimal heat exchanger network are as Pinch technology guidelines Oil refining follows: Petrochemical

• No heatChemical transfer across the pinch point. Low • No hot utilitytemperature use belowprocesses the pinch point. • No cold utility use above the pinch point. • No process heat exchanger should have an approach temperature less than the specified ΔTmin. • Minimize the number of heat exchangers that are needed for recoverable heat. required at a value of ∆Tmin. Heat7.12). exchanger network system (Table ∆Tmin heat exchanger network system design matches the available from hot of of An the optimum temperature level to maintain the same rate(HENS) of lost work. This is the reason for veryheat small values streams with required heat by the cold process streams and hence minimizes both the hot ∆Tprocess for the cryogenic processes. min and cold utilities required at a value of ∆Tmin. Selection of ∆Tmin value has implications for both HENS design helps recover energy and When reducethe capital cost withlevels a possible a minimum numberregion of heat capital and energy costs (Table 7.12). temperature move into the cryogenic exchangers NHx,decrease besquare estimated by:temperature level to maintain the same rate of lost work. min that may ∆Tmin must as the of the This is the reason for very small values of ∆Tmin for the cryogenic processes.

Energy Analysis  225 Table 7.12.  Optimum values of ∆Tmin for various industrial sectors according to heuristics (Turton et al. 2018). Industrial sector activity

∆Tmin, ºC

Oil refining

20–40

Petrochemical

10–20

1

Chemical

10–20

Low temperature processes

3–5

hot and cold utilities, respectively. The temperature thea ppossible a minimum number HENS design helps recover energy and reduce capital cost at with of heat exchangers NHx, min that may be estimated by: N Hx,min = N Hs + NCs + N HU + NCU − 1 (7.35)

where N ofhydrodealkylation hot and cold streamsprocess respectively, and NHU and NCU are the Energy fornumber toluene Hs and Ntargets Cs are the numberFigure of hot and cold utilities, temperature at the pinch point defines theprocess. minimum 7.16 shows the respectively. process flowThe diagram for toluene hydrodealkylation drivingkg/hr forceofhence the minimum to irreversibility in aatheat hydrogen at 21 oC, entropy 38.7 atmproduction reacts withdue 11,442.7 kg/hr toluene 24 oexchanger C, networkkg/hr for exchanging heat1492.1 between hot methane, and cold process streams (Demirel and reaction: Gerbaud 2019). benzene and kg/hr according to the following Energy targets for toluene hydrodealkylation process C7H8 + H2  C6H6 + CH4 Figure Toluene 7.16 shows the process flow diagram+ for toluene hydrodealkylation process. In the process, + Hydrogen Benzene Methane 357.3 kg/hr of hydrogen at 21°C, 38.7 atm reacts with 11,442.7 kg/hr toluene at 24°C, 38.7 atm to produceIn6969.5 kg/hr benzene kg/hr methane, according to the following reaction: the side reaction 2%and of 1492.1 the benzene is converted to 143.2 kg/hr biphenyl C7H8 +the H2 reaction: → C6H6 + CH4 75% conversion of toluene Toluene + Hydrogen → Benzene + Methane

(7.36)

2C6H6  C12H10 + H2

In 2Benzene the side reaction 2% of +theHydrogen benzene is converted to 143.2 kg/hr biphenyl and 1.86 kg/hr  Biphenyl hydrogen by the reaction:

TheC12 process Redlich-Kwong2C6H6 → H10 + His2 simulated by 2%using conversion of benzene 2Benzene → Biphenyl + Hydrogen H2-FEED

GAS-RECY

C1

(7.37)

PURGE S1

1

17

M1

TOL-FEED

16

HX1

H2

6

H1

F1

7

2

TOL-RECY

P1

5

3

10

R1

9

S2

8 4

11

M2 FUEL

P2 BENZENE D3 15

D2 12

14

C12-PURG

Figure 7.16.  Process flow diagram for toluene hydrodealkylation.

D1

226  Sustainable Process Intensification, room Engineering: for improvements (over 50%) Energy Analysis, Artificial Intelligence The process is simulated by using Redlich-Kwong-Soave equation of state consisting of energy intensive units of compressor and distillation columns. Individual intensification factors can be Figure 7.17 and Table 7.13 show the energy target for the process, in which the actual rates and the target values of hot and cold utilities. Also, the GHG emissions are compared. Table 7.13 shows di  Fbi for improvements (over 50%) in reducing utility rates and hence in reducing the considerable room IFi =   GHG emissions. Fai potential may point out the considerable recoverable heat using the pinch This analysis. Individual intensification factors can be determined using the equation: di

 F where IFi =  bi   Fai 

(7.38)

where Fbi denotes the actual operation and Fai representing the values of the rates for utility and GHG emissions after operation at energy target values. Table 7.13 shows the value of total intensification p IFtotal = ∏ ( IFi )cifrom: factor as 12.31, determined p

IFtotal = ∏ ( IFi )ci

i =1

(7.39)

Here, di = 1 ci Here, diindependent = 1 as a decrease in factor F is value beneficial. The exponent ci indicates the importance of factors and the of each ci the independent factors and the of of each ci ismay be set one that to the base case for IFtotal improvements. Thevalue value IFtotal betoused as aleads quantified intensification le for improvements. Theoperation value of IF may be used as a quantified intensification level after the for actual toward aiming at operation with the energy targets. total i =1

improvements for actual operation toward aiming at operation with the energy targets.

Figure 7.17. Comparison of the actual and target values of rates of energy and GHG emissions for the toluene hydrodealkylation process shown in Figure 7.16. Table 7.13. Available energy saving and GHG emission reduction potential for the toluene hydrodealkylation process shown in Figure 7.16.

Table 7.13

process shown Possible in Figure 7.16.Possible Factorshydrodealkylation subject to Actual Target intensification FaActual improvements Factors subject to Fb Target improvement, Possible % intensification

1. Heating MW 1.utilities, Heating utilities,8.8 MW

Fb

a

a

p

3.6

4.910.0

5.2 3.6

improvements

5.2 59.1 5.1 51.5 3. Carbon emissions, kg/s 0.575 0.235 0.34059.1 3. Carbon emissions, kg/s 0.575 0.235 0.340 a Total intensification factor for c1 = a 1, c2 = 1, c3 = 1 Total intensification factor for c1 = 1, c2 = 1, c3 = 1 a Total intensification factor for c1 = 1, c2 = 1, c3 = 3 Total intensification factor for c1 = 1, c2 = 1, c3 = 3a 2. Cooling MW 10.0 2. utilities, Cooling utilities, MW

8.8

Fa

5.1 4.9

IFptotal = ∏ ( IFi )ci with di = 1, i = 1,2,3

IFtotal = ∏ ( IFi )cii = with 1 di = 1, i = 1,2,3 i =1

di

F  Possible IFi =  bi  improvement, %  Fai 

59.1 2.4 51.5 2.0 59.1 2.4 12.3 73.3

.

Energy Analysis  227

Table 7.13 shows the values of two total intensifications based on Figure 7.17. Total intensification is 12.3 when the values of di and ci are all equal to one. If the importance of the independent factorshows c3 assumed as of 3 total representing the possible intensification, then 7.17 the value of 7.13 the values two total intensifications based on Figure TableTable 7.13 shows theisvalues of two intensifications based on Figure 7.17 total intensification becomes 73.3, which is around six fold of the previous base case obtained with of c3the as possible one. Table 7.13 demonstrates the priorities of policy makers, or as 3value representing the Therefore, possible as 3 the representing intensintens stakeholders, will affect the quantification of total intensification. Figure 7.18the shows hot of andpolicy cold composite curves, Figure 7.19 shows grand demonstrates thethe priorities of policy makers, or while stakeholders, will affect q demonstrates priorities makers, or stakeholders, will affect thetheqthe composite curve for the toluene hydrodealkylation process shown in Figure 7.16. The composite intensification. intensification. curve and grand composite curve with a pinch temperature at 120°C are obtained for a minimum approach temperature of ∆Tmin = 10°C. As Figure 7.18 shows, an available heat load of 25 MW that can be exchanged between hot and fortoluene the toluene hydrodealkylation process shown in Figure The composite for the hydrodealkylation process shown in Figure 7.16.7.16. The composite curvecurve cold process streams with a suitable heat exchanger network system design. Figures 7.18 and 7.19 o o a pinch temperature 120 are obtained for aofminimum approach curvecurve withthewith a pinch temperature at 120atprocess C areCobtained for aMW minimum approach show that toluene hydrodealkylation requires 4.85 minimum cold utility and o o C. C. 3.6 MW minimum hot utility with a value of ∆Tmin = 10°C and with a pinch temperature at 120°C.

7.18. Hot and cold curve composite curve for toluene hydrodealkylation process FigureFigure 7.18.Hot Hot composite curve for hydrodealkylation toluene hydrodealkylation process with minimum Figure 7.18.  andand coldcold composite for toluene process with minimum approach temperature ∆Tmin = 10°C.

Figure 7.19. Grand composite curvecurve for toluene hydrodealkylation process with minimum approach temperature of Grand composite curve for toluene hydrodealkylation process with minimum FigureFigure 7.19. 7.19. Grand composite for toluene hydrodealkylation process with minimum approach ∆Tmin = 10°C with a pinch temperature at 120°C. o o

228  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence

Figure 7.20.  Heat exchanger network system identifying the number of heat exchangers between available heat and required

20. exchanger n heatHeat for toluene hydrodealkylation process with minimum approach temperature ∆Tmin = 10°C. Here, for color prints, blue = 10 o for toluene hydrodealkylation process withand minimum approach temperature ∆Tmin lines indicate the streams needs to be heated red lines indicate the streams to be cooled down.

Energy Analysis  229

Figure 7.20 shows a base design for heat exchanger network for recovering 25 MW of process heat when ∆Tmin = 10°C with a pinch temperature at 120°C. Such designs are not unique and depend on the number of loops used for each hot and cold process stream and maximum values of heat loads for each heat exchanger used within the heat exchanger network system.

7.6.2  Energy Integration Pinch analysis leads to an optimum integration of energy leading to energy recovery and energy conservation. Energy integration would lead to sustainability in energy intensive systems including heat exchangers, distillation columns, evaporators, condensers, furnaces, and heat pumps with minimum possible utility requirements. An increase in ∆Tmin causes higher energy costs and lower capital costs for energy integration. An optimum ∆Tmin exists where the total annual cost of energy and capital costs is minimized. Table 7.12 shows the optimum values of minimum approach temperature ∆Tmin for various industrial sectors according to heuristics. Once the ∆Tmin is chosen, minimum hot and cold utility requirements can be evaluated from the composite curves. Pinch analysis has been applied widely in industry leading considerable savings (Demirel and Gerbaud 2019, Demirel 2021). needed Heat integration in a biodiesel plant Figure 7.21ainshows the process flow diagrams for biodiesel production processes. The biodiesel as shown Figure 7.16. The effectiveness plant uses methanol, oil, water/NaOH H3as POby4 an as approximate the basic economic feed streams and produces composite curves and exergy loss profiles and as well analysis.

(a: Design 1)

(b: Design 2) Figure 7.21.  Flow diagram for biodiesel plant: (a) base case design (Design 1); (b) retrofitted design (Design 2) (Nguyen and Demirel 2010).

230  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence 24,357 kg/hr and 99.4% pure of fatty acid methyl ester (FAME). The byproducts are 2510 kg/hr glycerol (99.99% pure) and 2402 kg/hr water (99.6% pure). FAME synthesis takes place in a reactor by the transesterification reaction catalyzed by NaOH at 60°C with a byproduct of glycerol (Nguyen and Demirel 2010). In distillation column T202, the FAME is purified. Phosphoric acid (H3PO4) is fed to the reactor R201 where the acid/base neutralization reaction takes place. In distillation column T301, the separation of glycerol from methanol and water occurs. Column T302 is used for purification of methanol. The thermodynamic method of UNIF-DMD is used to determine the vapor properties in columns T202 and T301, while the activity coefficient model NRTL method is used for evaluating the equilibrium and liquid properties in column T302. Figure 7.21b shows the retrofitted process by heat integration with the addition of two heat exchangers (HX1 and HX2) to exchange heat between hot and cold process streams and hence reduce the utilities needed (Nguyen and Demirel 2010). The retrofits consist of feed preheating and reflux ratio modification for distillation columns T202 and T301 and locating optimum feed stage and side reboiling for column T302, as shown in Figure 7.16. The effectiveness of the retrofits has been assessed by the improved column grand composite curves and exergy loss profiles as well as by an approximate economic analysis. Figure 7.22 compares the actual operation with the operation at target values. Table 7.14 shows that the room for improvements would be around 20% after establishing operation with energy targets. Targeted minimum hot and cold utilities, as well as minimum GHG emissions are the factors subject to intensification.

Figure 7.22.  Energy target for the biodiesel production identifying the possible energy savings and reduction in GHG 22 emissions (Nguyen and Demirel 2010). Table 7.14.  Energy targets in a biodiesel process representing possible improvements in energy savings and GHG emissions.

Table 7.14

emissions Factors subject to Factors subject to intensification

Actual

Target

Actual

Possible

Targetsavings Possible intensification savings 1. Heating utilities, kW 812.1 654.9 157.2 1. Heating utilities, kW 812.1 654.9 157.2 2. Cooling utilities, kW 2. Cooling utilities, kW689.9 689.9541.7 541.7 157.2 157.2 3. Carbon emissions, kg/hr 332.8 332.8264.0 264.0 68.8 3. Carbon emissions, kg/hr 68.8

Possible improvements Possible (%)

improvements (%) 19.4 19.4 22.5 22.5 20.6 20.6

7.6.3 Exergy analysis 7.6.3  Exergy Analysis Exergy analysis, a thermodynamic analysis technique based primarily on the Second Law of Thermodynamics, overcomes many ofmeans the weaknesses of energy analysis.processes Exergy analysis provides illuminating alternative of assessing and comparing an an illuminating alternative means of assessing and comparing processes and systems, which is both rational and meaningful. Two key characteristics of exergy analysis are that it provides efficiencies

Energy Analysis  231

that measure how nearly actual performance approaches ideality, and clear identification (relative to energy analysis) of the causes, types and locations of thermodynamic losses. Consequently, exergy analysis can assist in improving and optimizing designs (Ozturk and Demirel 2004, Rosen 2013b, Dincer and Rosen 2015, 2021a). The exergy associated with a quantity provides a quantitative assessment of its quality or usefulness or value. Energy cannot be created or destroyed, but exergy analysis quantifies how it can be degraded in quality. In the limit, energy is completely degraded in quality, i.e., a state in complete equilibrium with the surroundings and hence of no further use for performing tasks, and then its exergy is zero. The exergy of a commodity or flow is the maximum work that can be obtained from it as it passes reversibly to the environmental state, exchanging materials and heat only with the surroundings. Thus, exergy is a measure of the ability to perform work. Exergy analysis exploits this notion and indicates the theoretical limitations governing a system. The analysis also shows that exergy is conserved only in an ideal (reversible) system, and that a real system cannot conserve exergy. Exergy analysis thereby specifies quantitatively limitations by expressing losses in terms of exergy. Only a portion of the input exergy to a system or process can be recovered (Khoshgoftar and Rosen 2018, Ehyaei and Rosen 2019). Exergy is evaluated with respect to a reference environment. The reference environment is a sink and source for heat and materials, and acts as an infinite system. The reference environment is in stable equilibrium, with all parts at rest relative to one another. It experiences only internally reversible processes in which its intensive state remains unaltered (i.e., its temperature, pressure, and the chemical potentials for each of the components present remain constant). No chemical reactions can occur between the environmental components. The intensive properties of the reference environment partly determine the exergy of a flow or system. The exergy of a flow or system is zero when it is in equilibrium with the reference environment, and the exergy of the reference environment is zero (Al-Zareer et al. 2018, Demirel 2012, 2021). The natural environment does not have the theoretical characteristics of a reference environment, since the natural environment is not in equilibrium, its intensive properties exhibit spatial and temporal variations, and many chemical reactions in the natural environment are blocked because the transport mechanisms necessary to reach equilibrium are too slow at ambient conditions. Thus, the exergy of the natural environment is not zero. As a result, models for the reference state seek a compromise between the theoretical requirements of the reference environment and the actual behaviour of the natural environment. Numerous classes of reference-environment models exist (Rosen 2013b). An exergy balance for an open steady state system shows that exergy Ex is not conserved:

    T   T       f + Q 1 − 0  + WS  − ∑  mex  f + Q 1 − 0  += WS  Exdestroyed = Ex ∑  mex loss (7.40)  out   T in  S  TS       . where Ws is the shaft work. Note that the exergy loss rate in this equation only accounts for internal exergy losses, which are in the form of exergy destructions. In an open system, the specific flow exergy exf can be expressed as ex f =h − ho − To ( s − so ) + ∑ [ ∆Gi + RTo ci ln(ci / co,i )] + (1 / 2)v 2 + gz (7.41) i

where ΔGi is the Gibbs free energy change for species i, R is the universal gas constant, ci is the concentration of species i, v is the average velocity, g is the acceleration due to gravity, and z is the elevation. The specific enthalpy h and the specific entropy s are evaluated for the chemical composition of the substance and for the composition of the environment. Specific enthalpy and entropy values for the stream and environment compositions are evaluated for the same temperature and pressure, normally the environment conditions (To, Po). Table 7.15 shows the molar exergy, exi, of various pure species. Table 7.16 shows the molar exergy of separation of species in the reference

232  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence Table 7.15.  Molar exergy, exi, of pure species relative to a reference atmosphere at P0 = 100 kPa, T0 = 25ºC, and 60% RH; exi = µi(T0,p0,1) – µi0(T0,p0,xi0) (Szargut 2005). Constituent

Formula (state)

Molar fraction in ref. atm., xi

Molar exergy, exi (kJ/mol)

Nitrogen

N2(g)

0.7651

0.66

Oxygen

O2(g)

0.2062

3.9

Water

H2O(l)

0.0190

1.3

Ar(g)

0.0094

12

Carbon dioxide

Argon

CO2(g)

0.0003

20

Carbon monoxide

CO(g)

Hydrogen

H2(g)

275 236

Methane

CH4(g)

831

Ethane

C2H6(g)

1500

Ethylene

C2H4(g)

1360

Acethylene

C2H2(g)

1265

Propane

C3H8(g)

2150

n-Butane

C4H10(l)

2800

Carbon (graphite)

C(s)

410

Nitrogen monoxide

NO(g)

89

Nitrogen dioxide

NO2(g)

56

Ammonia

NH3(g)

340

Methanol

CH3OH(l)

720

CH3CH2OH(l)

1400

Ethanol

Table 7.16.  Molar exergy of separation of species in the reference atmosphere at 25ºC and 60% RH (relative humidity) and 100 kPa (Szargut 2005). Constituent N2

Mole fraction, xi

Molar exergy, exi (J/mol)

0.7651

0.66

O2

0.2062

3.9

H2O

0.0190

1.3

Ar

0.0094

12

CO2

0.0003

20

atmosphere at 298 K and 60% RH (relative humidity) and 100 kPa (Demirel 2004b, 2012, Demirel and Gerbaud 2019). Exergy losses represent inefficient use of available energy due to irreversibility and should be reduced where practical via improvements through retrofits or intensification. Smaller exergy losses correlate with less waste and less loss of useful commodities (e.g., electricity). Exergy analysis is an alternative to the more conventional energy analysis, and is a tool based primarily on the thermodynamic quantity exergy (Dincer and Rosen 2021a, Szargut 2005, Rosen et al. 2008). It is somewhat analogous to energy analysis, but fundamentally different. It thus provides a complementary analysis tool. Selected characteristics of exergy and energy methods are contrasted in Table 7.17. The quantity exergy is akin to energy but differs by providing a measure of the usefulness or quality of material or energy quantities. Exergy is based on the conservation of energy and non-conservation of entropy principles and is defined as the maximum work which can be produced

Energy Analysis  233 Table 7.17.  Contrasting exergy and energy methods based on selected characteristics. Exergy analysis

Energy analysis

Reflects energy quality

Neglects energy quality

Applies exergy balances, emphasizing that exergy is destroyed due to irreversibility and not conserved in real processes

Utilizes energy balances, wherein energy is conserved

Always provides efficiencies that measure approach to ideality

Does not necessarily provide efficiencies that measure approach to ideality

Always provides margin for efficiency improvement

Does not generally indicate margin for efficiency improvement

Provides a measure of disequilibrium with environment and potential for environmental and ecological impact

Does not provide a measure of disequilibrium with environment and potential for environmental and ecological impact

Predicts if a process is possible

Does not provide information if a process is possible

Optimization for lowering the exergy loss would lead to considerable improvements

Optimization for lowering the energy loss may not lead to considerable improvements

Identifies the ideal operation limits of heat engines

Does not identify the ideal operation limits of heat engines

by a flow of matter or energy as it comes to equilibrium with a reference environment. The reference environment can be selected for convenience, but it is often chosen to emulate the natural environment (Demirel 2006a,b, 2013b). Exergy analysis identifies thermodynamic losses in an overall process and its steps and consequently is beneficial in the analysis, design and improvement of energy systems and processes. Exergy analysis can reveal whether, and by how much, it is possible to design more efficient energy systems by reducing the inefficiencies. The exergy method thus greatly assists efforts to achieve engineering sustainability. The exergy method is particularly useful for attaining more efficient energy-resource use because it identifies efficiencies that are true measures of the approach to ideal behaviour and enables the locations, types, magnitudes and causes of inefficiencies (both wastes and internal losses) to be determined. Exergy analyses have been reported in numerous areas, e.g., electricity generation, cogeneration, heating, ventilation and air conditioning (HVAC). Exergy analyses have also been reported for chemical and fuel processing, manufacturing, energy storage, transportation, industrial energy use, building energy systems, and many others. Furthermore, exergy methods can be used in optimizing energy systems (Dincer et al. 2017). Several books on exergy analysis, including numerous applications, have been published over the past few decades (Dincer and Rosen 2021a, Dincer et al. 2017). Also, exergy has been worked extensively into many books on thermodynamics (Çengel and Boles 2019, Demirel and Gerbaud 2019). Specific examples of applications are numerous, as exergy analysis has been applied in a wide variety of areas: • Industrial systems such as air separation (Mehrpooya et al. 2018), desalination and reverse osmosis (Farsi and Rosen 2021, Rosen and Farsi 2022), dairy farming processes (Kool et al. 2018), gas pressure boosting (Salimi Delshad et al. 2021), and liquefied natural gas regasification (Mehrpooya et al. 2018). Such exergy analyses allow the industrial systems to achieve higher efficiency, through pointing out areas of significant loss. For instance, exergy methods were applied to separation processes to identify the minimum input work (or other input) of separation, thereby identifying how much extra work is being input in real processes and allowing the margin for improvement to be made clearer. • Renewable energy systems such as those utilizing ground based geothermal energy (Rosen and Koohi-Fayegh 2017, Demirel 2021) and high-temperature geothermal energy (Javanshir et al. 2020), solar thermal energy and solar photovoltaics (Rezaie et al. 2018, Rahnama et al. 2019, Aghbashlo et al. 2020), and biomass energy (Compton et al. 2018, Shahbeig et al. 2022), as well as multi-generation renewable energy systems (Kool et al. 2018). Applications of exergy

234  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence













analysis to renewable energy systems, like applications to nonrenewable energy systems, identify losses and help improve efficiencies. This is at times less beneficial for renewable energy systems since the energy resource may be free, but the higher efficiencies that exergy analysis can help can reduce the sizes and numbers of the components required (e.g., solar collectors or wind turbines) for renewable energy systems. • Hydrogen energy (Al-Zareer et al. 2018, Moharramian et al. 2019, Haghghi et al. 2019, Ehyaei and Rosen 2019, Mahmoudan et al. 2022). Such applications of exergy analyses allow hydrogen production and utilization with higher efficiency and less loss. • District energy (Rezaie et al. 2018, Rosen and Koohi-Fayegh 2016), including district cooling (Anderson et al. 2021). The application of exergy to district energy systems is particularly beneficial as it ensures better matching of thermal energy supplies to thermal energy needs, and avoids using very high quality thermal energy for very low quality demands, thereby boosting overall energy efficiency. • Energy storage (Demirel and Ozturk 2006, Rosen 2012b, Demirel 2021), including thermal energy storage (Dincer and Rosen 2021b, Rosen et al. 2004, Nazari et al. 2021). Exergy analysis is particularly advantageous for thermal energy storage as it identifies the thermodynamic value of the energy stored and avoids attributing excess value to the storage of low quality heat (like energy analysis does). • Buildings (Karimi et al. 2019, Ashrafi Goudarzi et al. 2019), including HVAC (Dincer and Rosen 2015). Exergy applications to HVAC are particularly sensitive since the systems often operate at temperatures close to the environmental condition, compared to industrial systems that often operate at very hot or very cold conditions. Detecting potential efficiency improvements is possible through appropriate exergy analyses of HVAC. • Electricity generation (Hamrang et al. 2021), including nuclear power plants (Seyyedi et al. 2018, Demirel and Gerbaud 2019), gas turbine plants (Yazdi et al. 2020, Seyyedi et al. 2018), combined cycle plants (Manesh and Rosen 2018), fuel cells (Mahmoudi et al. 2019), and organic Rankine cycles (Mahmoudi et al. 2019). Such exergy analyses permit power generation devices to achieve higher efficiency and reduce losses. For instance, exergy methods were applied to provide options for raising the efficiency of an electrical utility by 5% over a five-year time frame. • Polygeneration, including cogeneration and trigeneration (Rosen and Koohi-Fayegh 2016, Mahmoudi et al. 2022) and renewable energy-based polygeneration (Kool et al. 2018). The complexities of polygeneration systems make them difficult to analyse thermodynamically, and in many cases overall efficiencies can not be defined rationally with energy analysis (e.g., for a trigeneration system for heat, cold and electricity). But exergy analysis allows rational and meaningful definitions of efficiency to be attained in such instances. • Countries and sectors of countries as well as the planet (Rosen 2013a,b, 2022). Exergy assessments of such macrosystems are particularly useful in ascertaining the actual thermodynamic performances of countries in terms of meaningful efficiencies and losses. It is often seen that space heating, for instance, has a misleadingly high efficiency on an energy basis, but a much lower efficiency on an exergy basis, mainly because the product energy is relatively low in quality (i.e., heat at about 40°C).

On a broader level, the benefits of exergy methods for energy sustainability primarily relate to enhanced efficiency. But exergy methods can also be applied in other areas beyond thermodynamics. Some examples are: • Exergy methods and economics: Exergy-based economics has become a powerful tool for engineering design, improvement, and optimization (Rosen 2011, Demirel 2013b, Sciubba 2019, Dincer and Rosen 2021a, Hamrang et al. 2021).

Energy Analysis  235

• Exergy methods and environment and ecology: Exergy-based environmental and ecological methods have become advantageous for protecting the environment and ecosystems (Rosen Dincer and On2012a, a broader level, t Rosen 2021a, Sciubba 2019, Nielsen et al. 2020). The use of exergy in environmental fields can improve understanding efficiency. But exergy methods can also be applied of and mitigate environmental impact and help develop better predictors and indicators. As exergy is a measure of potential of a substance are: to cause change, the exergy of an environmental • Exergy methods and economics: Exergy-emission is measure of its potential to change or impact the environment. The exergy and of anoptimization emission is (Rosen zero only whenDemirel it is in equilibrium engineering design, improvement, 2011, 2013, withDincer the environment and thus benign. Exergy analysis has also been investigated as a tool for and Rosen 2021a, Hamrang et al. 2021). •addressing climate change (Rosen 2021). -

become advantageous for protecting the environment ecosystems (Rosen Thehave optimization of energy and other systems can utilizeand exergy analysis, as 2012a, well as Rosen 2021a, Sciubba, 2019, Nielsen et al. 2020). The use of exerg exergoeconomic and exergoenvironmental methods, in a meaningful way (Dincer et al. 2017). improvefor understanding and mitigate environmental and help that polygeneration Exergy analysis a coal-based of power and methanol productionimpact systems shows may save 3.9% and 8.2% energy compared to the individual processes. The total exergy loss measu (Demirel and Gerbaud 2019, Wang and Demirel 2018). increasesenvironmental around 18% inemission the actualisoperation Comparison of electrical heating technology based on exergy: resistance vs heat pump Two types of electrical heating are compared for the two important applications: domestic hot water production and space heating. The four specific cases are illustrated in Figure 7.23. exergoenvironmental These heating technologies have differing characteristics. Specifically, electric resistance heaters have very high energy efficiencies, typically over 98%. A value of 99.5% is considered here for(Demirel the electric heaters. Electric heat pumps have coefficients of performance (COPs) and resistance Gerbaud 2019). for heating usually ranging from 2.5 to 5.5 for air-source units. The coefficient of performance is a measure of theofheat pump energy andbased is dependent on the temperatures of the product Comparison electrical heatingefficiency technology on exergy: resistance vs heat pump heating and the surroundings. A typical value of 3 is considered here, for both domestic hot water Two types of electrical heating are compared for the two important and space heating.

Electrical power

Electrical resistance heater

Electrical power

Electric heat pump

Electrical power

Electrical resistance heater

Electrical power

Electric heat pump

Space heating

Space heating

Domestic hot water heating

Domestic hot water heating

Figure 7.23.  Processes considered for providing electrical space heating and electrical domestic hot water heating. Top row: Figure 7.23 space heating; second row: space heating with an electric heat pump; third row: domestic hot water heating electric resistance space heating; second spacehot heating anwith electric heat pump; third row: with electric resistance espace heating; bottom row:row: domestic water with heating an electric heat pump.

236  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence The space heater converts electricity to heat at a temperature suitable for keeping a room at a comfortable temperature, while the water heater converts electricity to heat at a temperature suitable for domestic hot water. The heating needs at the time are specified. The space heater provides a thermal energy rate of 30 kW to maintain the air temperature at 22ºC, while the domestic hot water heater provides a 10 kW heat rate to maintain a domestic hot water at 55ºC. The external ambient temperature during the heating operations is taken to be 5ºC, and this is considered the reference-environment temperature in the analyses (Rosen and Koohi-Fayegh 2016, 2017, Rosen 2011, 2012a). Energy and exergy rates for the input electricity and product heat are listed in Table 7.18 for space heating and domestic hot water heating using the electrical resistance heater and the heat pump. Also listed is the temperature at which the product heat is delivered for each application, and the energy and exergy efficiencies of the two devices considered for each application. Careful examination of Table 7.18 leads to several important observations, which suggest that useful and practical insights are obtained with exergy analysis: • The exergy efficiencies for domestic hot water heating and for space heating using an electric heat pump are seen to be meaningful and the energy efficiencies misleading. For a heat pump with a COP of 3, domestic hot water heating and space heating are both seen to be achieved using only one third of the electricity required for electric resistance heating. The heat pump exergy efficiencies, at 17% for space heating and 46% for domestic hot water heating, are seen to be much higher than the corresponding efficiencies for electrical resistance heating. • The energy efficiency of 300% seems unintuitive, but it corresponds to a coefficient of performance (COP) for a heat pump of 3. The energy efficiency exceeds 100% since more product heat is delivered than the electrical energy input; the excess delivered product heat is heat extracted from the environment at the ambient temperature. • The energy efficiency of electric resistance heating, at 99.5%, is very high and implies the maximum possible thermodynamic efficiency for electric heating is 100%. But this implication is incorrect, since an energy efficiency of 100% does not correspond to the most efficient heating device possible, as explained in the previous point. Regarding the last bullet, it is pointed out that the sharp difference in energy and exergy perspectives is attributable to the fact that energy analysis ignores the fact that, in electrical resistance heating, high-quality energy (electricity) is used to produce a relatively low-quality product Table 7.18.  Energy and exergy quantities for space and domestic hot water heating using an electrical resistance heater and a heat pump. Quantity

Electrical resistance heater Space heating

Domestic hot water heating

Heat pump Space heating

Domestic hot water heating

Input electricity Energy rate (kW)

30.2

10.1

10.0

3.33

Exergy rate (kW)

30.2

10.1

10.0

3.33

Product heat Temperature (ºC)

22

55

22

55

Energy rate (kW)

30.0

10.0

30.0

10.0

Exergy rate (kW)

1.73

1.52

1.73

1.52

Energy (%)

99.5

99.5

300

300

Exergy (%)

5.7

15.2

17.3

45.8

Efficiency

Energy Analysis  237

(domestic hot water), or an even lower-quality product (warm air). Exergy analysis recognizes this difference in energy qualities, and indicates the exergy of the thermal energy delivered by resistance heating to be 15% of the exergy entering the heater for domestic hot water heating, and to be 6% of the exergy entering the heater for space heating. Thus, the efficiency, based on exergy, of electric resistance heating is found to be about 15% for domestic hot water heating and 6% for space heating (Rosen 2013a,b, 2022). Column targeting tool with exergy analysis Distillation is an important separation system in manufacturing and chemical industries. Heat is the separation agent in distillation columns, making them highly energy intensive. Thus, efforts to reduce or minimize the energy dissipation may lead to major improvements toward process intensification. The column targeting tool (CTT) is based on the practical near-minimum thermodynamic condition representing a practical and nearly ideal column operation (Dhole and Linnhoff 1993). To achieve nearly ideal operation the CTT uses pinch analysis and exergy analysis. Pinch analysis helps integrate heat to reduce the cost of energy, while exergy analysis can identify the level of irreversibility and hence helps control the lost work and energy dissipation (Alhajji and Demirel 2016). Figure 7.24 shows the procedure of application CTT toin the a systematic CTT can help -by-stage calculations, and (iii) capable of of leading qualitative way. and quantitative andfor Linnhoff 1993, Demirel 2004a, 2006a,b, 2013a,b). identify theDhole targets improvements in energy efficiency and reduce thermal energy costs, and

Figure 7.24.  An algorithm for column targeting tool for improved sustainability through energy and exergy analyses.

238  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence hence reduce GHG emission. Various types of modifications are possible, including (Demirel 2006a,b, 2013a):

• • • •

Feed stage location (appropriate placement), Reflux ratio modification (reflux ratio versus number of stages), Feed conditioning (heating or cooling), and Side condensing or reboiling (adding side heater and/or cooler).

The thermal energy analysis capability of the CTT helps reduce the column reboiler and condenser duties and stage exergy losses, and consequently the GHG emissions by using the column grand composite curve (CGCC) and stage exergy loss profiles. As the exergy loss increases, the net heat duty must increase to enable the column to achieve the required separation of light components from heavy components. CGCCs show the inefficiencies introduced through column design and operation, such as mixing, pressure drops, multiple side-products, and side strippers. Using CGCC is significant because it is: (i) a graphical tool to assess the current energy use and flow conditions of distillation operations, (ii) based on the complex and rigorous stage-by-stage calculations, and (iii) capable of leading to the qualitative and quantitative assessments (Dhole and Linnhoff 1993, Demirel 2004a, 2006a,b, 2013a,b). Thermodynamic efficiency For distillation columns, the difference between the exergies of products and feed streams determines the minimum total exergy flow rate (separation power) necessary for a required separation:  Ex = min

 − ∑ mex  (7.42) ∑ mex

out

in

. When Exmin > 0, the thermodynamic efficiency becomes:

 Ex min (7.43)  + loss Exmin

η=  Ex

The denominator in Eq. (7.43) represents the total exergy input rate. Thermodynamic efficiencies before and after the retrofits can quantify the improvements and help in assessing the effectiveness of retrofits and hence the level of intensification (Demirel 2004a, 2013b). Retrofitting by column targeting tool in a biodiesel plant The CTT can be a retrofit tool for lowering cost of operation through modified operating conditions and providing insight into understanding tray/packing capacity limitations. The biodiesel plant shown in Figure 7.21 operates three distillation columns. Figure 7.25 shows the column grand composite curves for column T302 for design 1 and design 2. The CGCC displays the net enthalpies for the actual and ideal operations at each stage, and the theoretical minimum cooling and heating requirements in the temperature range of separation. The area between the actual and the ideal operations in a CGCC should be small in an improved unit (Demirel 2006b). The CGCCs help in identifying the following retrofits: (i) feed location (appropriate placement), (ii) reflux ratio (reflux ratio vs. number of stages), (iii) feed conditioning (heating or cooling), and (iv) side condensing and reboiling. A sharp enthalpy change occurs in Stage-H CGCC on the reboiler side, and it is an indication of a feed that is introduced too high up in the column. Similarly, a sharp enthalpy change will occur in Stage-H CGCC on the condenser side if a feed is introduced too low in the column. Appropriate feed placement not only removes the distortions in the Stage-H CGCC but also reduces the condenser and reboiler duties. Reflux ratio reduction lowers the condenser and reboiler duties and decreases operating costs; however, it will increase the number of stages to preserve the separation and increase capital costs. Side condensing or side reboiling

Energy Analysis  239 16 14 12

Stage

10 8

Design 1 Design 2

6 4 2 0

0

600

1200

1800

2400

3000

, for the base design and retrofits leading to design 2. (kW) provides a considerable saving s from 7492 kWEnthalpy to 3628 Deficit kW, which (a)

Figureand 7.25. T301. Grand column composite curvesthermodynamic and stage exergy loss profiles for base (Design 1) and improved T202 With the increased efficiency, the design columns operate closer todesign ideal (Design 2) for column T302 (Nguyen and Demirel 2010).

is

costs; Side condensing or side reboiling allows heat removal or addition utility (Nguyen and Demirel 2010, 2013). (a)

(b)

(c)

Figure 7.26.  Comparison of the stage-exergy loss profile of base case design (Design 1) and retrofitted design (Design 2): (a) for column T202, (b)- for column T301; (c) for column T302 (Nguyen and Demirel 2010).

allows heat removal or addition to the column using a cheaper cold/hot utility (Nguyen and Demirel 2010, 2013). Figure 7.26 shows the stage exergy loss profiles for the columns operated in the biodiesel losses of production process (Figure 7.21), for the base design and retrofits leading to design 2. After the retrofits, the overall exergy loss decreases from 7492. kW to 3628 kW, which provides a considerable 9 also saving of 51% in the available energy. Process heat integration is used to preheat the two feed streams for distillation columns T202 and T301. With the increased thermodynamic efficiency, the andideal ci operation. columns operate closeri to The assessments of the base case simulation indicate the need for modifications of the T302 in Figure 7.21 for the biodiesel distillation columns and suggest the required retrofits detailed in Table 7.19. For column T202, the retrofits consisting of a feed preheating ratio Improvement reduce the total exergy Designand 1 reflux Design 2 modification (%) loss by 47.3%, while the exergy losses of columns T301 and T302 decrease by 61.1% and 52.9%, Parameter (base case) (retrofitted) respectively. After the retrofits, the overall thermodynamic efficiency increases from 3.9% to . No. of stages 16 16 8.1%. The suggested retrofits reduce the consumption of exergy considerably and prove to be more Feed stage 13 9 o sustainable (Nguyen Table 53.1 7.19 also shows a total intensification factor Feed temperature, C and Demirel 2010, 2013). 53 Reflux 2 1.70 of 2.57ratio based on reductions in condenser duty, reboiler duty and exergy loss when the values of di 9.6 1.1 Condenser -2663.60 and ci are duty, both kW assumed to be one (Demirel 2013a). -2405.80

240  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence Table 7.19.  Configurations of the base and retrofitted designs for column T302 in Figure 7.21 for the biodiesel production processes (Nguyen and Demirel 2010). Parameter

Design 1 (base case)

Design 2 (retrofitted)

Improvement (%)

No. of stages

16

16

.

Feed stage

13

9

Feed temperature, oC

53

53.1

Reflux ratio Condenser duty, kW Distillate rate, kmol/hr

b

2

1.70

p

–2663.60 ci

–2405.80

i =1

90.96

91.17

2881.46

1223.63

IFtotal = ∏ ( IFi )

ate (1400 kW) used by the side reboiler. Condenser temperature, oC 66.75 66.83 di and ci are assumed to be one.

Reboiler duty, kW

Side reboiler stage efinery operation

___

13

Side reboiler duty, kW

___

1400

, as seen Bottoms rate, kmol/hr

F  IFi =  bi   Fai 

9.6

b 2.571.1

8.9a

1.1

in Figure133.18 7.27. These columns 133.26consume large amounts sizeable GHG emissions. The column targeting tool o Reboiler temperature, C 101.98 102.00 elps reduce the utilities required and design an Total exergy loss. kW 151.86 71.48 52.9

2.12

Total intensification factor p

2.57b

b

IFtotal = ∏ ( IFi )ci i =1

di

oil refinery mainly consists of three petrofrac columns as shown in Figure 7.27. A mixed oil

Value includes the heat rate (1400 kW) used by the side reboiler. Values of di and ci are assumed to be one.

a b

Crude oil refinery operation At theindustries same time, bottom columns producesfor 2070.3 bbl/hr separation of Petrochemical and chemical process use the distillation continuous o C. of petroleum products, as seen in Figure 7.27. These columns consume large amounts of energy and hence produce sizeable GHG emissions. The column targeting tool can improve the energy efficiency of the columns, while energy analysis helps reduce the utilities required and design an

Figure 7.27.  Base case process flow diagram. The temperature of the streams are in oC and the values of heat rates (Q) are in 27 MW. PF-STEAM: pre-flash steam; CU-STEAM: crude unit steam; VDU-STM: vacuum o distillation unit steam (Alhajji and Demirel 2015).

Energy Analysis  241

Figure 7.28. and Hotcold and cold composite curveswhen whenthe the minimum minimum approach temperature is 10°C at othe pinch point with a 8 Hot composite curves approach temperature is 10 o pinch temperature at 350°C for the refinery operation shown in Figure 7.27 (Dotted line is for cold composite curve and C for the refinery operation shown in Figure 7.27. continuous line is for hot composite curve).

improved exchanger network system for process heat integration between hot and cold process grandheat composite curve streams. A crude oil shows refinery mainly consistscolumn of three petrofrac columns as shown in Figure 7.27. A that the preflash mixed oil feed consisting of 80% oil with an API gravity of 33.4, and 20% of oil with an API gravity occurring of 28.5 are blended to form a feed with an API gravity of 32.63. A flow of CGCC 5228.4 also bbl/hr of mixed boiler. The crude oil enters the preflash column to produce a mixture of 4102.3 barrel (bbl)/hr that fed to main crude column at 229°C. The crude column produces 387.4 bbl/hr of heavy naphtha, 584.4 bbl/hr of due to kerosene, bbl/hr of diesel, and bbl/hr(Alhajji of automotive gas oil2015, (AGO). At the same time, eating or 716.0 cooling modifications are 470.6 not needed and Demirel 2016). the bottom produces 2070.3 bbl/hr of mixture that enters the vacuum distillation unit (VDU) column at 358°C. The VDU produces 475.3 bbl/hr of light vacuum gas oil (LVGO), 905.3 bbl/hr of heavy vacuum gas oil (HVGO), and 629.5 bbl/hr of residue (Alhajji and Demirel 2015). Figure 7.28 shows the hot and cold composite curves for the refinery operation shown in Figure 7.27 when the minimum approach temperature is 10°C at the pinch point with a pinch temperature of 350°C. The solid line represents the hot composite curve and the dashed line represent the cold composite curve (Alhajji and Demirel 2015). As seen, the refinery has enough hot process streams and a recoverable heat rate of around 20,000 kW. Column grand composite curve Figure 7.29 shows the column grand composite curve (CGCC) in stage-enthalpy deficits for the preflash column. The CGCC shows that the preflash column operation is close to optimum for most of the stages, except the sharp enthalpy change occurring in stage 1 (the condenser side). This requires normally either lowering the feed temperature or moving the feed stage toward reboiler. The CGCC also shows that there is no distance between the pinch point and ordinate and hence there is no need for the reflux ratio modification. Similarly, due to the closeness of the actual and ideal operations in most of the column height, side heating or cooling modifications are not needed (Alhajji and Demirel 2015, 2016).

242  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence PREFLASH Column Grand Composite Curve (Stage-H)

10

PREFLASH Column Grand Composite Curve (Stage-H)

10

9

Ideal Profile Actual Profile Ideal Profile Actual Profile

9

8 8 7

6 Stage

Stage

7

5

6 5

4

4

3

3 2

2 1

1

0

0

1200

1200

2400

2400

3600

3600

4800

4800

6000

6000

7200

7200

8400

9600 10800 12000 13200 14400 15600 16800 18000

8400 12000 13200 14400 15600 16800 18000 Enthalpy9600 Deficit 10800 kW

Enthalpy Deficit kW

Figure 7.29 Stage-enthalpy deficit for the preflash column shown in Figure 7.27 (

Figure Stage-enthalpy deficit for the preflash column shown in Figure 7.27 (Alhajji and( Demirel 2015). 297.29.  Stage-

Exergy7.30 loss profiles Figure for stage 1 (condenser stage) and the the column 30 shows the stage lossatprofiles forof the preflash column. Higher exergy losses Figure 7.30 shows theexergy stage exergy lossbottom profiles for the preflash column. Higher exergy losses are from the high temperature differences between the in of the column observed for stage 1 (condenser stage) and at the bottom of the column where steam is injected. the column. These losses result from the high temperature differences between the internal and external streams at the top and bottom of the column. PREFLASH Exergy Loss Profile (Stage-Exergy Loss)

Stage

10

PREFLASH Exergy Loss Profile (Stage-Exergy Loss) 9

9

8

8

7

7

6

Stage

10

6

Stage

Stage

5 4

5

3

4 2

3 1

0

100

200

300

400

500

600

2

700

800 900 1000 1100 1200 1300 1400 1500 1600 1700 1800 Exergy Loss kW

1 Figure 7.30. Exergy loss profiles for the preflash column shown in Figure 7.27 ( 0

100

200

300

400

500

600

700

800 900 1000 1100 1200 1300 1400 1500 1600 1700 1800 Exergy Loss kW

Figure 7.30.  Exergy loss profiles for the preflash column shown in Figure 7.27 (Alhajji and Demirel 2015).

Energy Analysis  243 Table 7.20. Efficiencies and exergy savings for the three columns in the refinery shown in Figure 7.27 (Alhajji and Demirel 2015). Base case Unit

Modified case

Exmin kW

Exloss kW

η%

Exmin kW

Exloss kW

η%

Change in η %

14836.6

3385.4

81.4

18229.5

3385.5

84.3

3.5

Crude

9749.9

9414.7

50.8

41015.9

8768.0

82.4

62.2

VDU

–3813.3

8051.7

31.1

–6210.5

8085.9

43.4

39.5

Preflash

a

a

Exloss: Total column exergy loss from the converged simulation by Aspen Plus (2022) with the BK-10 method.

20 shows the thermodynamic efficiency determined using Eq. (7.43) and the energy savings for Table 7.20 shows/year the using thermodynamic determined Eq. tool. (7.43) energy the retrofits efficiency suggested by the columnusing targeting Asand can the be seen, savings for operation hours of 8520 hr/year using the retrofits suggested by the column targeting all three column improve considerably and their operations dissipate less available work. The tool. As can be seen, operations of all by three column and their is exhibited the crude unitimprove shown inconsiderably Figure 7.27 (Alhajji and operations Demirel

dissipate less available work. The highest improvement of over 60% is exhibited by the crude unit shown in Figure 7.27 (Alhajji and Demirel 2015). targets2013b) reducing irreversibility causes unnecessary heat Thermodynamic (Demirel analysis2013b) (Demirel targets reducingthatirreversibility that causes With theand column unnecessary heat dissipation due to mismatches between the operating conditions design parameters. With the column targeting tool, this is accomplished in a realistic approach by controlling the thermodynamic driving forces of finite temperature differences, pressure differences, a, 2006a,b). and chemical potential differences in heat, momentum, and mass transfer, respectively (Demirel 2004a, 20 E 2006a,b). Heat exchanger network system Base case Modified case a Exmin Exlossintegration, Ex Exloss hot η η min available Using process heat the streamsChange from the crude and vacuum distillation kW kW kW kW % % in η % units are used to gradually increase the mixed-oil feed temperature by using heat exchanger network 14836.6 3385.4 81.4 18229.5 3385.5 84.3 3.5 system 9749.9 (HENS), 9414.7 which is50.8 shown41015.9 in Figure8768.0 7.31. Heat exchanger network system (HENS) recovers 82.4 62.2 available heat and utilizes it where it is needed to save energy and reduce GHG emissions. −3813.3 8051.7 31.1 −6210.5 8085.9 43.4 39.5 With the heat exchanger network system, considerable energy is recovered in the furnaces of method. loss: Total column exergy loss from the converged simulation by Aspen Plus (2022) with the BK-10 the columns, as shown in Table 7.21. The furnace used in the main unit, which is a crude column, has the largest saving of around 19%. The energy saving leads to reductions in the cost of energy and GHG gas emissions. These results illustrate that it may be possible to achieve a more sustainable increase the mixed-oil feeddetermined temperaturevia by using heat exchanger network system (HENS), refinery process by simple retrofits thermodynamic analysis and energy analysis 1. s (White 2012, Alhajji and Demirel 2015, 2016). E16

LIGHTS

DIESEL

HNAPHTHA

E2

PF-WATER

CU-WAT

E12

E4

E15 S25

HVGO

CU-STM1

E13

S15

S14 PF-STEAM

E5

CU-STM3

CDU-FEED

VDU

E6 S19

S17 S16

S22

CU-STM2

S18

MCRUDE

WATER

E11 AGO

E3

E14

CRUDE

NAPHTHA

S13

S8 S7

E1

S12

E9

E8 LVGO

OFGAS

E10

PREFLASH

KEROSEN

CU-STEAM

E7 R-C

RC

S23 S24

VDU-STM

RESIDU

Figure 7.31.  Process flow diagram after using newly installed heat exchangers to match the available and required heats (Alhajji and Demirel 2015).

244  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence Table 7.21.  Duty savings for the furnaces of the columns (Alhajji and Demirel 2015). Unit

Base case duty (kW)

Modified case Saved duty duty (kW) (kW)

Change in duty (%)

Pre-flash furnace

58012.6

56861.1

1151.5

1.9

Crude furnace

62661.6

50572.6

12089.0

19.3

VDU furnace

28918.5

23899.4

5019.2

17.3

Biorefinery and petrochemical industry integration Refineries can co-process renewable feedstock alongside conventional crude-oil for diesel fuel with higher renewable content and considerably reduced GHG emissions. This may be a part of broader refinery-petrochemical integration for producing ethylene, propylene, and aromatics. Some obstacles are non-uniform and corrosive feed. Renewable feedstocks include vegetable oils and waste fats, with new alkylation technologies supporting crude-to-chemicals initiatives. One promising product is renewable diesel attained using with the single stage Ecofining process (50–70% less expensive than original, two-stage process and can reach production in 12 months, versus three years) by repurposing some unused refinery assets of hydrotreating or hydrocracking. Ecofining uses a combination of catalysts to clean and remove oxygenates from the feedstock and combine hydrotreating with isomerization to improve its cold-flow properties. The renewable diesel can be used as a drop-in fuel in vehicles with no engine modifications and reduces GHG life cycle emissions by 80% compared with conventional diesel (Allen et al. 2018).

7.7  Food, Energy, and Water System By 2050, the demand for energy is predicted to nearly double globally, with water and food demand estimated to increase by over 50%. The ability of existing water, energy, and food systems to meet this growing demand is constrained by the competing needs for limited resources and by climate change impacts (Figure 7.32). The interlinkage between the water, energy and food supply systems is a major consideration in the sustainable development strategies of countries (Al-Saidi and Elagib 2017, Artioli et al. 2017, IEO 2019, IRENA 2020b). Details on these considerations follow: • Nearly 15% of global freshwater is required for extracting and processing fossil fuels and for generating electricity. Conversely, disruptions in energy supply impact water treatment, production, and distribution, and hence water security. At the same time, the agri-food supply chain accounts for 30% of the world’s energy consumption and is the largest consumer of water resources, accounting for approximately 70% of all freshwater use. Vulnerabilities in water and energy supply pose critical risks for food security. • Such interlinkages require exploration of integrated options to ease the pressures and develop pathways based on sustainable and efficient use of limited resources. Renewable energy technologies can address some of the trade-offs between water, energy, and food, bringing substantial benefits in all three sectors. The American Wind Energy Association estimates that during 2013 electricity from wind energy in the U.S. avoided the consumption of more than 130 billion liters of water, equivalent to the annual water consumption of over 320,000 U.S. households. The European Wind Energy Association found that wind energy in the European Union avoided the use of 387 billion liters of water in 2012—equivalent to the average annual water use of 3 million EU households. • Solar-based pumping solutions offer a cost-effective alternative to grid- or diesel-based irrigation pump sets. The Valley Center Municipal Water District in the U.S., for example, installed a 1.1 MW solar power system that provides 2.1 GWh per year, offsetting up to 20% of the electricity required by the utility’s largest pumping station.

Energy Analysis  245 Food • 60% more in 2050 •Considerable use of biomass in cooking and heating •Increase in population

Risk to Supply Chain Availability Accesibility Affordability Utilization Stability Reliability Quality

Water • 55% more in 2050 •In irrigation, agricultural sector power generation

Energy • +80% more in 2050 •In industry, heating & cooling, transportation sector,desalination

Figure 7.32.  Interrelations of food, energy, and water with projections to 2050 (IEO 2019, IRENA 2020b).

Figure 7.32 Interrelations of food, energy, and water with projections to 2050 (IEO 2019, IRENA 2020b). • The United Nations’ energy smart food program proposes: (i) improving access to modern energy services, (ii)15% enhancing efficiency (iii) a gradual increase in the • Nearly of global energy freshwater is requiredand for extracting and processing fossil fuels use and of for renewable energy. Renewable energy provides electricity, or transportation services generating electricity. Conversely, disruptionsheat, in energy supply impact water treatment, production, and distribution, water security. the example, same time, the agri-food accounts of for within the agri-food sector.and In hence the United States,Atfor nearly 840supply GWhchain equivalent 30% of the world’s energy consumption and is the largest consumer of water resources, accounting energy was generated in 2013 by anaerobic digesters placed on farms, which utilize a wide for approximately 70% of all freshwater use. Vulnerabilities in water and energy supply pose critical range of agricultural crop residues, and animal and food wastes to generate usable energy risks for food security. nexus •Water-energy Such require exploration of integrated optionsheating. to ease the pressures and develop on-site in the forminterlinkages of electricity or boiler fuel for space or water This could positively Energy is based on sustainable pathways andco-benefits efficient use oftolimited resources. Renewable businesses, energy technologies affect economic development, bringing farmers, landowners, and can address some of the tradecommunities across all major segments of the agri-food chain.

Water‑energy nexus (Figure 7.33). Association found that wind energy in the European-faceted Union avoided use of 387 billion liters of Energy is involved in preserving food, extending its availability, andthereducing post-harvest water in 2012 – equivalent to the average annual water use of 3 million EU households. losses. Bioenergy can improve access to energy and increase food security creating employment •TheSolar-based solutions offerand a cost-effective alternative to grid-decisions or diesel-based irrigation followingpumping tools rely on data can be effective in policy opportunities and•pump hence contributing toand environmental and societal (Al-Saidi andaElagib sets. The Valley Center Municipal Water in theobjectives U.S., for example, installed 1.1 MW Data availability accessibility areDistrict solar power systemand thatspecifically provides 2.1 GWh per year, offsetting up toeconomic 20% of the growth, electricityenergy required 2017). The impacts of bioenergy, biofuels, on food prices, •by the utility’s largest pumping station. security, deforestation, land use and climate change are complex and multi-faceted (Figure 7.33).

Water for Energy

Energy for Water animal and food wastes to generate usable energy on-site in the form of elec •End use •Wastewater collection and treatment •Construction and maintenance

-

•Extraction and mining •Energy and fuel production •Transportation •Waste disposal and emission control •Construction

Figure 7.33.  Water-energy nexus.

246  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence The following tools rely on data and can be effective in policy decisions. Some details on them follow: • Data availability and accessibility are key for a nexus assessment for specific sectors. • Most nexus tools are designed for a thorough analysis of the three sectors with quantified trade-offs. Water‑food systems Table 7.22 shows the possible risks, their impacts and mitigation/improvement measures for the risks in water-food systems (Green et al. 2017). Table 7.22.  Water-food nexus with risks, impacts, and possible improvements. Risk category Risk to food security

Risks Increased variability in water availability due to global warming. Uneven distribution of food production.

Risk to water security

Water shortages in agriculture. Poorly regulated agricultural investments. Misuse of water resources.

Impacts Higher prices. Volatility of food prices. Regional food production. Poor-quality water usage. Poor quality food production.

Improvements Reduce the variability of water resources, through technologies like water storage and approaches like real time control. Investigate the impact of water variability on the food supply chain, focusing on key bottlenecks and their remediation. Water pollution affecting Optimize of water usage in agriculture, agricultural activity and considering all factors and constraints aquatic life. and suitable objective functions (e.g., low Negative local socio-economic cost, high efficiency, low water use, low impacts. emissions). Depletion of freshwater Education on effective water use and resources. distribution for experts as well as lay people, decision and policy makers and the general public.

Energy‑food systems Table 7.23 shows possible risks, their impacts and mitigation/improvement measures for the risks in the energy-food nexus (Green et al. 2017). Table 7.23.  Energy-food nexus with risks and impacts. Risk category Risk to food security

Risks Dependence on fossil fuel increases volatility in food prices. Potential trade-offs between bioenergy and food-based energy crops.

Risk to energy security

Variability in energy demand for increased food production. Unreliable energy supply chain that depends on feedstock availability.

Impacts Higher energy cost and dependence on fossil fuels. Volatility of energy prices. Social, environmental, and health issues arising from fuel sources. Production of bioenergy interfering with food supply chain. Negative impacts of energy production technologies.

Improvements Investigate and minimize the impact of fuel price volatility on food production, through various approaches such as utilizing energy storage, choosing energy sources that better match supply and demand. Encourage the use of second-generation biomass that does not interfere with the food supply chain, and choose lands for biomass growth that are not highly suited to crop production and other agriculture. Rising energy needs for agriculture Optimize energy usage, considering can strain the energy systems. all factors and constraints and suitable Quality and affordability of objective functions (e.g., low cost, high energy-based crops can affect the efficiency, low emissions). energy supply. Complete the energy transition by Ratio of renewable to integrating energy storage with nonrenewable energy can create renewable energy production and volatility. distribution, and shifting away from fossil fuels to sustainable energy sources in a way that avoids excessive disruptions.

Energy Analysis  247

7.8  Life Cycle Analysis of Energy Systems Global environmental awareness has become a business priority and has led to efforts to examine products and services with life cycle analysis (LCA), following a cradle-to-grave approach. The ISO 14040 series of standards provides the framework for LCA, which consists of four interrelated phases: 1. Clearly defining the goal and scope of the study. 2. Compiling a life cycle inventory (LCI), containing of an inventory of relevant energy and material inputs and environmental releases. 3. Evaluating the potential environmental impacts associated with the identified inputs and releases. 4. Interpreting the results to enable more informed decision-making. An important task in LCA is to keep assumptions to a minimum and explicitly report the assumptions and values on which the LCA is based. LCA approach helps decision-makers to identify environmental hot spots, as well as to improve industrial systems (Curran 2015). A cradle-to-grave scope starts with raw material acquisition that includes the removal of raw materials and energy sources from the Earth, such as the harvesting of trees or the extraction of crude oil. Land disturbance, as well as transport of raw materials from the point of acquisition to the point of processing, are considered part of this stage. The manufacturing stage produces the product from the raw materials and delivers it to consumers. The use stage involves the actual use, reuse, and maintenance of the product. Energy requirements and environmental wastes associated with product storage and consumption are included in this stage. In the recycling and waste management stage, energy requirements and environmental wastes associated with product disposal are included, as well as post-consumer waste-management options, such as recycling, composting, and incineration (Matzen and Demirel 2016). To properly assess the environmental impact of a product or process, we must consider its full life cycle. This can be done using life cycle assessment (LCA), a useful method for assessing and improving the environmental performance of a product or process. LCA considers and evaluates all steps over the life of the product or process, in essence considering all activity involved from “cradle” to “grave”. As a consequence, LCA helps (1) develop an inventory of resource use and waste emissions and (2) identify and assess the corresponding environmental impacts (climate change, ozone depletion, acid precipitation, etc.) (Graedel and Allenby 2010). An additional advantage of LCA is that it links environmental impacts to the specific part(s) of the life cycle responsible for them. Performing a LCA generally involves four main steps (see Figure 7.34) as noted earlier, although variations on this exist. Details on these four steps follow: • Step 1: Goal and scope definition. This step includes definition of the objective(s) of the LCA as well as its scope, the latter delineating the assessment’s limits and boundaries (environmental, economic, area, etc.). This step is sometimes viewed as a pre-LCA step, since it establishes parameters for the assessment rather than carrying it out. • Step 2: Inventory assessment. This step identifies and quantifies the environmental burdens associated with a product or process by considering the inputs (energy and materials in the form of environmental or reused resources) and wastes emitted to the environment, through acquiring technical, environmental, economic, and other information and data on the process or product. • Step 3: Impact assessment. This impact of both energy and material use and environmental emissions are assessed in this step. This is carried out by quantifying the environmental effects and pressures associated with the inputs and outputs quantified during the previous step

248  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence Life cycle analysis (LCA) steps

Pre-LCA steps

1. Goal and scope definition

2. Inventory assessment

LCA steps

3. Impact assessment 4. Improvement assessment

Figure 7.34.  Principal steps in life cycle assessment. Interpretation is used at all stages.

(inventory Relevant environmental Note thatassessment). many interactions occur between theimpact steps categories in Figure are 7.34considered, informed by and consistent with the objectives and scope of the LCA. A wide range ofsubsequent environmental impact improvements to help shape objectives, scope and inventory analysis for LCAs. categories have been developed by various organizations. These include the United Nations Environment Protection Agency, the Nordic LCA usuallyProgramme, considers thethe fullUS life Environmental cycle of a process or product by investigating the Council followingof Ministers and the Centre of Environmental Science at Leiden University in the Netherlands. Figure 7.35: Pre • Step 4:• Improvement assessment. Environmental improvement options and opportunities are developed and evaluated in this step, often prioritized in terms of benefits and needs. The • improvements identified in this step often are in line with improved sustainability. with the longest duration. Note that interactions occur between the steps in Figure 7.34, allowing feedback provided • many Post-operation/utilization steps: These involve the recovery and reotherwise wasted, recyclingscope of wastes and disposal of finalfor wastes. by improvements to helpbeshape objectives, and inventory analysis subsequent LCAs. LCA usually considers the full life cycle of a process or product by investigating the following, as seen in Figure 7.35: • Pre‑operation/utilization steps: These involve the extraction or harvesting of raw resources, manufacturing and processing of resources, storage and transportation and distribution to users. • Operation/utilization: This involves utilization of a product or process and often constitutes the step with the longest duration. • Post‑operation/utilization steps: These involve the recovery and re-use of outputs that would otherwise be wasted, recycling of wastes and disposal of final wastes. LCAs have been reported for a wide variety and number of processes (e.g., chemical plants, power generating stations, manufacturing) and products (e.g., cars, appliances, coffee cups) (Granovskii et al. 2007). In addition, LCAs have been carried out for ammonia production (Singh et al. 2018a) and hydrogen production (Jianu et al. 2016, Singh et al. 2018a, Sadeghi et al. 2020, Valente et al. 2021). Numerous principles, methodologies and guidelines have been developed for LCA. In particular, the International Organisation for Standardisation has published a variety of related documents in this area, including: ISO 14040 Life Cycle Assessment - Principles and Guidelines ISO 14041 Life Cycle Assessment - Life Cycle Inventory Analysis ISO 14042 Life Cycle Assessment - Impact Assessment ISO 14043 Life Cycle Assessment - Interpretation

Energy Analysis  249 Resource collection/harnessing Resource/material processing Energy and material resources (fuels, water, wood, ores, chemicals)

Transportation, distribution, storage Operation/utilization

Energy and material wastes (stack gases, liquid and solid residues, radioactivity)

Reuse, recovery, recycling

Waste disposal Figure 7.35.  Scope of life cycle assessment of a product or process, showing steps in the life cycle vertically and inputs and outputs horizontally.

ISO 14048 Life Cycle Assessment - Data Documentation Format ISO 14049 Life Cycle Assessment - Examples for the application of ISO 14041

, manufacturing) and products (e.g., cars, appliances, coffee cups) ( Additionally, theLCAs Society Toxicology and (Singh Chemistry (SETAC) have produced 2007). In addition, have for beenEnvironmental carried out for ammonia production et al. 2018) and hydrogen production al. 2016, Singh et al. 2018 documents on(Jianu LCAetprinciples, methodologies and guidelines.

7.9  Energy Analysis and Sustainability including:

The sustainable development scenario (SDS)and targets steady improvements in efficiency in all areas ISO 14040 Life Cycle Assessment - Principles Guidelines ISOwith 14041limiting Life Cycle Assessment - Life Cycle Inventory Analysis aligned the rise in global temperatures to 1.5°C. This includes efforts to promote ISO 14042 Life Cycle Assessment - Impact Assessment the efficient design, use and recycling of materials such as steel, aluminum, cement, and plastics ISO 14043 Life Cycle Assessment – Interpretation with increased material in energy and material efficiencies with technological ISO 14048 Life Cycle efficiency. Assessment - Increases Data Documentation Format, ISOcan 14049 Life Cycle Assessment - Examples the application of ISO 14041 changes reduce the GHG emissions. But for caution is needed, since design for storage, the interface between electric vehicles and the grid, and data privacy all have the potential to expose consumers Additionally, the Society for Environmental Toxicology and Chemistry (SETAC) have produced to new risks (Hayashi et al. 2014, Compton et al. 2018, Demirel 2021, Dincer and Rosen 2021a, documents on LCA principles, methodologies and guidelines. Anderson et al. 2021). 7.9

Energy sustainability indicators are numerous, and often include:in with limitin

1. Life cycle GHG emissions. 2. Soil quality emissions. esign 3. Harvest levels of wood resources 4. Emissions of non-GHGs in air 5. Water use and efficiency 6. Water quality 7. Biological diversity in the landscape 8. 9. 10. 11. 12. 13.

Land use and land-use change related to bioenergy feedstock production Allocation and tenure of land for new bioenergy production Price and supply of a national food basket Change in income Jobs in the bioenergy sector Change in unpaid time spent by women and children collecting biomass

250  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence 14. 15. 16. 17. 18. 19. 20. 21. 22. 23.

Bioenergy used to expand access to modern energy services Change in mortality and burden of disease attributable to indoor smoke Incidence of occupational injury, illness, and fatalities Productivity Net energy balance Gross value added Change in consumption of fossil fuels and traditional use of biomass Training and re-qualification of the workforce Energy diversity Infrastructure and logistics for distribution of bioenergy

According to the IEA (2019), the climate challenge is an energy challenge. The energy that powers our daily lives produces three-quarters of global emissions. Making all that energy carbon-neutral by 2050 is a sustainability target for our economies, environment, and societies. The growing climate ambitions add to the significant momentum behind clean energy (Zvolinschi et al. 2007).

7.9.1  Energy Analysis and Process Intensification Energy analysis has a direct implication in process intensification that includes improved energy conversion efficiency, energy conservation and recovery, and energy storage. Substantial improvements in energy efficiency are achievable across all sectors, from buildings to transportation and industry. However, aviation, shipping, and industrial subsectors such as steel, cement, and chemicals manufacturing need extra improvement efforts, such as direct air capture and storage (DACS), bioenergy with carbon capture and sequestration (BECCS), and enhanced carbon uptake in soils and forests (Armstrong and Styring 2015). A large and growing number of countries, states, cities, and corporations have pledged to reduce their net GHG emissions to zero over the next 30 years, in hopes that future global warming can be limited to a target of 1.5 degrees Celsius. These efforts will also consider assessments of wider societal trends, such as changes in economics, demographics, housing patterns, and infectious disease incidents that impact energy systems (Charpentier 2016, Demirel 2018b, Reynolds 2020, Adamu et al. 2020, Demirel 2021). The critical near-term actions to achieve five key technological improvements in energy goals are listed below: 1. Invest in energy efficiency and productivity. Energy use can be reduced, and industrial energy productivity (dollars of economic output per unit of energy consumed) can be increased. 2. Advance electrification. Energy efficiency in transportation, buildings, and industry overlaps with electrification, because switching to electric heat pumps and motors also significantly increases the efficiency of heating and transportation relative to fossil-fueled boilers and internal combustion engines. Electrification may lead to the installation of broadband and smart grid technologies that enable demand-side management and grid optimization. Increasing efficiency and productivity help to reduce the power loads for equipment, which can reduce the cost of capital and operations, lowering hurdles for electrification in these sectors. 3. Produce carbon‑free electricity. Increasing the share of renewable electricity likely requires an increased pace of wind and solar installation, along with coal- and gas-fired plants being retired at an accelerated pace. Existing nuclear plants can be preserved wherever it is possible to continue safe operations. 4. Plan, permit, and build critical infrastructure. Build and upgrade electrical transmission facilities to increase overall transmission capacity to interconnect and harness low-cost wind and solar power, and to facilitate needed infrastructure like electric vehicle (EV) recharging

Energy Analysis  251

networks. This infrastructure should be based on a sustainable CO2 capture, transport, and storage network. 5. Expand the innovation toolkit. Investment in clean energy is essential to provide new technological options, to reduce costs, and to better understand how to manage a socially just energy use and transition. This can enhance energy systems for transportation, buildings, and industry with improved energy storage to complement variable renewable electricity and low-cost zero-carbon fuels including hydrogen from renewable energy or biomass gasification. Progress is needed for aviation, marine transport, and the production of steel, cement, and bulk chemicals, among other areas. Energy transition The “energy transition” requires “greener” energy sources with help of technology despite the dominance of hydrocarbons. The extent of the energy transition complexity requires a balance of the many objectives across a company’s assets and a data-based and quantitative approach. Digitalization and industrial artificial intelligence may be crucial tools in this balancing act (Demirel 2018b, Beck 2020). Worldwide policy support and cost reductions make it less expensive to use greener energy resources, including non-carbon energy—hydro, nuclear, and renewables. The production of chemicals is very energy intensive, and the energy transition would require reduced and renewable energy in manufacturing and processing sectors. For example, conventional crude oil refineries worldwide can all operate with chemical production to maximize economies of scale and exploit integration opportunities for chemical and fuels. A business-as-usual (BAU) approach to the production and consumption trends through 2050 may provide the basis for modelling to assess sectoral energy conservation (EC) approaches with their energy saving potentials (IRENA 2020b). This model consists of two phases: • Phase 1: data collection and developing sector profiles with BAU energy consumption projections and • Phase 2: assessment of ECs with barrier analysis. Technical potential scenarios can be used to estimate the level of energy consumption, and they refer to all industrial processes that are upgraded with energy conservation measures (ECMs) (Demirel 2018a), regardless of economic constraints of the scenarios. Details on potential scenarios follow: • Economic potential scenario 1 determines the level of energy consumption that would occur if all industrial processes were upgraded with ECMs that are economically feasible with a 2-year simple payback period. • Economic potential scenario 2 considers a 5-year simple payback period at a predefined uptake rate and trend. The economic and technical potentials are projected up to 2050 (at 5-year increments), based on the amount of savings achieved relative to the sector-specific BAU projections. To project and quantify the EC approaches at a sectoral level, an industrial energy efficiency model has been used with a database containing potential ECs, both in terms of technical best practice and management (IRENA 2020a, 2020b). Long-term targets alone will not reduce emissions rapidly enough to reach net zero by mid-century, according to the IEA (2019). A transformation of the energy infrastructure would be required. Technologies are available for innovating, scaling, and achieving competitive advantage via the hydrogen economy, biofuels, and other energy transition strategies. Additionally, hydrogen provides an energy carrier for circular economy programs that seek to eliminate emissions and waste in production. There is significant opportunity now for companies to accelerate the time-to-value for the hydrogen economy, carbon capture and biofuels by leveraging today’s digital solutions that help ensure faster adoption, scale, and competitive advantage.

252  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence Electrification Electrification may lead to energy security. Cost reductions in renewables and advances in digital technologies are creating further opportunities for electrification. Lower battery costs combined with renewables including hydrogen are an important part of electrification, alongside the direct use of renewables and hydrogen. Combined solar and wind energy with battery storage plants can reduce cost and increase the capacity factors of renewable energy. This expansion of usage may ultimately exceed the present fossil-fuel fired power generation usage (Saker 2022). That means addressing emissions from existing power plants, factories, cargo ships and other capital-intensive infrastructure already in use. Three options can be considered to reduce emissions from the existing stock of plants (IEA 2019): retrofit them with carbon capture, storage, and utilization (CCU) or biomass co-firing equipment; repurpose them to focus on providing system adequacy and flexibility while reducing operations; and retire them early. Low-carbon hydrogen at present is relatively expensive to produce. Sustainable potential for biomethane supply produced from organic wastes and residues could cover some 20% of today’s gas demand. However, transforming the entire energy system will require progress across a much wider range of energy technologies, including efficiency, carbon capture and utilization, hydrogen, nuclear and others. Initiatives from civil society, companies, investors, and governments can make a major difference (IEO 2019). Gradual process intensification is applied to a conventional flowsheet to produce, for example, the green solvent ethyl lactate from ethyl alcohol and lactic acid. The approach takes the base design and integrates two pieces of equipment into one at each step of the intensification task. Each intensified structure is rated through economic, environmental, sustainability and inherent safety metrics. The process involves the separation of an ethanol-water azeotropic mixture, for which an extractive distillation system is used. Two entrainers are considered for the extractive distillation process, one based on a mixture of ethylene glycol/glycerol, and the other on acetol. The results of intensifying an original flowsheet with a reaction unit and four separating units into two pieces of equipment show that significant economic savings and improvements in sustainability and inherent metrics can be obtained (Tusso-Pinzóna et al. 2020). Decarbonization Utilities are adopting decarbonization as a primary measure for large corporate customers and other stakeholders. Hydrogen and carbon capture, sequestration, and utilization technologies are used by utilities, oil and gas companies and other industrial sector players. The electric sector has begun to embrace hydrogen technology. The gas industry is interested in “blue hydrogen” technology. Industry hopes to replace blue hydrogen with “green hydrogen” produced from excess renewable energy. With its ability to mix with existing natural gas fuels, hydrogen may lead to decarbonization in the electricity sector (Demirel et al. 2015). Renewable natural gas also can offer an opportunity for both wastewater utilities and natural gas providers to reduce their carbon footprints (Ondrey 2019a, IRENA 2020a, b). Global decarbonization efforts in the gas industry are growing in parallel with the use of renewable fuels as well as hydrogen and ammonia. Ammonia is a stable compound produced from natural gas and water. In producing ammonia, the latter two substances are first fed to a catalytic steam methane reformer (SMR) where hydrogen is produced from the methane: CH4 + H2O → CO + 3H2 (7.44) This gas mixture is then fed to a catalytic water-gas shift conversion process for further hydrogen production from water: CO + H2O → CO2 + H2 (7.45)

Energy Analysis  253

After the CO2 is removed, pure blue hydrogen is produced and combined with nitrogen, using the Haber-Bosch process, to produce blue ammonia: 3H2 + N2 → 2NH3 (7.46) Green ammonia can be produced from renewable hydrogen. According to the IEA, electrification would account for about 40 to 50 percent of the decarbonized energy market, and other carbon-free energy sources and energy carriers would still be important. Investment in a global hydrogen economy may increase markedly as transportation, power generation, commercial, industrial, and manufacturing sectors seek to align with environmental, social and governance (ESG) commitments. Ammonia is traditionally a fertilizer and feedstock in manufacturing of plastics, explosives, textiles, and other industrial products. Ammonia is also considered for storing and transporting zero-carbon energy and hydrogen at large scale (Matzen et al. 2015a). Ethylene plants are a key part of the petrochemical industry and offer great potential for decarbonization as part of global efforts to improve energy efficiency while complying with environmental regulations geared toward a sustainable future. Plant operators face the challenge of lowering the consumption of power and steam for profitable operations and want to develop and optimize technology and equipment packages considering maintenance, operating expenses (OPEX) and capital expenses (CAPEX), and regulatory compliance (Alhajji and Demirel 2016). The mass energy densities of hydrogen and ammonia are 120 MJ/kg and 18.6 MJ/kg, respectively. Liquid ammonia has a higher energy density than liquid hydrogen, allowing it to store more energy at the same volume and offering a safer, cheaper means of transport across long distances than the transport of liquified hydrogen (see Table 7.24). Hydrogen requires large storage volumes and has limited existing infrastructure for its storage and distribution. Ammonia, therefore, can be a hydrogen carrier for the transportation and storage of the fuel and later converted back to hydrogen at the end user site (Matzen et al. 2015a). Ammonia has the potential to accelerate decarbonization in power generation, transportation, and green chemicals. Ammonia can be used as fuel for internal combustion (IC) engines, such as marine fuel with modified engines. Dual fuel injection, such as ammonia + diesel or ammonia + hydrogen, may improve flame stability, and balance CO and NOx emissions (Dimitriou and Javaid 2020). Besides, ammonia can also be considered as a fuel for solid-oxide fuel cells (SOFCs) and polymer electrolyte membrane fuel cells (PEMFCs). Hydrogen is commonly used as a rocket fuel and for fuel-cell vehicles, in addition to its use as a fuel blend in turbines. The density of hydrogen requires higher flame speeds compared with hydrocarbons in the combustion zone and needs combustors to be configured for hydrogen. Fuel cells in vehicles can achieve efficient use of hydrogen as a fuel. The production of hydrogen from catalytic cracking of ammonia is common (Demirel 2018b). Due to cleaning processes and energy requirements, the available energy of hydrogen from ammonia is almost the same. For example, 1 ton of ammonia as fuel would produce 16,530 MJ of energy; one ton of ammonia would produce 0.18 tons of hydrogen which is equivalent to 16,626 MJ. Both ammonia and hydrogen are likely to be considered in low-carbon strategies. Based Table 7.24.  Energy densities and efficiencies of ammonia and hydrogen based on lower heating values. Substance

Energy/weight kJ/kg

Energy/volume kJ/m3

Liquid temp. o C

Efficiency, % (energy out/energy in)

Hydrogen (compressed at 245 bar(g), 20°C)

120,000

2,110,132

–253

83

Hydrogen (liquid)

120,000

9,323,189

–253

56

Ammonia

18,600

11,457,600

–33

62

Liquefied natural gas (LNG)

49,000

21,217,000

–162

93

254  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence on a cost comparison and further technology development, the use of ammonia as a fuel rather than a hydrogen carrier may be the more economical option (Pavlos and Rahat 2020). Intensified process extracting sugars Extracting sugars of xylose and glucose from cellulosic biomass is costly because of the biopolymers in plants protecting these sugars. An intensified process extracting sugars employs beams of accelerated electrons to fracture chains of cellulose and hemicellulose causing improved enzymatic hydrolysis to extract end products at 10 times the volume of production. In the first step, waste biomass, including corn cobs, corn stover, and sugarcane bagasse, are milled to a particular size range. Next the milled biomass passes below a beam of accelerated electrons that collide with atoms of biomass, causing electrons to be ejected from the biopolymers, leaving the biomass in an ionized state, which is unstable and break apart polymer chains. The molecular weight of the polymers is reduced in this treatment by 95% or more. Therefore, in the third stage, enzymatic hydrolysis yields sugars more efficiently by a factor of 10 compared with untreated biomass. Since there are no high temperatures or pressures required and low water use and waste production, the process considerably lowers the cost of extracting sugars (Ondrey 2019b).

7.9.2  Energy Analysis and Artificial Intelligence Predictive analytics substantially reduce unplanned “flaring.” The World Bank estimates that flaring contributes more than 350 million tons of GHG emissions every year. These emissions could be dramatically reduced by increasing equipment reliability to eliminate unplanned shutdowns and the flaring that comes with them. Predictive maintenance can dramatically improve safety. The Chemical Safety Board in the U.S. asserts that unplanned startups and shutdowns contribute to 50% of safety incidents in the refining industry. Natural gas is emerging as an important “bridge fuel” to reduce carbon with the help of technology for improved energy-intensive process. Some examples of such technologies follow (Beck 2020): • Digital twin models and advanced control can reduce the energy use during manufacturing. • Prescriptive maintenance technology consisting of machine learning and advanced AI analytics can alert operators to conditions with risks to the high-capital compressors and cold boxes. Also, wind farms are adopting prescriptive maintenance approaches. • In self-optimizing plants, data and AI contribute to make investments self-learning, self-adapting, and self-sustaining. • Renewable power assets may be crucial, including bioenergy conversion approaches for bioethanol, biodiesel, waste-to-energy pyrolysis, algae conversion, and biochemicals. • Energy efficient conversion of hydrocarbons, synthesis of chemicals and supply chain technologies play important role in sustainability. For example, both digital twin monitoring systems and dynamic optimization methods can together save 5–15% energy use, reducing carbon emissions by a proportional amount. • A utility supply chain may benefit from optimization with the choice of oil, gas, biofuels, and renewables with shorter and longer time intervals. AI-driven predictive maintenance can warn operators of potential equipment failures days, weeks, or months in advance, reduce the number of unplanned shutdowns, and keep production within safe operating limits. Collecting data from across the enterprise and using data with advanced technology helps remove uncertainty in operation. Such technologies employ models that include mass and energy balances with fundamental thermodynamics, kinetics, fluid mechanics, and transport phenomena (Pantelides and Renfro 2013). Significant progress made on catalysts, adsorbents,

Energy Analysis  255

solvents, complex feedstocks, and multiphase flows have had an impact on process design and modeling with sustainability in mind (Charpentier 2016, Nishant et al. 2020, Nti et al. 2022). According to the U.S. Energy Information Administration, global energy demand will grow by almost 50 per cent between 2020 and 2050 due to the increase in population and standards of living (IEA 2019). An energy transition is a part of sustainable movement towards “greener” energy sources with technological advancements. The oil and gas industry plans to become renewable power generators and achieve net-zero in its operations by 2050. Liquified natural gas (LNG) is emerging as a “bridge fuel” to reduce carbon. Digital twin models and advanced controls help in attaining sustainable energy use during LNG processing. This technology advances the ‘greenness’ of natural gas where possible with prescriptive maintenance technology, machine learning, and advanced AI analytics for protecting high-capital devices. Highly complex plants need self-optimizing approaches that are self-learning, self-adapting, and self-sustaining, enabled by data and AI. Also, devices that have equipment inherently installed remotely are successfully adopting prescriptive maintenance options. Bioenergy conversion approaches, including bioethanol, biodiesel, waste-to-energy pyrolysis, algae conversion, and biochemicals can be enhanced by both digital twin monitoring systems and dynamic optimization. Such approaches can reduce both energy use and reduce GHG emissions by 5–15% (Demirel 2018b, Kwok 2019, Nti et al. 2022).

7.10  Case Studies Natural gas upgrading Natural gas upgrading focuses on performance improvements and cost reductions in the upstream and downstream operations to achieve efficient, sustainable technologies for domestic natural gas utilization. The main objectives include leveraging natural gas reserves by providing domestic utilization and intensifying modular equipment and processes to maximize positive environmental and economic impacts. This may be achieved by upgrading Natural Gas Research and Development (R&D) Roadmaps and facilitating efficient pipelines (IRENA 2020b). Blue ammonia transition To meet government commitments, many energy companies and asset owners are seeking opportunities to divest fossil fuel assets and balance portfolios with a focus on decarbonization. This indicates that ammonia, both blue and green, may be a key element in the 2050 decarbonization effort in a sustainable manner. Blue ammonia can be produced, stored, and transported using ammonia at large scale with existing infrastructure. Ammonia’s stability as a gas at room temperature facilitates transport. A considerable part of the existing liquefied petroleum gas fleet is capable of transporting ammonia and will be critical as new hydrogen powered technologies and will help achieve climate targets (Matzen et al. 2015a, Demirel 2018b). Compared to land-based facilities, floating natural gas liquefication offers a better return on investment because of the shorter period of commercial operation. The same benefits can be obtained with floating blue ammonia production units combined with the direct disposal of the captured CO2 to depleted wells through existing pipelines which could reduce the cost of the liquefaction, transportation, and storage of CO2 at another location (Demirel 2021). Although the mass energy densities of hydrogen is high, at 120 MJ/kg, hydrogen requires large storage volumes, which limits the present potential of hydrogen as a fuel. But, ammonia can be considered as a hydrogen carrier for the transportation and storage of the fuel; subsequently it can be cracked back to hydrogen for the end user (Demirel 2021). According to the IEA World Energy Outlook, electrification will only be able to make up about 40 to 50 percent of the decarbonized energy market, and other carbon-free energy sources and energy carriers are still important. Investment in a global hydrogen economy may increase notably as transportation, power generation, commercial, industrial, and manufacturing sectors seek to align

256  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence ESG commitments. Petrochemicals, steel, aluminum, and cement, as well as shipping, aviation and road freight are subject to abatement costs, which can motivate them to switch to lower carbon technologies to stay sustainable (Valera-Medina et al. 2018). Ammonia can be used as fuel for ammonia driven modified engines when petroleum oil stocks became low. Ammonia fuel storage is typically larger than those for hydrogen and some modifications in the construction of storage material selection are required. Furthermore, the higher compression ratio required in the modifications results in a dual fuel injection, such as ammonia + diesel or ammonia + hydrogen. This in turn, keeps the compression ratio modest, improves flame stability, and achieves acceptable CO and NOx emissions. Ammonia also can be as a fuel for fuel cells (Demirel 2018b, Valera-Medina et al. 2018, Javanshir et al. 2020, Pavlos and Rahat 2020). Engines using ammonia may emit high NOx, exceeding 1000 ppm. However, this emission can be reduced with careful engine design and systematic tuning, as well as modifications to the combustor inlet temperature, injection port location, fuel blend, and air injection rate. Nonetheless, appropriate selective catalyst reduction (SCR) units are typically needed to remove NOx from the flue gas. Hydrogen can be used in fuel-cell vehicles, as well as in a fuel blend in turbines. The density of hydrogen requires higher flame speeds than hydrocarbons in the combustion zone and requires reconfiguration of combustors. Fuel cells in vehicles usually make efficient use of hydrogen as a fuel. The production of hydrogen from catalytic cracking of ammonia is common. Research into ammonia decomposition is ongoing in the areas of plasma technology and ammonia electrolysis (Sadeghi et al. 2020). Hydrogen versus ammonia As noted earlier, the energy of hydrogen from ammonia and ammonia are almost the same. For the capital expenditure (CAPEX), practically the cost of a selective catalytic recovery (SCR) unit may be compared to the cost of a cracking unit for a particular volume of ammonia using an air pollution control cost spreadsheet without considering the ammonia production cost. Both ammonia and hydrogen are likely to be considered in low-carbon strategies. Based on the cost comparison and with further technology development, the use of ammonia as a fuel rather than a hydrogen energy carrier may be the more economical option. Still the choice between ammonia or hydrogen will be determined based on various factors, including use and overall system maturity (Valera-Medina et al. 2015, Demirel 2018b, 2021). Power‑to‑X Power-to-X facilitates storing fluctuating renewable energy effectively are major challenges of the energy transition. Renewable hydrogen can store renewable electricity which can then be used in industry, manufacturing and power generation. This effort is sustainable energy centered around hydrogen, including hydrogen’s use to power steelmaking and rail transport (Ondrey 2019c, 2020a). Summary Energy exists in many forms and is convertible from one form to another. Energy production mainly involves converting one form of energy to a form that is needed. For example, the chemical energy in fossil fuels such as coal or natural gas can be used in steam power plants to produce electricity, while hydroelectric power is based on the kinetic energy of flowing water. Also, polygeneration is the production of more than one useful form of energy from the same energy source. There are two main types of energy: renewable (solar, hydraulic, wave, ocean, wind, biomass, geothermal, etc.) and nonrenewable (fossil fuels such as coal, oil and natural gas, and uranium). The world’s energy demand is expected to rise considerably in the future as the global population rises and developing countries industrialize and raise standards of living. This chapter examines such technical topics as energy production and conversion, energy conservation and efficiency, energy storage, energy analysis and advanced tools like exergy analysis.

Energy Analysis  257

Coverage is also provided of broader energy-related topics, such as energy economics, the food energy water nexus, life cycle assessment of energy systems, and energy analysis and sustainability. Case studies are included to illustrate energy issues. The bottom line of this chapter is that a transformation of our energy infrastructure is required to achieve or shift toward sustainability. In some areas like climate change, a worldwide undertaking of unprecedented speed and scale is required, necessitating decisive action over the next decade and beyond, especially if the rise in mean global temperature is to be limited to 1.5°C. In fact, making energy carbon-neutral in 10 or 20 years may constitute a sustainability target for economies, environment, and societies. Yet, more broadly, energy sustainability requires improvements in all parts of the energy infrastructure, for greater efficiency, lower environmental impact, acceptable costs and societal alignment. This can involve increasing use of electric and hydrogen automobiles, expanding the production of hydrogen energy; and boosting the use of sustainable energy. To measure and monitor actions to enhance energy sustainability, relevant indicators need to be considered. These can include life cycle GHG and other emissions and their impacts on health; soil, water and air quality; efficiency and productivity of energy utilization; use of renewable energy utilization and reduction or avoidance of use of fossil fuels; ecosystem and biological health and diversity; and energy costs and affordability. Finally, it is pointed out that process intensification can directly and indirectly help enhance many aspects of energy utilization, including improved energy conversion efficiency, energy conservation and recovery, and energy storage.

Nomenclature AFUE Annual fuel utilization efficiency BAU Business as usual BECCS Bioenergy with carbon capture and sequestration BTL Biomass-to-liquid CAES Compressed air energy storage CAPEX Capital expenses CHP Combined heat and power COP Coefficient of performance D diameter DACS Direct air capture and storage DDGS Dried distillers grain solubles DME Dimethyl ether EC Energy conservation ECM Energy conservation measure EER Energy efficiency ratio EROI Energy return on investment ESG Environment, social, and governance FAME Fatty acid methyl ester GHG Greenhouse gas H Enthalpy HENS Heat exchanger network system HRSG Heat recovery steam generator HVAC Heating, ventilation and air conditioning IF Intensification factor LCA Life cycle analysis LCEE Life cycle energy efficiency LCI Life cycle inventory LHV Lower heating value LNG Liquefied natural gas

258  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence NHxmin Minimum number of heat exchangers NOx Nitrogen oxides OPEX Operating expenses ORC Organic Rankine cycle PCM Phase changing material PEMFC Polymer electrolyte membrane fuel cell PHES Pumped hydro energy storage Q Volumetric flow rate . Q.C Condenser duty QR Reboiler duty SCR Selective catalytic recovery SEER Seasonal energy efficiency ratio SMES Superconducting magnetic energy storage SOFC Solid-oxide fuel cell TES Thermal energy storage T Temperature v . Speed of air flow W Work rate or power

Greek letters ε Effectiveness η Efficiency ρ Density

Problems 7.1 Define and describe the different forms of energy and explain their differences. Identify which are renewable and which are nonrenewable. 7.2 Describe the possible uses of (a) solar energy and (b) geothermal energy. 7.3 Solar energy itself is freely available. Given this positive attribute, why is solar energy not more widely applied? 7.4 Is exergy analysis a substitute for energy analysis? 7.5 Describe the advantages of each of exergy analysis and energy analysis. 7.6 Write steady-state energy and exergy balances for the following devices: (a) a general control volume, (b) a diabatic heat exchanger, (c) an adiabatic heat exchanger, (d) a pump, (e) a gas turbine. 7.7 Describe how life cycle assessment works and what information it provides. Explain how LCA relates to both environmental stewardship and sustainability. 7.8 Some claim that the main reason life cycle assessment is not used to a greater extent by industry is that the results of LCA are difficult to attain or difficult to interpret or lacking sufficient objectivity. Do you agree? Explain. 7.9 People are continuously encouraged to conserve energy even though according to the first law of thermodynamics energy is conserved anyway. Explain why this confusion sometimes exists, and how the confusion can be avoided. 7.10 Apply exergy analysis to a specific industry that has not been examined previously in the literature using exergy methods. What are the improvements attained by applying exergy methods? Explain them qualitatively and quantitatively.

Energy Analysis  259

7.11 Apply life cycle assessment to a specific industry that has not been examined previously in the literature using LCA. What are the improvements attained by applying life cycle assessment? Explain them qualitatively and quantitatively. 7.12 Identify four types of energy storage and describe fields in which they are commonly applied. 7.13 How can the application of energy storage make the use of wind energy more attractive? What types of energy storage can be applied in this situation? 7.14 Explain why tidal power plants are costly even though they use tidal energy, which is free. 7.15 Describe the steps in a life cycle assessment, and how LCA permits the impact of general engineering systems on the environment to be evaluated. 7.16 When environmental assessments are performed for a system, often only the operating phase of the life cycle rather than the full life cycle is assessed. In what situations might such a simplified approach be (a) reasonable for realistically assessing the environmental impacts and (b) unreasonable? 7.17 Design and analyze a sustainable energy system that combines at least two types of sustainable energy sources and/or technologies to meet the needs of a building in which you reside (or another building you select), so that the building becomes a “near net zero energy” building (i.e., a building that uses almost zero net energy in the form of fossil fuels over the year to meet its own needs). Specifically, you should aim at making the building use only 20% of the fossil fuel energy it used before, i.e., its original energy use. Consider in your work the energy requirements of the building for electricity, cooling, heating (space and hot water) and any other required energy services. The design should involve at least conceptual system development and more a detailed design where possible. The analysis should support the design and also provide additional information that can be useful in evaluating the design and its merits. The following steps are recommended (but not all are necessarily required, and other steps can be included where relevant): • Design the system. • Perform an analysis of the system. In this step, you may wish to present balance equations for mass, energy, and exergy for the system and its components, and determine efficiencies and other performance parameters, as relevant. You may also wish to analyze cost, environmental and sustainability factors. • Compare the results with those for the original building (which likely used conventional stand-alone systems to provide the energy services). • For important design parameters, perform a parametric study to show how system performance is affected by varying the operating and environment conditions. • Discuss the results and findings, verify and validate the results, draw conclusions, and make recommendations regarding what actions you suggest, if any, based on your results and conclusions. Throughout, make reasonable and appropriate assumptions and approximations, list properties, data, etc. utilized and/or assumed, and use relevant software to assist where appropriate and useful.

Research Projects 7.1 Investigate the impact of energy analysis on environmental sustainability, economic sustainability and social sustainability and sustainable development. 7.2 Examine and explain the impact of energy transition and decarbonization on process intensification. 7.3 Elaborate on the impact of artificial intelligence on energy transition and decarbonization.

260  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence

References Adamu, A., Russo-Abegão, F. and Boodhoo, K. 2020. Process intensification technologies for CO2 capture and conversion—a review. BMC Chemical Engineering 2: 2–18. Aghbashlo, M., Tabatabaei, M., Rahnama, E. and Rosen, M.A. 2020. A new systematic decision support framework based on solar extended exergy accounting performance to prioritize photovoltaic sites. Journal of Cleaner Production 256: 120356. Alhajji, M. and Demirel, Y. 2015. Energy and environmental sustainability assessment of crude oil refinery by thermodynamic analysis. Int. J. Energy Research 39: 1925–1941. Alhajji, M. and Demirel, Y. 2016. Energy intensity and environmental impact metrics of the back-end separation of ethylene plant by thermodynamic analysis. Int. J. Energy Environ. Eng. 7: 45–59. Allen, J.W., Unlu, S., Demirel, Y., Black, P. and Riekhof, W. 2018. Integration of biology, ecology and engineering for sustainable algal based biofuel and bioproduct biorefinery. Bioresources and Bioprocessing 5(47): 1–28. https://doi.org/10.1186/s40643-018-0233-5. Al-Saidi, M. and Elagib, N.A. 2017. Towards understanding the integrative approach of the water, energy and food nexus. Sci. Total Environ. 574: 1131–1139. doi: 10.1016/j.scitotenv.2016.09.046. Al-Zareer, M., Dincer, I. and Rosen, M.A. 2018. Transient energy and exergy analyses of a multistage hydrogen compression and storage system. Chemical Engineering and Technology 41: 1594–1603. Anderson, A., Rezaie, B. and Rosen, M.A. 2021. An innovative approach to enhance sustainability of a district cooling system by adjusting cold thermal storage and chiller operation. Energy 214: 118949. Armstrong, K. and Styring, P. 2015. Assessing the potential of utilization and storage strategies for post-combustion CO2 emissions reduction. Frontiers in Energy Research 3: DOI=10.3389/fenrg.2015.00008. Artioli, F., Acuto, M. and McArthur, J. 2017. The water-energy-food nexus: An integration agenda and implications for urban governance. Polit. Geogr. 61: 215–223. doi: 10.1016/j.polgeo.2017.08.009. Ashrafi Goudarzi, S., Fazelpour, F., Gharehpetian, G.B. and Rosen, M.A. 2019. Techno-economic assessment of hybrid renewable resources for a residential building in Tehran, Iran. Environmental Progress & Sustainable Energy 38(5): 13209. Aspen Technology. 2022. http://www.aspentech.com/products/aspen-plus.aspx; accessed 11 June 2022. Beck, R. 2020. Energy Transition. Industry Perspective. Aspen Tech (www.aspentech.com). Bingham, R., Agelin-Chaab, M. and Rosen, M.A. 2019. Whole building optimization of a residential home with PV and battery storage in The Bahamas. Renewable Energy 132: 1088–1103. Çengel, Y.A. and Boles, M.A. 2019. Thermodynamics: An Engineering Approach. 9th edn. McGraw-Hill, New York. Chan, Y. and Kantamaneni, Y. 2015. Study on Energy Efficiency and Energy Saving Potential in Industry from Possible Policy Mechanisms. ICF International, Fairfax. Charpentier, J-C. 2016. What kind of Modern “green” Chemical Engineering is required for the Design of the “Factory of Future”? Procedia Engineering 138: 445–458. Compton, M., Rezaie, B. and Rosen, M.A. 2018. Exergy approach for advancing sustainability of a biomass boiler. International Journal of Exergy 27(1): 62–80. Curran, M.A. 2015. Life Cycle Assessment: A systems approach to environmental management and sustainability. Chem. Eng. Prog. 11(10): 26–35. Demirel, Y. 2004a. Thermodynamic analysis of separation systems. Sep. Sci. Tech. 39: 3897–942. Demirel, Y. 2004b. Exergy use in bioenergetics. International Journal of Exergy 1: 128–146. Demirel, Y. 2006a. Assessment of thermodynamic performances for distillation columns. Int. J. Exergy. 3: 345–361. Demirel, Y. 2006b. Retrofit of distillation columns by thermodynamic analysis. Separation Science and Technology 41: 791–817. Demirel, Y. and Ozturk, H.H. 2006. Thermoeconomics of seasonal heat storage system. International Journal of Energy Research 30: 1001–1012. Demirel, Y. 2012. Chemical Exergy. Exergy and its Applications for Better Environment and Sustainability University of Ontario Institute of Technology Oshawa, ON, Canada; April 30–May 4, 2012. Demirel, Y. 2013a. Sustainable distillation column operations. Chem. Eng. Process Techniques 1005: 1–15. Demirel, Y. 2013b. Thermodynamics analysis. Arabian Journal Science Engineering 38: 221–249. Demirel, Y., Matzen, M., Winters, C. and Gao, X. 2015. Capturing and using CO2 as feedstock with chemical-looping and hydrothermal technologies. Int. Journal of Energy Research 39: 1011–1047. Demirel, Y. 2018a. Energy conservation. Vol. 5. pp. 45–90. In: Dincer (ed.). Comprehensive Energy Systems. Elsevier, Amsterdam. Demirel, Y. 2018b. Biofuels. Vol. 1., Part B. pp. 875–908. In: Dincer (ed.). Comprehensive Energy Systems. Elsevier, Amsterdam.

Energy Analysis  261 Demirel, Y. and Gerbaud, V. 2019. Nonequilibrium Thermodynamics: Transport and Rate Processes in Physical, Chemical and Biological Systems. 4th ed., Elsevier, Amsterdam. Demirel, Y. 2021. Energy. Production, Conversion, Storage, Conservation, Coupling. 3rd ed. Springer, London. Dhole, V.R. and Linnhoff, B. 1993. Distillation column targets. Comp. Chem. Eng. 17: 549–60. Dimitriou, P. and Javaid, R. 2020. A review of ammonia as a compression ignition engine fuel. International Journal of Hydrogen Energy 15: 7098–7118. Dincer, I. and Rosen, M.A. 2015. Exergy Analysis of Heating, Refrigerating and Air Conditioning: Methods and Applications. Elsevier, Oxford, UK. Dincer, I., Rosen, M.A. and Ahmadi, P. 2017. Optimization of Energy Systems. Wiley, London. Dincer, I. and Rosen, M.A. 2021a. Exergy: Energy, Environment and Sustainable Development. 3rd ed. Oxford, UK: Elsevier. Dincer, I. and Rosen, M.A. 2021b. Thermal Energy Storage: Systems and Applications. 3d ed. Wiley, London. Douvartzides, S.L., Charisiou, N.D., Papageridis, K.N. and Goula, M.A. 2019. Green diesel: biomass feedstocks, production technologies, catalytic research, fuel properties and performance in compression ignition internal combustion engines. Energies 12: 809–817. Ehyaei, M.A. and Rosen, M.A. 2019. Optimization of a triple cycle based on a solid oxide fuel cell and gas and steam cycles with a multiobjective genetic algorithm and energy, exergy and economic analyses. Energy Conversion and Management 180: 689–708. Farsi, A. and Rosen, M.A. 2021. Exergoeconomic analysis of a geothermal steam turbine combined with multi-effect desalination and reverse osmosis. e-Prime: Advances in Electrical Engineering, Electronics and Energy 1: 100022. Gagliano, A., Nocera, F. and Bruno, M. 2018. Simulation models of biomass thermochemical conversion processes, gasification and pyrolysis, for the prediction of the energetic potential. Advances in Renewable Energies and Power Technologies. Volume 2: Biomass, Fuel Cells, Geothermal Energies, and Smart Grids, 39–85. Goldmeer, J. 2019. Power to Gas: Hydrogen for Power Generation. Fuel Flexible Gas Turbines as Enablers for a Low or Reduced Carbon Energy Ecosystem. GE A3861, General Electric, February 2019. Graedel, T.E. and Allenby, B.R. 2010. Industrial Ecology and Sustainable Engineering. Upper Saddle River, NJ: Prentice Hall. Granovskii, M., Dincer, I. and Rosen, M.A. 2007. Exergetic life cycle assessment of hydrogen production from renewables. Journal of Power Sources 167: 461–471. Green, J.M.H., Cranston, G.R., Sutherland, W.J., Tranter, H.R., Bell, S.J., Benton, T.G. et al. 2017. Research priorities for managing the impacts and dependencies of business upon food, energy, water and the environment. Sustain. Sci. 12: 319–331. doi: 10.1007/s11625-016-0402-4. Haghghi, M.A., Shamsaiee, M., Holagh, S.G., Chitsaz, A. and Rosen, M.A. 2019. Thermodynamic, exergoeconomic, and environmental evaluation of a new multi-generation system driven by a molten carbonate fuel cell for production of cooling, heating, electricity, and freshwater. Energy Conversion and Management 199: 112040. Hall, C.A.S., Lambert, J.G. and Balogh, S.B. 2014. EROI of different fuels and the implications for society. Energy Policy 64: 141–152. Hamrang, F., Mahmoudi, S.M.S. and Rosen, M.A. 2021. A novel electricity and freshwater production system: performance analysis from reliability and exergoeconomic viewpoints with multi-objective optimization. Sustainability 13(11): 6448. Hayashi, T., van Ierland, E.C. and Zhu, X. 2014. A holistic sustainability assessment tool for bioenergy using the Global Bioenergy Partnership (GBEP) sustainability indicators. Biomass and Bioenergy 66: 70–80. IEA. 2019. World Energy Outlook 2019, IEA, Paris https://www.iea.org/reports/world-energy-outlook-2019. IEO. 2019. International Energy Outlook 2019 with projections to 2050. September 2019, U.S. Energy Information Administration, U.S. Department of Energy, Washington, DC 20585. Available at: https://www.eia.gov/ieo. IRENA. 2020a. Renewable Power Generation Costs in 2019, International Renewable Energy Agency, Abu Dhabi. IRENA. 2020b. Global Renewables Outlook: Energy transformation 2050 (Edition: 2020), International Renewable Energy Agency, Abu Dhabi. Javanshir, N., Seyed Mahmoudi, S.M., Akbari Kordlar, M. and Rosen, M.A. 2020. Energy and cost analysis and optimization of a geothermal-based cogeneration cycle using an ammonia-water solution: thermodynamic and thermoeconomic viewpoints. Sustainability 12: 484. Jenkins, S. 2019. Process intensification for carbon capture could reduce costs. Chem. Eng. March: 7. Jianu, O., Pandya, D., Rosen, M.A. and Naterer, G. 2016. Life Cycle Assessment for thermolysis and electrolysis integration in the copper-chlorine cycle of hydrogen production. WSEAS Transactions on Environment and Development 12(Art. #26): 261–267.

262  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence Karimi, M.S., Fazelpour, F., Rosen, M.A. and Shams, M. 2019. Techno-economic feasibility of building attached photovoltaic systems for the various climatic conditions of Iran. Environmental Progress & Sustainable Energy 38(6): e13239. Khoshgoftar, M.M.H. and Rosen, M.A. 2018. Combined cycle and steam gas-fired power plant analysis through exergoeconomic and extended combined pinch and exergy methods. Journal of Energy Engineering 144(2): 04018010. Koohi-Fayegh, S. and Rosen, M.A. 2017. Optimization of seasonal storage for community-level energy systems: status and needs. Energy, Ecology and Environment 2(3): 169–181. Koohi-Fayegh, S. and Rosen, M.A. 2020. A review of energy storage types, applications and recent developments. Journal of Energy Storage 27: 101047. Kool, E.D., Cuomo, M.A., Reddy, B.V. and Rosen, M.A. 2018. Multi-generation renewable energy system for dairy farms: exergy analysis. European Journal of Sustainable Development Research 2(3): 37. Kumar, A., Demirel, Y., Jones, D.D. and Hanna, M.A. 2010. Optimization and economic evaluation of industrial gas production and combined heat and power generation from gasification of corn stover and distillers grains. Bioresource Technology 101: 3696–3701. Kwok, R. 2019. AI empowers conservation biology. Nature 567: 133–134. Mahmoudan, A., Esmaeilion, F., Hoseinzadeh, S., Soltani, M., Ahmadi, P. and Rosen, M.A. 2022. A geothermal and solar-based multigeneration system integrated with a TEG Unit: Development, 3E analyses, and multi-objective optimization. Applied Energy 308: 118399. Mahmoudi, S.M.S., Sarabchi, N., Yari, M. and Rosen, M.A. 2019. Exergy and exergoeconomic analyses of a combined power producing system including a proton exchange membrane fuel cell and an organic rankine cycle. Sustainability 11(12): 3264. Mahmoudi, S.M.S., Akbari, A.D. and Rosen, M.A. 2022. A novel combination of absorption heat transformer and refrigeration for cogenerating cooling and distilled water: Thermoeconomic optimization. Renewable Energy 194: 978–996. Manesh, H.K. and Rosen, M.A. 2018. Combined cycle and steam gas-fired power plant analysis through exergoeconomic and extended combined pinch and exergy method. J. Energy Eng. 144(2): 04018010. Matzen, M., Alhajji, M. and Demirel, Y. 2015a. Technoeconomics and sustainability of renewable methanol and ammonia productions using wind power–based hydrogen. Advanced Chem. Eng. 5: 128. Matzen, M., Alhajji, M. and Demirel, Y. 2015b. Chemical storage of wind energy by renewable methanol production: Feasibility analysis using a multi-criteria decision matrix. Energy 93: 343–353. Matzen, M. and Demirel, Y. 2016. Methanol and dimethyl ether from renewable hydrogen and carbon dioxide: Alternative fuels production and life-cycle assessment. J. Cleaner Production 139: 1068–1077. Mehrpooya, M., Ansarinasab, H., Moftakhari Sharifzadeh, M.M. and Rosen, M.A. 2018. Conventional and advanced exergoeconomic assesments of a new air separation unit integrated with a carbon dioxide electrical power cycle and a liquefied natural gas regasification unit. Energy Conversion and Management 163: 151–168. Moharramian, A., Soltani, S., Rosen, M.A., Mahmoudi, S.M.S. and Jafari, M. 2019. Conventional and enhanced thermodynamic and exergoeconomic analyses of a photovoltaic combined cycle with biomass post firing and hydrogen production. Applied Thermal Engineering 160: 113996. Mohr, A. and Raman, S. 2013. Lessons from first generation biofuels and implications for the sustainability appraisal of second generation biofuels. Energy Policy 63: 114–122. doi: 10.1016/j.enpol.2013.08.033. Nazari, M.A., Maleki, A., El Haj Assad, M., Rosen, M.A., Haghighi, A., Sharabaty, H. and Chen, L. 2021. A review of nanomaterial incorporated phase change materials for solar thermal energy storage. Solar Energy 228: 725–743. NCBI, National Center for biotechnology Information. 2014. The Nexus of Biofuels, Climate Change, and Human Health: Workshop Summary. Washington, DC: The National Academies Press. https://doi.org/10.17226/18493. Nguyen, N. and Demirel, Y. 2010. Retrofit of distillation columns in biodiesel production plants. Energy 35: 1625–1632. Nguyen, N. and Demirel, Y. 2013. Economic analysis of biodiesel and glycerol carbonate production plant by glycerolysis. J. Sustainable Bioenergy Systems 3: 209–216. Nielsen, S.N., Müller, F., Marques, J.C., Bastianoni, S. and Jørgensen, S.E. 2020. Thermodynamics in ecology—An introductory review. Entropy 22(8): 820. Nishant, R., Kennedy, M. and Corbett, J. 2020. Artificial intelligence for sustainability: Challenges, opportunities, and a research agenda. International Journal of Information Management 53: 102104. 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Energy Analysis  263 Ondrey, G. 2019b. Accelerated electrons unlock sugars from cellulosic biomass. Chem. Eng. March: 11 (www. chemengonline.com). Ondrey, G. 2019c. Power-to-X: Batteries not required. Chem. Eng. January: 14–17. Ondrey, G. 2020a. Integrated power-to-x-to-power hydrogen gas turbine. May: 29–31. Ozturk, H.H. and Demirel, Y. 2004. Exergy-based performance analysis of packed bed solar air heaters. International Journal of Energy Research 28: 423–432. Pantelides, C.C. and Renfro, J.G. 2013. The online use of first-principles models in process operations: Review, current status and future needs. Computers & Chemical Engineering 51: 136–148. Pavlos, D. and Rahat, J. 2020. A review of ammonia as a compression ignition engine fuel. International Journal of Hydrogen Energy 15: 7098–7118. Pinto, F.S., Zemp, R. and Jobson, M.R. 2011. Thermodynamic optimization of distillation columns. Chem. Eng. Sci. 66: 2920–34. Ptasinski, K.J. 2016. Efficiency of Biomass Energy. An Exergy Approach to Biofuels, Power, and Biorefineries. Wiley, New York. Rahnama, E., Aghbashlo, M., Tabatabaei, M., Khanali, M. and Rosen, M.A. 2019. Spatio-temporal solar exergoeconomic and exergoenvironmental maps for photovoltaic systems. Energy Conversion and Management 195: 701–711. Reynolds, P. 2020. The sustainability future for energy and chemicals, ARC Strategy Report, September. 2020. https://www.aspentech.com/en/resources/report/arc-strategy-report-the-sustainability-future-for-energy-andchemicals. Rezaie, B., Reddy, B.V. and Rosen, M.A. 2018. Exergy assessment of a solar-assisted district energy system. The Open Fuels & Energy Science Journal 11: 30–43. Rosen, M.A., Tang, R. and Dincer, I. 2004. Effect of stratification on energy and exergy capacities in thermal storage systems. Int. J. Energy Research 28: 177–193. Rosen, M.A., Dincer, I. and Kanoglu, M. 2008. Role of exergy in increasing efficiency and sustainability and reducing environmental impact. Energy Policy 36(1): 128–137. Rosen, M.A. 2011. Economics Smith and Exergy: An Enhanced Approach to Energy Economics. Nova Science Publishers, Hauppauge, NY. Rosen, M.A. 2012a. Environment, Ecology and Exergy: Enhanced Approaches to Environmental and Ecological Management. Nova Science Publishers, Hauppauge, NY. Rosen, M.A. (ed.) 2012b. Energy Storage. Nova Science Publishers, Hauppauge, NY (invited). Rosen, M.A. 2013a. Assessing global resource utilization efficiency in the industrial sector. Science of the Total Environment 461-462: 804–807. Rosen, M.A. 2013b. Using exergy to assess regional and national energy utilization: a comparative review. Arabian Journal for Science and Engineering 38(2): 251–261. Rosen, M.A. (ed.). 2015. Energy Storage. Routledge/Taylor and Francis (part of series: Critical Concepts in Natural Resources). Rosen, M.A. and Koohi-Fayegh, S. 2016. Cogeneration and District Energy Systems: Modelling, Analysis and Optimization. Institution of Engineering and Technology, London. Rosen, M.A. and Koohi-Fayegh, S. 2017. Geothermal Energy: Sustainable Heating and Cooling Using the Ground. Wiley, London. Rosen, M.A. 2021. Exergy analysis as a tool for addressing climate change. European Journal of Sustainable Development 5(2): em0148. Rosen, M.A. 2022. Exergy consumption and entropy generation rates of earth: an assessment for the planet and its primary systems. Energy, Ecology and Environment 7(1): 1–13. Rosen, M.A. and Farsi, A. 2022. Sustainable Energy Technologies for Seawater Desalination. Academic Press/ Elsevier, New York. RSB. 2009. Roundtable on Sustainable Biofuels. Global principles and criteria for sustainable biofuels production, version 0.5. Swiss Federal Institute of Technology, Lausanne, Switzerland. http://cgse.epfl.ch/page65660en.html. Sadeghi, S., Ghandehariun, S. and Rosen, M.A. 2020. Comparative economic and life cycle assessment of solar-based hydrogen production for oil and gas industries. Energy 208: 118347. Saker, M. 2022. AI/ML solutions in Energy Transition. Power Generation/Oil & Gas, GE Digital. Salimi Delshad, M., Momenimovahed, A., Mazidi, M.S., Ehyaei, M.A. and Rosen, M.A. 2021. Energy, exergy, exergoenvironmental, and exergoeconomic (4E) analyses of a gas boosting station. Energy Science & Engineering 9(11): 2044–2063. Sciubba, E. 2019. Exergy-based ecological indicators: From thermo-economics to cumulative exergy consumption to thermo-ecological cost and extended exergy accounting. Energy 168: 462–476.

264  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence Searchinger, T., Heimlich, R., Houghton, R.A., Dong, F. et al. 2008. Use of U.S. croplands for biofuels increases greenhouse gases through emissions from land-use change. Science 319(5867): 1238–1240. Seyyedi, S.M., Hashemi-Tilehnoee, M. and Rosen, M.A. 2018. Exergy and exergoeconomic analyses of a novel integration of a 1000 MW pressurized water reactor power plant and a gas turbine cycle through a superheater. Annals of Nuclear Energy 115: 161–172. Shahbeig, H., Shafizadeh, A., Rosen, M.A. and Sels, B.F. 2022. Exergy sustainability analysis of biomass gasification: a critical review. Biofuel Research Journal 9(1): 1592–1607. Singh, D., Sandhu, S.S. and Sarma, A.K. 2018a. An investigation of green diesel produced through hydro-processing of waste cooking oil using an admixture of two heterogeneous catalysts. Energy Sources 40: 968–976. Singh, V., Dincer, I. and Rosen, M.A. 2018b. Life cycle assessment of ammonia production methods. pp. 935–959. Chapter 4.2 in Exergetic, Energetic and Environmental Dimensions. Dincer, I., Ozgur Colpan, C. and Kizilkan, O. (eds.). Academic Press. Szargut, J. 2005. Exergy Method: Technical and Ecological Applications. Southampton, UK: WIT Press. Theising, T.R. 2016. Preparing for a Successful Energy Assessment. AIChE, 9 May: 9. Turton, R., Shaeiwitz, J.A., Bhattacharyya, D. and Whiting, W.B. 2018. Analysis, Synthesis and Design of Chemical Processes. 5th ed., Prentice Hall, Upper Saddle River. Tusso-Pinzón, R.A., Castillo-Landero, A., Matallana-Pérez, L.G. et al. 2020. Intensified synthesis for ethyl lactate production including economic, sustainability and inherent safety criteria. Chem. Eng. Process.-Process Intensif. 154: 108041. Valente, A., Iribarren, D. and Dufour, J. 2021. Comparative life cycle sustainability assessment of renewable and conventional hydrogen. Science of The Total Environment 756: 144132. Valera-Medina, A. et al. 2015. Ammonia, methane, and hydrogen for gas turbines. Energy Procedia 118–125. Valera-Medina, A. et al. 2018. Ammonia for power. Progress in Energy and Combustion Science 63–102. Wang, X. and Demirel, Y. 2018. Feasibility of power and methanol production by an entrained-flow coal gasification system. Energy & Fuels 32: 7595–7610. White, D.C. 2012. Optimize energy use in distillation. CEP 2012; March: 35–41. Xue, C., Zhao, J., Chen, L., Yang, S.-T. and Bai, F. 2017. Recent advances and state-of-the-art strategies in strain and process engineering for biobutanol production by Clostridium acetobutylicum. Biotechnology Advances 35: 310–322. Yazdi, M.R.M., Ommi, F., Ehyaei, M.A. and Rosen, M.A. 2020. Comparison of gas turbine inlet air cooling systems for several climates in Iran using energy, exergy, economic, and environmental (4E) analyses. Energy Conversion and Management 216: 112944. Zhang, W., Maleki, A., Rosen, M.A. and Liu, J. 2018. Optimization with a simulated annealing algorithm of a hybrid system for renewable energy including battery and hydrogen storage. Energy 163: 191–207. Zvolinschi, A., Kjelstrup, S., Bolland, O. and van der Kooi, H.J. 2007. Exergy sustainability indicators as a tool in industrial ecology. Journal of Industrial Ecology 11: 85–98.

Chapter 8

Artificial Intelligence INTRODUCTION and OBJECTIVES This chapter provides an understanding of artificial intelligence (AI) in general, through coverage of such topics as industrial artificial intelligence, information system engineering, digital industry platforms, and cybersecurity. Descriptions are also included of the three elements for industrial AI: data science and AI, software at scale, and domain expertise. Coverage is also provided of topics that link sustainability and AI, including AI and process intensification as well as AI and sustainable engineering. Finally, case studies are provided to illustrate and demonstrate AI and its applications. The bottom line of this chapter is that AI can play a significant role in shifting society and industry towards sustainability in general and in achieving the 17 UN sustainable development goals. The main objectives of the chapter are: • Data processing and AI • Digital industry platforms • Impact of AI on process intensification • Impact of AI on sustainable development • Cybersecurity

8.1  Industrial Artificial Intelligence Industrial Artificial Intelligence (AI) is a systematic, collaborative, and integrative discipline focusing on developing, embedding, and deploying various purpose-oriented machine learning algorithms, domain‑specific industrial applications with sustainable business value for capital-intensive, process industries. Real-life behaviors of complex interconnected assets, processes and systems are defined by design characteristics and capacity limits. AI operates and manages the asset within the fundamentals of science (Pendyala 2020a, Ahmad et al. 2019, 2021). Industrial AI enables companies to: • Solve complex problems more easily, • Accelerate and increase value creation with higher-quality data, greater accuracy, and improved access to enterprise data, • Take advantage of intuitive guidance to better support their workers, • Automate and simplify the creation and sustaining of models, and • Reduce the total cost of ownership. Industrial AI combines the fundamentals of engineering with AI capabilities and purpose-built software leading to the self-optimizing plant. The deployment of AI covers the full asset life cycle,

2

266  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence

Industrial Expertise

Fist principles Design operation & maintenance Know-how Plant safety Sustainable

Sustainable

Data & AI

Machine learning Statistics Deep learning Optimization

Industrial AI Resilient

Software

Modeling/scaling Computing Data analytics Simulation Cloud

Figure 8.1.  Industrial artificial intelligence as the overlap of industrial expertise, data sciences, software and modeling (Pendyala 2020a,b).

a,b).

,

adding performance engineering and asset performance management options to those production optimization capabilities (Peiretti and Brunel 2018, Duan et al. 2019, Abduljabbar et al. 2019, Ding et al. 2021). • C As Figure 8.1 shows, the three elements for the industrial AI are industrial expertise with first overall principles, dataincrease sciences withproductivity. machine learning, and modeling with scaling. Data science and AI • AI of capabilities mainly consists machine tolearning, deep learning, reinforcement learning, and optimization. can leverage one or more AI capabilities to forecast when asset needscomputing, to be serviced. Software modeling and scaling consist of high performance andandistributed data • Various machine learning methods to deliver the preanalytics, cloud, and visualization of workflow. Industrial expertise consists of deep industry know-how, first principles, operational process knowledge, and process safety management. network models to semi-supervised learning for pattern detection. Industrial AI intercepts all three elements of data science and AI, software and modeling, and industrial expertise, and hence offers comprehensive asset operation and management for capital intensive industries. This helps deliver a comprehensive business outcomes for the specific business needs of industries (Ahmad et al. 2019, Pendyala 2020a,b, Isensee et al. 2021). requisites to the enabling AI and ( intelligence has the following three (Figure • Cases and business applications thatdimensions leverage data to 8.2): provide its stakeholders with greater • Applications: Intelligent agent, extended value, such as prescriptive maintenance to reduce unscheduled downtime, improve the asset

AI can be viewed across three levels:

lifespan, and increase overall productivity. • AI capabilities to be utilized to power the business application or intelligent software agents • AI capabilities that can leverage one or more AI capabilities to forecast when an asset needs to be serviced. systems learning methods to deliver the pre-identified AI capability based on various • Various machine • learning Machine methods, learning:ranging Deep from learning and shallow reinforcement, machine supervised learninglearning, using regression and/or supervised, neural unsupervised. network models to semi-supervised learning for pattern detection.

8.1.1  The Constellation of Artificial Intelligence The constellation of AI is a framework that enterprises can use to build out their AI program. The framework emphasizes the business value of the use 2 case and application, helps focus on the pre-requisites to the enabling AI and machine learning (ML) capabilities as a critical pillar

Artificial Intelligence  267 APPLICATIONS Intelligent agent

Intelligent automation

INDUSTRIAL AI

Knowledge representation Data analytics

Audio Signal processing

MACHINE LEARNING Deep learning Reinforcement Supervised

Robotics Expert systems

Predictive systems

Recommendations Intelligent products

Figure 8.2.  Layers of machine learning, artificial intelligence, and applications in the constellation of artificial intelligence (Pendyala 2020a).

of an enterprise-level digital transformation (Pendyala 2020a,c, López-Guajardo et al. 2021). The constellation of artificial intelligence has the following three dimensions (Figure 8.2): • Applications: Intelligent agent, extended reality, collaborative robotics, biometric, facial and gesture recognition, text, speech, image and video analytics, personalization, intelligent automation, intelligent products, recommendation systems. • AI capabilities: Knowledge representation, computer vision, natural language understanding, audio signal processing, speech to text, speech and optimization, expert systems (inference), predictive systems • Machine learning: Deep learning and shallow learning, reinforcement, supervised, and unsupervised. Each AI use case and business application can enable enterprises to build a holistic AI program. This helps analyze the business value for each AI initiative and understand the requirements necessary to invest in an AI program. Machine learning Machine learning (ML) results from pattern recognition and learning from data, or previous calculations, to develop decisions and results. Machine learning capability comes from the ability of data analysis that automates analytical model building. Data mining and Bayesian analytics with enhanced computational abilities can produce models that can analyze complex data and produce accurate results for process improvements and better business opportunities. Therefore, ML acts as a subset of AI to train a machine or a process how to learn and manage data toward automated analytical model building (Saldana et al. 2021, Ding et al. 2021, López-Guajardo et al. 2021, Nguyen and Bavarian 2022). Some key points of machine learning follow: • The overall goal of using ML strategies is to augment human intelligence and improve safety (see Figure 8.3). • ML can use pattern recognition to find inefficiencies. • ML can deal with poorly defined problems, noisy data, and nonlinearities—all of which are common problems in manufacturing sector.

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Machine learning • Process industry • Data processing • Model selection • Training • Performance assessment

Language processing

Expert systems

Artificial Intelligence

Computations

Robotics

Figure 8.3.  Artificial intelligence with machine learning steps.

8.1.2  Artificial Intelligence and Analytics Industrial AI is purpose built and applies the basic principles of physics, chemistry, reaction kinetics, and mass and energy balances. This leads to sophisticated set of models to increase efficiency by tuning them with real-world data and experience for the industrial and manufacturing sectors. The use of data for asset management provides the ability to anticipate equipment failures and avoid costly downtime due to outages. Sophisticated data analytics facilitates the regulatory case for approval for sustainable investments for the company, the customer, and the environment. The emergence of tools enabled by AI provide predictive analytics, resilience, and reliable operation. For example, data-driven modeling is a key for power utilities to anticipate and control the behavior of distributed energy resources, and to improve distribution grid resilience. For water utilities, digital tools help dynamically plan and prioritize capital improvement based on scenarios of severe storms, market dynamics, and human resources (Hebrault et al. 2022). Industrial AI combines the fundamentals of engineering and domain expertise into purpose-built software solutions. Some design packages offer solutions for companies with AI capability toward the Self-Optimizing Plant.

8.1.3  Opportunities and Challenges of Digitization AI-assisted development of predictive maintenance agents enables rapid deployments at enterprise scale and support scalability. Industrial AI with the basic principles creates sophisticated sets of models to increase efficiency and enhance the accuracy of existing models and approaches by tuning them with real-world data and experience. AI drives significant efficiencies for those companies with multiple production sites, distribution networks, and selecting the optimum strategies amongst thousands of simulated alternatives. Digitization enables the workforce to work remotely, often leading to cost savings and efficiency improvements. Data analytics is becoming core to operations. However, data analytics need training and supporting workforce with digital knowledge, which would be an essential criterion for new hires. With industrial AI, the roles of the workforce in on-the-ground teams become increasingly virtual or remote with added ability for decision-making and interactive operator training capabilities, based on past industrial experience (Peiretti and Brunel 2018, Barrientos 2019, Rachinger et al. 2018).

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With technology and available data, for example, industrial entities can execute on increasingly aggressive decarbonization plans and respond to emergencies effectively. This brings confidence to service providers to transform their operations and provide even more reliable, and resilient service to customers. However, any effort at digitization must include the evolving cybersecurity environment, including utility collaboration and coordination with the government to share knowledge and protect against these threats (see Section 8.8). With cybersecurity, embracing data is an opportunity for utilities to transform their operations while also building a new paradigm to offer best-in-class customer experiences. For remote working, security and data protection remain a priority and are managed with sound practices and policies (Vogl et al. 2019).

8.1.4  The Industrial AI Readiness Checklist While data seem like the most practical area to “get ready” before the organization adopts AI, it needs to be purposefully guided by key skills and business goals (Beck 2020a, Morse 2020, Schwartz 2020, Pendyala and Morse 2020). The elements of industrial AI readiness are now described, in terms of four main dimensions (Pendyala 2020c): Strategy: The first dimension of strategy can be described as the planning and alignment of AI into enterprise-wide business goals, data objectives, high-value use cases and measurable key priorities. The outcome is a clear action plan for the desired levels of industrial AI readiness in the organization. In this way, AI becomes an enhancement to the enterprise’s digital transformation strategy. This is critical in enabling organizations to: • Embed data-driven thinking into their fabric • Reconcile the leadership mandate with grassroots support • Benefit from traverse impact across every function Exploring and identifying AI-enabled use cases may begin with identifying the business problems, corporate objectives, and strategic goals. People: This dimension refers to the mindset, roles and skills required to develop, deploy, and deliver AI-enabled initiatives, internal and external to the enterprise. Even the most innovative AI solutions will not be effective if people are not organized and motivated to use them. In this area, enterprises need to align leadership, company culture and change management to ensure people are ready, willing and able to use AI. For people to successfully build and work with AI solutions, there needs to be: • Business and technical training • Continuous job support  • Meaningful inclusion in the strategy and process of developing and deploying AI solutions Data: With no industrial data, there is no Industrial AI. The main questions are how much, how frequent, and how reliable is the data. These questions need to be supported by an overarching industrial data strategy. Infrastructure: Infrastructure refers to the tools and workflows for powering Industrial AI across the life cycle. It also covers the software, hardware and enterprise architecture needed to productize AI in industrial environments, including broader collaboration between development, data science, and infrastructure capabilities. Companies need an industrial infrastructure strategy to: • Significantly reduce implementation risks and accelerate time-to-market • Gain operational flexibility and scale to future-proof AI investments • Harmonize the AI model life cycle across various Industrial AI applications

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8.1.5  Execution of Industrial AI Performance engineering can enable companies to solve complex problems and optimize, using enterprise data, AI, and the cloud, with the following features: • Hybrid models revolutionizes the model creation and deployment workflow to build sustainable models and drive an alliance with production optimization, • Multi‑case analysis means one can analyze designs across all operating cases and visualize the operations space to drive optimal performance, • Cost estimation insights make every new estimate better than all past estimates by delivering continuous organizational learning, and • Asset performance management solutions is now increasing the advanced notification for assets failures and their downtime and hence reducing the cost of maintenance (Brooks 2020). Some of the important steps in the execution of industrial AI are as follow: • Step 1: Define Business Value - What are your most critical business problems today? Which will yield faster a data-driven digital twin approach? Can you map the digital twin to the value that it will provide in addressing your business initiative? • Step 2: Learn from Case Studies of Early Experiments - For asset integrity, process safety, sustainability, and energy optimization, there should be substantial information sharing. • Step 3: Define a Stepwise Approach - By defining the problem clearly, an effective digital twin can be developed and implemented. This can then be expanded to four other similar process systems and applied to a completely different asset type to solve a different business problem in another part of the world. • Step 4: Evaluate - Since technology is moving very fast in the areas of data cleaning, data analytics, fast-performing online models and Industrial AI, there is a significant benefit in selecting a partner and adopting technologies that embed data science and Industrial AI. Measuring success with respect to a baseline will make course corrections and broaden digitalization efforts as they proceed, allowing AI is likely to build up a long-term competitive advantage. As Figure 8.4 shows, implementation and deploying AI starts with a business initiative with clear business objectives. After that selection a process, modeling and a roadmap integration business with operation follows. Proper safety and environmental conduct are necessary for maintaining a manufacturer’s license to operate. For example, asset performance management software applications help in reducing productivity losses by avoiding breakdowns through timely warnings. Advanced AI and machine learning can deliver failure alerts, which is particularly important if there is no clear prepared plan to keep staff safe and avoid potential problems (e.g., spills or gas flaring). An Industrial AI approach can cover virtual sensors, predictive models, reduced order models, simulated processes, and equipment, thereby predicting quality, and pollutants so as to permit reduced emissions and carbon footprints (Seele and Lock 2017, Piccarozzi et al. 2018, Morse 2020, Miceli et al. 2021). Clear communication and process documentation are critical to mitigate the risks of AI’s complexity and autonomy, and to understand the limitations and benefits of AI. Black box models When industries effectively deploy AI systems, the workforce typically makes less decisions on operations but focuses instead on higher level strategic and conceptual decisions. These decisions are evaluated by checking outcomes and quantifying how often the AI system has made the right decision. But the machine will make a mistake sometimes and it is necessary to know how the machine arrived at the wrong decision by being able to trace back every step that led the decision

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Business Initiative • Key business initiatives, Constraints,Readieness, constraints, readieness, Current current plant data data

Process • Required digital transformation in business improvements, Targeted economic gains targeted economic gains

Models • Match goals with organizational capabilities

Roadmap • Plan Plan operation operationand andearnings, earnings. Scaling, scaling, Clearbusiness businessobjectives objectives clear

Figure 8.4.  Aspects of the implementation of artificial intelligence.

making. However, many state-of-the-art AI systems, called black box models, do not offer the option Figure Aspects of the 2020). implementation of artificial intelligence. to explain their8.4. decisions (Serneels AI systems with black box models tend to be better predictors across a wide range of applications compared with intrinsically explainable models (Usman et al. 2019). Surrogate models are only used to try and explain the black box model. It is also possible for models to learn explanations along with decisions, when a big data set of explanations is available. AI may be involved in the series of decisions leading to an accident, due to an impenetrable decision taken by a black box algorithm. In addition, product quality may often require explainable AI. There are some checks in place beyond the AI algorithm’s predictions to make sure that out-of-spec material does not reach the customer. AI enabled engineers design an optimal plant at every level of detail and can analyze the root cause of an event in the plant without years of data history (Pendyala 2020a,b). Self-tuning adaptive control can align with new operating techniques and strategies with continuously optimizing the key process units to maximize uptime and prevent downtime. Asset health analytics and monitoring tools with machine-learning-based prescriptive maintenance can avoid equipment failure and improve plant uptime (Figure 8.5). Data collected during the learning mode is used for building machine learning through a neural network model. Then the model can predict the behavior of renewable power generation systems. Therefore, AI/ML becomes an integral part of reliable and agile renewable power generation 8 aimed at decarbonization. This can lead to sustainable operation and continuous renewable power generation (Saker 2022).

Learning

Building

Controlling

•Identify the space of operation

•Build machiene learning model with training data

•Find optimal operation for current time and space

Figure8.5.  8.5. Steps in autonomous artificial(Saker intelligence Figure Steps in autonomous tuning intuning artificialin intelligence 2022).

(Saker, 2022).

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8.1.6  Agility and Digitalization Agility is a skill-based ability that helps an organization act quickly in space and time with accuracy. In a volatile, uncertain, and complex environment, companies need to operate with insight and agility. Companies need the depth of their resilience and learn how to navigate demand, supply, workforce, and economics. Digitalization is a strategic lever for making, for example, a refinery operation more agile and more flexible (Beck 2020b, Rachinger et al. 2018). Agility enabled models can quickly find a solution with a global optimum and can continuously update to capture the sustainable operating conditions. For example, the most agile companies are those adopting the digital twins as a virtual copy of company’s assets and assimilating digitalization into their operations. This enables the business to unlock the potential of the accumulated data to adjust toward profitable operation. Digital twin models can be used to inform the planning model of operating scenarios to improve collaboration among remote workers. Energy companies and manufacturing sector entities continue to focus on sustainability goals with a progressive recovery agility and flexibility. Competition and sustainability issues are forcing the manufacturing sectors to adopt advanced manufacturing paradigms like agile manufacturing that enables an organization to survive in the competitive business environment. Therefore, agility is the performance measure of manufacturing practices needed for survival. Therefore, a combination of agility and sustainability is needed as a performance measure for industrial sector (Vinodh 2010, Miceli et al. 2021). Increasing the cost availability of renewable energy sources requires agility in producing power so as to match supply and demand. Power agility demands that the power generation must respond quickly to the variability in time and quantity of renewable resources. AI can help synchronize the generation to the demand and lead to agility by integrating metrological data patterns with the solar and wind-based power generation. This kind of autonomous tuning works with learning, building, and controlling modes, as shown in Figure 8.5 (Rachinger et al. 2018).

8.1.7  Challenges with Industrial Artificial Intelligence Artificial intelligence systems have both positive and negative impacts on sustainable development in electronic markets. However, the most important component in using AI technology is to consider social and ethical considerations. The AI revolution has harmed the market and economy with historic mass unemployment and an uncertain job market shift. Furthermore, advances in AI are raising security and privacy concerns, particularly as social media platforms emerge. AI should be developed in a controlled environment with precise and aligned data collection to fulfill goals (Thamik and Wu 2022). Artificial intelligence has great potential in reducing energy consumption, environmental burdens, and operational risks of chemical production. One barrier is the lack of quantitative understandings of the potential benefits and risks of different AI applications. A conceptual framework from industrial ecology, economics, and engineering can guide the selection of performance indicators and evaluation methods for a holistic assessment of AI’s impacts. This framework could be a valuable tool to support the decision-making related to AI in the fundamental research and practical production of chemicals (Liao et al. 2022, Sullivan et al. 2018). Artificial intelligence is empowering the products and services. However, it may create challenges including those that are behavioral, cultural, ethical, social, and economic (Figure 8.6). These fundamental challenges have raised serious questions for the sustainable development of electronic markets. Some key issues are the security and privacy of consumers, AI biases, individual autonomy, wellbeing, and issues of unemployment. Therefore, companies that use AI systems need to be socially responsible and make AI systems as secure as possible to promote the sustainable development of countries (Thamik and Wu 2022, Wu et al. 2019).

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Establishing artificial intelligence

Behavioral, cultural issues

Effects on human health

Effects on market and economy

Ethical and social issues

Security and privacy risks

Accountability and legal issues

Figure 8.6.  Some conceptual impacts of AI associated with risk and social issues (Thamik and Wu 2022).

Figure 8.6 Some major AI challenges are as follows (Kiel et al. 2017, Duan et al. 2019, Masood and Egger 2019, Ahmad et al. 2021):

• AI is affecting the human workforce, causing it to change and evolve. Humans may lose jobs to • New AI is affecting the human workforce, ca machines. responsibilities that may require unique human abilities should be designed. • AI creates • extra pressure on society and may change human behavior and may stress people may psychologically, making work even more challenging for earning a living. • do AInot systems do not show anyfeelings. human- It can drag the entire world into an AI conflicts • AI systems show any human-like that could cause significant setbacks • • Data power AI algorithms, and as more and more data are collected about every individual’s demographics, our privacy may be compromised. • • with machines is a challenge for society as it may change behaviors. . • Interaction • AI failures could cause massive destruction if not managed and checked properly. Ethical and social issues • Some ethical andinsocial issues are relevant in this field, including the following: wellbeing. • Social issues include the potential for large-scale unemployment, reduced autonomy, and a decline in wellbeing. 9 • AI platforms such as social media may have had adverse effects ethically and socially by engaging people online, resulting in addictive behaviors. • Digital addiction is widespread and causes disturbances that negatively influence individual academic or organizational performance and relationships. • Security and privacy issues • AI systems enhance chances to access, collect, share the consumers’ personal and corporate information, which may be risky. This requires reinforcing the need for cybersecurity • When AI makes decisions autonomously, its role may go beyond just a support tool. When a problem occurs, it may not be clear whether creator or developer can be held accountable for AI’s decisions • Multidisciplinary boards may take responsibility in complex situations by examining the information delivered, but such exercises are not conclusive all the time.

8.2  Information System Engineering Information is described as data that can be transformed to create a value in a specific context. Data and information are often used interchangeably (Giulini et al. 2020, Elmendorp 2022). Smart

274  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence infrastructure enables data-driven utility operations. Many utilities have access to vast amounts of information through available data from advanced sensors (Goldman et al. 2013). These utilities are enabled by the availability of software as a cloud-based solutions, making it easier to close the digital divide. For water utilities, digital tools are being used to dynamically plan and prioritize capital improvement plans based on extremes. Digitalization can help workforce with the technology to improve safety, achieve decarbonization, and recover of sulfur and NOx. Digital twin models help optimizing asset performance and planning. Examples include troubleshooting operations via online column models and monitoring heat exchanger performance and fouling with online models. These models can monitor the status of equipment and units with respect to process safety, process integrity, emissions, energy use, fouling and degradation (Beck 2020b, Rachinger et al. 2018, Wu et al. 2022). To transition from business-as-usual to digital enabled, capital-intensive operation, businesses need to transform their industrial facilities and value chains into a system of smart, self-optimizing, interconnected semi-autonomous processes. This leads to a better operational performance, agility, and profitable growth while also enabling creation of new business models. The digital transformation journey will change the nature of asset intensive industries, particularly in the energy and manufacturing sectors, and how humans work and interact with intelligent systems and virtual models (Li et al. 2019a,b). Performance management Performance management is a key element of controlling operational risk stabilize processes. This can avoid safety, environmental and production disruptions that can occur when equipment operates beyond its design and safety limits. Unexpected events can disturb the operation, especially when they are occurring on a regular, or even semi-regular, basis and result in prolonged quality, yield, and safety issues. In many cases, frontline workers may be lacking the advanced skills and appropriate tools to quickly analyze and promptly stabilize disturbance conditions, causing delays, lengthening the disturbance, exacerbating the consequences, and often resulting in rushed, ill-advised decisions (Cherrafi et al. 2017, Res and Kenett 2018, Sayyadi 2019, Brooks 2020).

8.2.1  Information Quality Quality has two elements of objective definitions for producing products or delivering services, with measurable characteristics satisfying a fixed set of specifications. These are usually numerically defined and include subjective definitions for producing products or delivering services that satisfy customer expectations for their use or consumption (Goetzer and Volk 2019). According to the ISO 9000 standard, quality is the “degree to which a set of inherent characteristics fulfill requirements” (ISO 9001). According to the American Society for Quality, quality is “a subjective term for which each person or sector has its own definition”. The notion of quality needs: (a) standardized terminology of terms and concepts, (b) metrics and metrology, (c) harmonization and extension of standards, (d) conformance testbeds for standards and (e) development of information models that support sustainability (Demirel 2014). Some key concepts in information quality follow (Res and Kenett 2018): • Physical quality refers to all physical properties, including those that are geometrical, mechanical, and material. Physical products are directly interconnected with the information about them, which includes: (a) inputs for the manufacturing environment (e.g., information on the raw materials), (b) a manufacturing environment/design, and (c) outputs for the manufacturing environment (product specifications). • Information quality refers to all product/process information traceable throughout the product life cycle and involved processes. The four aspects of information quality typically are: (a) identification of the information quality dimensions, (b) definition of the dimensions, (c) classification of the dimensions and (d) metrics for the evaluation of dimensions.

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• Indicators represent a single parameter of a system, for example greenhouse gas (GHG) emissions or energy use and can be descriptive, normalized, comparative, structural, intensity, decomposition, causal, consequential, and physical. • Aspects are related to a specific category. Indicators then become the specific measurement of an individual aspect that can be used to demonstrate the status and performance of a system. • Indices combines of several indicators. Ecological Footprint (a ratio of the amount of required to available land and water for a population) or Environmental Vulnerability Index (comprised of indicators of hazards, resistance, and damage) are examples. Proper methods of normalization, weighing and aggregation are a pre-requisite for indices. • Frameworks combine large numbers of indicators in qualitative ways such as the vulnerability framework. They are hard to compare over time.

8.2.2  Information and Data Management Quality helps organizations reduce waste, minimize costs, and maximize impact and safety. Organizations are also trying to build a culture of sustainability, applying quality management to design processes supporting environmental protection with reduced emissions (Siva et al. 2016, Freeman 2019, Radziwill 2019). There is a critical need for access to industrial analytics and actionable insights in making business decisions. Therefore, organizations focus on data issues, ranging from data collection to strategic data management. This needs the AI-enabled technologies in data integration, data mobility, and data accessibility across the organization. The selection of fit-for-purpose industrial AI applications with data science can be key to overcoming a lack of existing workforce expertise and the need for data scientists. This is possible by reducing complexity in integrated data processing with a cloud-ready infrastructure that can be easily expanded and upgraded (Goldman et al. 2013, Geotzer and Volk 2019, Bua et al. 2020, Giulini et al. 2020). Information processing and sustainability Information entropy is the average amount of information conveyed by an event when considering all outcomes. This means that entropy is a measure of uncertainty in the system. The derivations of the maximum information entropy procedure are not defined by a probability space but by a path space for nonequilibrium systems (Demirel 2014, Demirel and Gerbaud 2019, Li et al. 2019a,b). Quantifying chemical structures, signal processing, and molecular ensembles may be possible through the information entropies of individual molecules, chemical and physicochemical processes, and chemical structures. A total information entropy of the molecules may be based on the number, type, and arrangement of each atom in a molecule. These arrangements may be partitioned using specific rules. For example, when two vertices are determined to be equivalent, they are designated the same value and counted into the information bit approach. Given such molecular bit information, engineers and scientists may be able to work towards an AI solution that helps a system in self-correction by processing information received and preparing a response. Given this information and AI tools, the system may self-regulate to improve operation (Bua et al. 2020, Sabirov and Shepelevich 2021). A self‑correct system A self-correct system increases sustainability in all three relevant categories: social, economic, and environmental. From a social and safety perspective, the interaction between workers and equipment would be minimal. A self-correcting system would prevent excessive downtime and improve efficiency from an economic perspective. An environmental view of a self-correcting system would leave much less potential for chemical release in downtime and better energy use. This approach is challenging in processing the flow of information in an increasingly complex system such as an entire manufacturing plant. The information flow between different systems would have to be compatible with all other equipment in the plant. A problem may arise when the information receiver

276  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence does not have the means for processing the information, or an alternative response is derived from improper information processing (Li et al. 2019a, b, Pietri 2020a,b). Engineers do not want these alternative responses occurring in a plant as this could lead to unintended safety consequences. In a complex plant, individual units need to be able to both process and generate information for full-scale integration and AI correction approaches. For example, a reactor may use AI tools to self-correct, having developed the knowledge of quantified bit information from individual molecules. Identifying these molecules with machine learning approaches at lower resolutions sets the stage for adaptive control of unit processes that can share information for overall adaptive process control. Another concern may be going from a high-resolution measure of molecule information (bits) to a lower resolution, leading to a loss of information. Giulini et al. (2020) performed entropy mapping experiments on proteins and found that they could still maintain most of the information about proteins using the position of the retained atoms at a lower resolution. An AI solution helps to identify which high-resolution models reflect the low-resolution counterpart with minimal information loss in terms of conformational changes and functional properties for identification (Mizraji 2021). To use information to generate order may be proposed for an enzymatic activity, gene expression, or metabolic mechanisms. Enzymes may be the link between biological systems, information, and thermodynamics. A model developed for studying order phenomena in simple systems can be generalized to more complex forms of matter, including liquid crystals and polymers (Demirel and Gerbaud 2019, Demirel 2014). Bifurcations for information processing The trends and bifurcations for information processing may lead to process intensification. Machine learning presents an opportunity to describe high degree of complexity at a molecular level and apply it to process intensification. At a molecular simulation level, peptides can self-assemble and have possible uses in new materials with specific chemical and physical properties (Kim and Parquette 2012). This may lead to practical approaches to create ML algorithms and neural networks to decrease complexity, in line with process intensification. Some of these models include thermodynamics, morphology, and kinetics of different peptide arrangements (Demirel 2014, Thurston et al. 2016). This may be achieved by: • applying ML to known big data basis for predicting a broad spectrum of thermodynamic properties, • applying ML to simulate molecular interactions to accelerate the discovery of new materials with desired properties, and • eliminating the current lack of understanding between quantum phenomena and atomic level interactions to obtain detailed all-atom molecular level simulations (Ding et al. 2021). For example, a model in ML can be used to predict density and dynamic viscosity of biofuels compounds. The biodiesel model uses a known thermodynamic property database containing extensive data analysis of densities and dynamic viscosity values for hydrocarbons and oxygenated compounds in a temperature range of 88 to 723 K (Saldana et al. 2021, Mizraji 2021). Biocomputing The idea of using biological living cells as a type of substrate for user-defined computations has been part of the new developments in biotechnology. The approach is based on using a type of “genetic circuit” to model an approximation to silicon-based computers leading to cellular approach, which explores the diverse types of functions already working in cells to consider potential pathways in which biocomputing might prove superior to traditional computational systems (Grozinger et al. 2019). For example, the “toggle circuit” based on 1-gene regulatory circuits can be constructed

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from networks from simple regulatory elements in E. coli (Gardner et al. 2000). However, certain aspects of the cell are widely different than our current mechanistic approaches (Zhao et al. 2021, Mizraji 2021). The concept of cellular supremacy seeks the knowledge and application domains where cell-based systems will, naturally, perform considerably better than existing computers (Goldman et al. 2013, Grozinger et al. 2019). The research helps to study how living systems operate in domains that are not necessarily accessible for regular computers, such as environmental bioremediation, bioproduction (TerAvest et al. 2011) and medical applications such as specific targeted therapeutics. The new cell supremacy paradigm should be always analyzed in its proper context, with the understanding that silicon-based computing is qualitatively different than the possible computing performed by living systems. Information processing in living and non‑living systems Recognizing the complexity and broad link that information theory plays in biological systems is a basic step on understanding ways and designing possible paths for process intensification in physical systems and therefore in engineering systems. The possibilities for an in-depth understanding of this were already devised by John Clerk Maxwell in 1871 with his, now famous, Maxwell daemon. In perspective, this early mental experiment is an approach to connect thermodynamics and information. As a complex biological system, in constant use in living creatures and widely used in science and engineering applications, the enzyme is an excellent example of the link between biological systems, information and thermodynamics. Enzymes are biological catalyst and can only work and react within real thermodynamic systems ((Demirel 2014, Mizraji 2021). Machine learning for molecular thermodynamics Thermodynamics, at a molecular level, has a high degree of complexity that escapes most macro models. Machine learning (ML) presents an opportunity to have a better description of this type of systems and apply it to process intensification. Three different aspects can be delimited for the application of ML: • Applying ML to known big data basis and be able to predict a broad spectrum of thermodynamic properties. • Applying ML to simulated molecular interactions to accelerate the discovery of new materials with specific desired properties. • Eliminating the current lack of understanding between quantum phenomena and atomic level interactions to obtain detailed all-atom molecular level simulations (Ding et al. 2021). One example of using ML to determine properties is the development of ML models to predict density and dynamic viscosity of compounds in biofuels (Saldana et al. 2021). For this biodiesel model, a known thermodynamic property database, DIPPR 801 was used. The database contained the data analysis, 5634 densities and 3547 dynamic viscosity values for hydrocarbons and oxygenated compounds, in a temperature range of 88 to 723 K (https://www.aiche.org/dippr/eventsproducts/801-database). At a molecular simulation level, peptides are a very important type of molecules, and it has previously been determined that they can self-assemble and have possible uses in new material with specific chemical and physical properties (Kim and Parquette 2012). Naturally, this has attracted many researchers, and the practical approach is to create ML algorithms and neural networks to work around this complexity. Some of these models include the thermodynamics, morphology, and kinetics of different peptide arrangements (Thurston et al. 2016). Some years ago, the quantum realm, presented challenges that seemed unbreakable, and it was better to leave them to quantum physicists and dismiss possible effects for process engineering. Lately, the necessity of battery technologies and other alloys technology has expanded the search

278  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence for ML models for lithium-based alloys and its force fields in crystalline and amorphous structures (Xu et al. 2020). These models are looking to predict the volume change during initial lithiation, and can be expanded to anticipate bulk densities, radial distribution functions and lithium diffusivity among other properties. Even in thermodynamics systems that might be extensively researched, there is still plenty of opportunity to analyze thermodynamic effect on different levels. One example is water crystallization, where water particles modelled as coarse-grain interactions. These are modeled with no explicit hydrogen, and thus, the ice nucleation process is accelerated, sometimes by several orders of magnitude (Moore and Molinero 2011). These coarse-grain models have been advanced by different researchers. Some of the results have been the calculation of the Landau free energy solely from the positions of the water particles and, even more impressive, the initial development of an in-depth understanding of atomic level phase-changing phenomena (Zhang et al. 2018).

8.3  Digital Industry Platforms Digital technology supports needs of remote and collaborative work, and changes in the roles of the workforce as on-the-ground teams become increasingly virtual. Organizations are considering more work being done remotely with fewer operators, in and around assets. Companies are accelerating their digitalization strategy in refinery, energy and chemical businesses, and rethinking global supply chains that are today highly interdependent and just-in-time in nature. Several companies including energy companies are accelerating digitalization and using it to rebalance capital expenses (CAPEX) and operating expenses (OPEX). Economic modeling and risk tools can rationalize the CAPEX portfolio into a series of scenarios by impact on revenue and sustainability, as well as by financial risk. One can examine optionality of locations, timing and contracting and their impact on agility and workforce. Production planning can be rapidly moved to the cloud for remote applications for safer operations. The key to success is agility enabled with models that can quickly find a solution with a global optimum and can be continuously adjusted to current operating conditions. For example, remote access and workflow tools provide remote workers the ability to react to changes and manage asset production. Asset health can be monitored remotely with prescriptive maintenance analytics to provide early warning of failures and optimize operations for safety, yield, and energy. Self-tuning adaptive control can be adjusted quickly to align with new operating strategies and continue optimizing the key process units (Hahn 2020). Maintaining sustainability efforts Energy companies continue to focus on sustainability goals with digitalization to improve safety, protect the environment and maintain continuity. Progressive recovery, agility and flexibility are key in the energy industry due to unpredictable oil and gas prices, as well as changing patterns of demand. Digitalization is a strategic lever for making a refinery more agile and operation more flexible. For example, ML-based prescriptive maintenance can improve plant uptime. The use of an advanced digital environment enables analytical data to be transformed into information, providing an ability to predict process performance. Digital transformation and automation offer advantages to downstream processing. Automation can help determining how to scale up a process that was created with simple lab equipment to one that is run in a sophisticated and automated manufacturing environment. Automation can remove manual data entry and retrieval, and improve data collection, quality, and accuracy, due to the continuous feedback of data. Further, advanced automation based on advanced sensors and measurements on the production floor can eliminate hold-ups and accelerate decision making about the next steps in a process. This may speed up analysis and correlations between ‘simple’ measurements, as well as the release of new products.

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8.3.1  Smart Sensors Modern instrumentation with built-in diagnostic and behavior monitoring improve not only maintenance but also process reliability. Smart sensors can help process optimization with advanced analytics and networking capabilities, in line with the ultimate vision of industrial productivity improvements and the expected results of integration between machines and humans. Smart sensors can make multiple measurements, including pressure, temperature, level, flow corrosion monitoring, and acoustic sensing. In addition to sensors, one needs to transfer data to a location, often the cloud, for storage and analyzing, and eventually to transfer this the people who can use it. Industry 4.0 provides workers with real-time data that have been analyzed to provide actionable information to control and increase the overall efficiency of processes. Industry 4.0 allows management, in real time, to understand how they are performing from a sustainability and regulatory standpoint, and to know levels of carbon footprint, maintenance, and safety. This initiates a new breed of employee who will have usable data to act. Smart sensors allow technicians to diagnose a device from any location and to access the information to troubleshoot and solve problem (Hauge 2020). Digital transformation Digital tools, such as cloud-based electronic lab notebooks and data analysis software, can lead to efficiently developing successful chemical products. Lower data storage costs, increased computing power, and advanced analytics can help discover and implement new products. Manufacturing and industry face challenges in innovation and research for sustainable long-term growth. Advances in scientific instrumentation, cloud-based storage options, and computing power have started the age of real-time data sharing and digital innovation (Roldan et al. 2018). Product development in the digital age Internal or consumer demand drives the need for product development. At present, researchers search published data and their past experiments and then design experiments to test the hypothesis and relevant parameters, collecting and documenting data as they go. Consider the development of a water-based coating. Chemists may need to search through existing experimental data to answer such questions as: Have water-based coatings been developed? If so, what features need to be changed to meet the new specifications? A search of the literature will be done separately using databases. During product testing, results will be laboriously recorded, then shared with team members or other collaborators largely with meetings or email, which could create lags. Data analysis may be conducted with discrete software. Multiple formulation or materials would be tested and the data analyzed, to decide whether a desired product can be manufactured. If no candidate seems viable, analysis may inform another ideation and experimentation phase for a further development round, which can be expensive and time consuming (Sharma 2021, Sloan et al. 2020). On the other hand, digital transformation and data recording and storage allow researchers to search and analyze their data quickly to identify product leads. Digital tools can be implemented throughout product development and augmented by analytics and artificial intelligence leading to faster and smoother innovation. In implementing digital tools, companies can choose either digital record keeping, or integrated digital transformation throughout the product development process and communication with customers (Tosic and Zivkovic 2019). Researchers can develop an internal vocabulary of custom tags to help them search quickly and to select related data. For example, this could be as broad as tagging with “adhesives” to encompass all research within that area or as granulated as “polyvinyl acetate” to identify experiments related to that one type of adhesive. Tagging can help researchers find relevant experiments during data analysis. The recorded information will likewise be searchable, allowing researchers to refer to experiments using only that information (Stridiron 2020, Costa et al. 2020, Li et al. 2020, Tremblay 2020, Riedelsheimer et al. 2021).

280  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence Collaborative communication with electronic lab notebooks Digital transformation helps with collaborations across organization and around the globe, so sending data between people can help move projects forward in an interconnected provided manner with security measures to prevent inappropriate sharing and to ensure compliance with regulations related to data sharing between companies and countries. Electronic lab notebooks are software programs that enable scientists to record experimental observations, capture and analyze data in one place, share results electronically, and prevents data loss. Some next-generation electronic lab notebooks (ELNs) allow selective data sharing. For example, some notebooks let users control which data are included when sharing a notebook with other parties, letting companies filter access and incorporating an added layer of security. Using advanced programming interface (API) technology, applications are directly embedded in ELNs. Researchers can create and share custom templates for common experiments, which streamlines the layout and format of similar experiments and standardizes how data are captured, thereby increasing reproducibility and consistency. For instance, scientists developing a new catalyst to produce a polymeric plastic can record their data in an ELN and associate searchable keywords such as “catalyst research” or “polypropylene” with the data. They can then tag other colleagues who would be alerted and can view the data or start a chat within the ELN interface to respond to a question, getting feedback in real time (Kwok 2019). Cloud-based ELNs offer three key features: • simplicity with no on-site installation or data storage requirements for cloud based ELNs, reducing demands on information technology departments, • communication and collaboration in real time with feedback, and • lower cost. Efficient data analysis workflows The first step in analyzing data is getting access to it. With cloud-based tools, researchers can access company-wide data stores quickly and search for the data they want using key search terms efficiently. After accessing the experimental results needed for product analysis and decision-making, the right files can be added into a system that uses mathematical modeling and statistics. For example, to design a formulation for a new polymer-based surface coating, researchers can identify key specifications required, such as being sufficiently hard, resistant to scratching or deformation, and sufficiently dense. These specifications can help determine drying times and coverage area. Researchers can upload their experimental results and key metrics for a selected formulation. Statistical analysis, data mining, ML, and AI can be leveraged in advanced software to help move to the manufacturing stage (Kwok 2019). Artificial intelligence and machine learning are also lowering the time and cost for the iterative design-and-test process for products by accurately predicting properties of hypothetical molecules. AI and ML use algorithms to predict chemical properties such as solubility and melting points based on existing experimental data. Each algorithm is trained using existing molecules and their known properties, thereby improving the predictive power as more data are collected. Digital tools incorporating AI and ML can accelerate research and development by predicting product specifications of possible candidates from the start (Jha et al. 2019). Maintenance and AI Random asset failures cannot be avoided with traditional maintenance alone. IIoT enables increased access to data with various predictive and prescriptive maintenance strategies (LePree 2018, Brooks 2020). Various types of maintenance exist: • In reactive maintenance, non-critical assets run until failure. • In preventive maintenance, asset failures are prevented using an enterprise asset management or computerized maintenance management system.

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• In condition-based maintenance, the focus is on the physical condition of equipment. • In predictive maintenance, asset performance is continuously monitored with sensor data and pattern recognizing or artificial intelligence to provide advance warning of failures. • In risk-based maintenance, a prognostic strategy is used. This strategy is based on balancing the risk of asset failure with the cost of maintaining assets. Prescriptive analytics A prescriptive analytics strategy is needed for complex and critical assets by enabling an operator to detect and attend to variations in equipment behavior before they fail. This reduces unscheduled downtime and shutdowns considerably. Prescriptive analytics are also used for ‘what if’ scenarios. To do that data must be captured and translated into knowledge, with the following steps:

• • • •

connecting people, processes and assets using digital technology, collecting and managing the data, analyzing information with ML and pattern recognition, and acting to prevent/reduce downtime and optimize asset management and maintenance.

These steps help create digital twins of operations and asset life cycles (Custeau 2019).

8.3.2  Hybrid Artificial Intelligence Systems Artificial intelligence is a transformational, complex, multi-faceted, and rapidly evolving. The industrial sector requires diverse functions across an organization to operate interdependently. Before an organization adopts AI with data, it needs to be guided by key skills and business goals. For example, Aspen Hybrid Models are a new generation of innovative process modeling, which may change the course of the chemical and hydrocarbon industry (Azizi 2019). Hybrid model takes advantage of the rapid development of analytics, ML and AI. Hybrid models combine AI and first principles modeling to deliver a comprehensive, accurate model more quickly, without requiring significant levels of expertise (Figure 8.7). Machine learning creates a model 18 leveraging simulation or plant data, while using domain knowledge including first principles and engineering constraints, to build an enriched model without requiring modeling expertise. AI within hybrid models can optimize design, operate, and maintain assets that cannot easily be modeled from

Hybrid Model First principles from chemistry & physics Challenges May not cover all phenomena Expertise required Time consuming to create and run

Leverage domain knowledge Combine first principles with AI & ML Interpolate & extrapolate more easiliy Easy analysis & interpretation Needs less training & data Run faster

Pure machine learning model Challenges Needs reliable data May violate physical constraints Difficult to interpret

Vision

Create & deploy workflow for self-sustaining fit-for-purpose models to be used with confidence across the asset lifecycle Figure 8.7.  Hybrid model systems (Pendyala 2020e).

282  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence first principles alone. Hybrid models help organizations create and sustain better models faster, by keeping the model more relevant over a longer period (Sanjurjo-González et al. 2021). AI must be combined with domain expertise to create real-world applications that permit it to work safely, reliably and intuitively. Hybrid models combine AI, first principles and domain expertise to deliver a comprehensive, accurate model more quickly without significant expertise. Machine learning is used to create the model, simulation, and pilot plant data, while using domain knowledge including first principles and engineering constraints to build an enriched model. Hybrid models Hybrid models can reduce waste and boost productivity in challenging processes and equipment including solvent extraction, membranes, and extruders to achieve sustainability targets. The diversity and complexity of chemical processes have limited the application of advanced process control (APC). However, AI assist lowers that barrier and APC can be employed on batch processes to enable closed-loop control to boost quality and productivity, with less manual intervention. Stakeholders, consumers, and employees can benefit from digitalization to address the challenges of sustainability (Pendyala 2020a,e, Schuller et al. 2022). Hybrid models can be beneficially adopted by capital intensive manufacturing sectors. This includes the energy and petrochemical sector: • Refining: Embedding accurate, fast running, nonlinear hybrid models of key economic units. • Equipment monitoring: Unit and equipment level models use AI analytics combined with first principles for easy to develop, updated and run digital twin models. For example, simple fouling monitoring use can reduce the downtime for a single heat exchanger train. Monitoring reactor fouling and catalyst activity can also have a positive impact in industry. • Polymer industry: Hybrid models for polymer production can predict operating performance and lead to considerable savings, due to increased efficiencies and reactor uptime. Other applications include separation membranes, oils-to-chemicals asset models, and a wide range of others. Using the hybrid models, users can model complex processes. Examples include:

• • • •

batch processes, which can be too varied to systematically model fluidized bed processes with complex chemical and fluid behavior bio-process reactors and fermenters complex refining units

Users get the accuracy of empirical models and the strength of first principles models, leveraging the power of AI paired with domain expertise, to create a more predictive model faster and with less experience required than ever before. Hybrid models provide a better representation of the plant over a longer time. With models in place, the connected worker can perform higher value-added and strategic work. Hybrid models, embedded with AI, address the training and enabling workforce, creating immediate value for organizations and assets. Enterprises need the ability to build and deploy these models (Sayyadi 2019). Process industry companies focus on flexibility, strategies for resilience in production, and extended maintenance. Powerful and accurate models are an important step in understanding how a specific process will respond to a change. As plants and their systems have increased in complexity, these models have become essential to operations. Hybrid models combine AI and first principles to deliver a comprehensive, accurate model more quickly, without requiring significant expertise. Machine learning is used to create the model leveraging plant data, while using domain knowledge

Artificial Intelligence  283 Table 8.1.  Modeling approaches with artificial intelligence. Model

Description

Examples

AI-driven hybrid model

An empirical model that uses ML to build the model based on plant/experimental data, first principles, constraints, and domain knowledge to produce accurate model.

• Model complex process units and processes

Reduced order hybrid model

An empirical model using ML to build a model based on data from sustainable runs, constraints and domain knowledge to create a fit-for-purpose model

• Chemical process industry

First principledriven hybrid model

First principles model enhanced with data and AI to improve the model’s accuracy and predictability.

• Batch unit modeling

with process intensification • Inferential sensors • AI enabled equipment unit models • Model update • Deploying online modeling • Deploying process train model • Advance process control model deployments • Bioprocess modeling • Complex process modeling

including first principles and engineering constraints to build an enriched model without an AI expert (Roldan et al. 2019, Pendyala 2020a,e). Three fundamental capabilities for this are as follows: • strong and deep domain expertise in process industries • strong capabilities to capture and analyze the data available through connected sensors • innovative leadership in turning machine learning and AI into industrial tools. Several key technologies are provided to industry via the following three types of hybrid models (Table 8.1): • Type 1: AI-driven hybrid models use ML to create an empirical model based on plant data. These models are augmented with first principles with thermodynamic properties, mass and energy balances and domain knowledge. Examples include complex reactions, new material processes, and new technology. • Type 2: Reduced order hybrid models use ML to create an empirical model based on data simulation runs, with constraints and domain expertise. Users can extend the scale of modeling from units to the entire plant and synchronize the model across design, operations, and maintenance. Examples include building value chain-wide models for reliable outputs. • Type 3: First principles-driven hybrid models use first principles models with AI, using data from operations to predict unknown variables. Machine learning determines the unknowns and continuously calibrates the model as conditions change. Examples include embedding AI-created and first principles-governed models for unique batch processes. Several key technology components involved in hybrid models with AI are: • Create hybrid models to turn plant data into AI-based ML models • Combine first principles physio-chemical knowledge with these AI-based empirical models • Deploy intuitive, automated workflows as operational applications. Some of the unique benefits of hybrid models are: 1. Expanding the modeling’s scope and impact for complex units to control yield, performance, and quality, such as specialty chemical reactor models. 2. Empowering a typical workforce to develop models for equipment and assets without expert modeling skills, using data to develop reliable models.

284  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence 3. Representing complex behaviors accurately for planning, dynamic optimization, and online equipment monitoring, and for achieving closed-loop production optimization, via fit-for-purpose models. 4. Sustaining modeling that is closely tied to plant data and able to assess the asset operations closely as the operations evolve, thereby sustaining the benefits. 5. Accelerating collaboration between disciplines by enabling model alliance across disciplines. For example, planning models updated by hybrid models improve information sharing and collaboration. Some cases that illustrate how hybrid modeling may help follow: • expand the scope of business problems that digital twins can solve • improve profitability and quality • make technology easily to apply for the new workers For instance, refining and olefin margins are closely related to the ability of plant planners and operators to achieve monthly production targets. Up-to-date revisions of detailed reactor models use hybrid modeling workflow and achieve considerable economic gains. Sustainability indicators via industrial internet of things Products are enhanced to be able to communicate and generate data, which can be used to monitor and optimize sustainability indicators via industrial internet of things (IIoT). However, the impact of products on sustainability across their lives is often not considered and analyzed. The Digital Twin (DT) focuses on product individual data collection and analysis. It can optimize the system’s sustainability individually, presently as well as for future product generations. However, the design and realization of such a DT requires new approaches and capabilities, which is an identified research gap. A methodology called DT V-Model consists of DTs of physical IoT-based products, with the aim to optimize system sustainability, specifically environmental aspects. It is based on the V-model for the development of smart products and is enhanced with additional roles and approaches for DT development (Alhaddi 2015, Riedelsheimer et al. 2021). The results of the DT-V-model include digital master (DM) data from the planning phase and digital shadow (DS) data from the production, operation, and end of life-phase. For a DT, the information and models from the product development phase include the planned production and use phases of energy consumption for energy efficiency. The DS consists of the actual production energy consumption and the usage phase of energy. The methodology is applied to a use case of an IoT-based consumer product that can be customized to a certain degree by the consumer (Riedelsheimer et al. 2021).

8.3.3  Digital Twins Digital twin (DT) is a digital representation of an asset, like a compressor, a reactor, or a whole industrial plant. The digital version must include the processes and systems with data from sensors and physical observations, as well as maintenance manuals and other critical documents. DT communicates with sensors that collect data to feed the asset model and combines the modeling and the Internet of Things (IoT). For example, compressors are used for pressures up to 50,000 pounds of pressure per square inch, which puts a lot of strain on the machinery and cause failures many times a year. A compressor generates data on vibration, temperature, and pressure. DT can predict a machine’s failure and apply preventive maintenance to help avoid costly unplanned downtime. For supply chain planning, the supply chain model can use inputs from the digital twin maintenance model to improve scheduling (Banker 2018, Menachery 2020, Beck 2020b,c). Engineers check process status, perform analyses, and generate solutions that may reduce risk and optimize the performance of processes. With the asset’s lifetime, operational data, and IIoT

Artificial Intelligence  285 Plant Digital Twin

Design Asset model Equipment Process model Economic model

Operation Planning/scheduling Control & optimize Operation model Marketing model Demand model Operational excellence Digital Twin

Asset lifecycle Maintain Equipment analytics Process analytics Risk model

Operational integrity Digital Twin

Figure 8.8.  Digital twins as digital profiles of options around asset life cycle.

feeds, DT allows operators to predict asset behavior based on safety, reliability, and profitability under various conditions. Therefore, DT requires complete and continuous data from assets. As the operation continues, a digital clone of the asset can be updated in real time (Riedelsheimer et al. 2021). Digital twin helps predict and optimize performance of the asset for business, processes, environment, safety. As Figure 8.8 shows, digital twins are a digital 21 profile of the past, current and future behavior of a process. Therefore, DT helps optimize business performance based on models and real-time data, creating an evolution in process design and operation, as well as safety and maintenance (Beck 2020b). The DT implemented on high value units, such as distillation columns, reactors, and heat exchangers, provides optimization without putting onsite workers at increased risk. These high-value areas may include margin capture, safe operations, and agility in the face of uncertainty and sustainability (Beck 2020b, Brooks 2020). AI and sustainability often are both mutually catalytic and synergistic, as they share the same underlying business drivers of creating a safer, greener, longer, and faster operation (Pendyala and Morse 2020). Some examples follow: • a refinery deploying a machine learning-based digital twin to predict future equipment failures, prevent outages and plan maintenance, and optimize uptime and yield, • a company in a gas field that deployed a digital twin encompassing process, energy, utility, and carbon balance information to help gain asset-wide visibility for sustainability metrics (use of energy, water and hydrocarbons), • a company with a digital twin that pairs an engineering online model with an advanced process control model, to improve sulfur recovery performance, reducing SOX emissions by 95%, • an oil company that created a multi-asset planning digital twin to evaluate multi-site refining and distribution strategies for improved operating efficiency. These companies are using current technology to tie models to connected data from assets, to predict, decide, and improve performance. • a heat exchanger train model that provides fouling and cleaning schedule advice, or even make closed-loop decisions. Hybrid models can be easily developed, updated, and run just for fouling

286  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence monitoring to save tens of millions of dollars per year. AI-based technology learns from existing design and operations data and then delivers prescriptive maintenance plans. • a compressor train in a low-density polyethylene unit that, with several days advance failure warning regarding the compressor unit, can be taken down before failure to avoid a potential safety hazard and environmental release, while reducing the downtime. • carbon capture initiatives supported by Industrial AI, including design, validation and commercial scale-up of capture technology, with increased model accuracy across the entire asset life cycle. • hybrid models that take advantage of AI combined with first principles asset models that can be run online to monitor and optimize major manufacturing sectors. Deploying DT with the hybrid models increases efficiency for major energy consuming units in these sectors. To start with DT implementation, some guidelines are necessary to ensure success: (i) leadership support for digital transformation, (ii) clear definition of scope, (iii) realistic timeline to adopt the technology with proven value, and (iv) investment in workforce with proper training. Analytics capabilities keep evolving from predictive to prescriptive, meaning from what will happen to what should be done (Seele and Lock 2017). Integration of AI options supported by the cloud platform can occur with materials distribution and service providers. This involves optimizing transport routes, saving considerable amounts of materials annually, and increasing the availability at the appropriate locations. Services become able to synchronize the processes along the supply chain to become more flexible for specific requirements of customers, e.g., delivery speed, pricing, and material quality. This is possible by combining all company data together on a single platform through self-learning algorithms based on machine learning that can analyze all relevant information and make recommendations to support employees (Arnold et al. 2012). State of the art of digital twin development Digital twins are multifaceted and need to be tailored to the use case under consideration. DTs integrate several domains: software, mechanical, electronic, electrical, data science and engineering, information, and communication as well as service. All the features of a DT system need to be determined and aligned with the life cycle of a product. Scenarios for digital twin (DT) development are listed in Table 8.2. Therefore, considering additional characteristics, a suitable methodology needs to meet the following requirements (Beck 2019, Riedelsheimer et al. 2021): • The collaboration between the DT domain specific roles should be supported and described on an organizational level. • DT should be applicable to highly complex systems from all DT domains. • A certain amount of detailed description of the necessary development steps and results is necessary to enable documentation and traceability of the process. • Changes during the execution need to be possible. • DT should be standardized and accepted in industrial application and research. Table 8.2.  Scenarios for digital twin (DT) development. Scenario

Description

Simultaneous

Development of a product and services simultaneously

Successive

Further product development toward a new version in parallel with DT

Extended

DT for an existing smart product

Legacy

DT for an existing smart product for a new product generation

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• Sustainability aspects should be considered. • Guidance and expertise are needed to develop several variants of products and their DTs. • DT should be easy to understand and apply. Types of digital twin models Each manufacturing sector must define a scope for developing digital twins based on its business processes, their interactions and the enterprise’s value drivers. Businesses must also establish creation and maintenance plans for each digital twin. An organization needs to prioritize where to first make digital copies of processes based on their value with possible multiple digital twin implementations. They will eventually connect and combine, to become more intelligent (Beck 2020b,c). Plant digital twin process models Rigorous process simulation models provide an accurate representation of chemical processes. Rigorous first principles models are always more accurate when the chemistry and physics are known, and the model is calibrated against plant operation. Machine learning and deep AI have key roles to play for complex processes as operations change unit behavior. Dynamic what-if models are important operational tools to ensure both process safety and effective operator training to minimize human error. Additionally, advanced data analytics form the basis of empirical process unit models, using multivariate analysis that can simulate and optimize chemical process quality (Beck 2020b,c). Operational excellence and integrity of digital twin Plant operations are modeled and virtually viewed as planning, scheduling, control and utility models. Such digital twins inform business decisions such as feedstock selections and products trading, as well as technical decision making, like optimizing quality, throughput, energy use, emissions compliance, and safety. DT can provide guidance on both tactical and strategic decisions around prescriptive maintenance, offering real-time recommendations to adjust or maximize production, minimize environmental impacts, mitigate production losses, and prioritize safety. In addition, quality and risk assessments provide a future view of equipment and asset health, risk profiles and root causes of failures to improve uptime and operational integrity. Digital twins and business model Digital twins put forward a five-dimensional sustainable business model. The dimensions are: value creation, value network, financial model, customer interface, and value proposition. Digitalization can reconfigure every aspect of enterprises’ activities as multi-variety coordination. Enterprises need to adopt a systematic approach by drawing a digital roadmap to address a business model across their value chain and to transform from enterprise-centered to user-centered. This can greatly improve the efficiency and effectiveness of the experiences of enterprises and users. Enterprises can use a DT network to provide a comprehensive view of products, manufacturing, supply chain, customer experience, service quality, performance, and profitability (Figure 8.9). Enterprises use digital twin platform networks may generate economic, social, and environmental benefits in various dimensions and their coupling relationships, as shown earlier in Figure 8.8. Numerous generic strategies and digital transformation suggestions exist for enterprises to innovate the sustainable business model (Li et al. 2020). Digital twin models in refining Digital twin models of refining units can be part of the planning of operating to improve collaboration among remote workers. In addition, digital twin models are beneficial in optimizing asset performance. Examples include troubleshooting operations via online distillation column

288  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence

Value creation

Customer interaction

Economics Society Environment

Value creation

Value network

Financial model

Value proposition

(a)

Customer interaction

Digital Virtual Entity Digital Twin Platform Network

Value network

Financial model

Value proposition

(b)

Figure 8.9.  (a) Dimension for sustainable business model, (b) dimension mapping in digital twin platform network.

models and monitoring heat exchanger performance and fouling with online models. Remote staff can run digital twin models and advise onsite operators of the correct process changes to run units safely and reliably. Adaptive process control can also be managed •and monitored remotely provided • Forecast and plan that the security and data protection are managed (Menachery 2020). Plant and Streamlining with a modular approach Plant digital twin schedule and design and Manufacturing sectors need continually to invest in new control and processes and new plants, innovate, and debottleneck optimize designs may be helpful. A modular approach search for new products. To achieve these goals, modular helps reduce design, schedule, and scaling problems, and cost uncertainty, with a faster start-up and Operation with easy construction management. This includes theAssess ability to modify the process at different locations, production prescriptive applications, and scales, with maintenance reduced cost risk.reliability A plant, treated as being composed of modules, is desirable for the upstream and midstream energy sector, which includes the oil and gas industry • Automated natural gas liquefaction, and acid gas removal. • Distribute with value including dehydration, Integrated engineering tools execution chain optimization help a workforce develop datasheets, process modeling, and concurrent and accurate CAPEX estimates in modular design. Figure 8.12. Asset optimization, with the artificial with for digital twin inmodular plant, for operational Captured design knowledge improves abilityintelligence of a workforce delivering designs. excellence and operational integrity. Modular design supports business strategies for lower capital project investments, while still driving towards growth, expedite project execution by integrated global design teams for faster on-time delivery (Beck 2016).

Cost‑competitive manufacturing 2nd: 4th: 1st: 3rd: Mass Data Product Mechanization quality and capacity utilization influence cost through the downtime, energy efficiency, manufacturing processingComputationand composition in powerexpenses. For example, feedstock availability and maintenance may cause lines and physical and automation fluctuatinggeneration energy consumption (Buer et al. 2018). Five essential principles of manufacturing cost utilities systems competitiveness are as follows: • Safety is relevant the entire manufacturing sector with the processing materials can be Figure 8.13. Thetofour “industrial revolutions” preceding and leading toofIndustry 4.0that (Lydon 2020). explosive, flammable, toxic and/or generally hazardous. Unsafe operations can lead to the loss of life and the license to operate, and can cause environmental impacts, leading to regulatory fines and even criminal penalties. Some industries, for example, have implemented various best practices incorporated in comprehensive process safety management (PSM) programs with overpressure protection systems. Safe Operating Window Management defines and monitors best practice with the full range of safe operating parameters for an entire plant. • Plan to fail may lead to successful execution in the complex interaction between feedstock, plant design and performance. Determining the desired feedstock type, quality, and operating conditions requires planning to achieve low operating cost. It supports rapid re-planning when

Artificial Intelligence  289

operations exhibit dynamics, such as swings in pricing and feedstock availability. For example, integrated refinery/petrochemical optimization requires feedstock optimization for co-produced olefins and aromatics plants. • Large, complex manufacturing facilities have inherent operational dynamics that may create significant challenges to effective cost control. Measurement is the foundation of cost control. Key performance indicators (KPIs) are based on real-time operational data to measure important cost structure drivers on a continuous basis (Figure 8.10). For example, a producer may establish several KPIs for energy consumption across a facility and monitor them to rapidly detect overconsumption of energy and adjust. Real-time quality monitoring is a related best practice displayed on a performance dashboarding with an indicator of the KPI status versus target. • Sustainable improvement results from understanding of processes and their interactions. Actual operations may evolve after an initial start-up because of feedstock changes, product specification changes or increased production levels. Therefore, facilities require a deep understanding of current operations, process capability and plant constraints that can only be achieved through engineering-based modeling of the process systems. Developing and experimenting these basic models serves to deepen process understanding of the various causes and effects, with effective troubleshooting and best practices. This may prevent operational issues and a cost performance gap. Process improvement identification may lead to a best practice in which process simulation models are applied to enhance cost structure by increasing energy efficiency, improving yield, and expanding feedstock flexibility. • It is beneficial to be expecting the unexpected and taking appropriate preventative action to minimize any consequent negative impacts. For example, maximizing the reliability of a component and interconnected systems is essential to minimizing any unexpected costs. Sometimes focusing on solving pressing short-term problems diverts the focus on longer-term preventative measures, ultimately leading to larger reliability problems in the future. A quantitative understanding of the potential causes of unreliability across a plant can prevent potential failures in advance. One of the widely applied practices is equipment performance monitoring, which allows planning maintenance activities and operational adjustments. Reliability and availability modeling is a best practice for evaluating the future performance of an entire plant site. Furthermore, predictive and prescriptive analytics can predict impending equipment failures with the application of advanced pattern recognition 26 with statistical and machine learning techniques.

Feedstock cost Conversion efficiency Yields

Reliability

Cost Structure Energy efficiency

Capacity utilization Product quality

Figure 8.10. Commodities andstructure cost structure interactions. Figure 8.10.  Commodities and cost interactions.

290  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence These five essential principles can be viewed as a roadmap to improve cost structure in the industrial and manufacturing sectors by the adoption of proven industry best practices. Most of the best practices can be implemented with relatively little investment and can be deployed modularly. Digitalization and knowledge creation Knowledge creation facilitated by direct or indirect application of digitalization supports sustainability in manufacturing industries. The increased digitalization and the adoption of Industry 4.0 with strong management leadership lead to organizational knowledge capabilities supporting direct and indirect improvements. Both inside the organizational context (intrinsic knowledge creation) and multi-organizational contexts (extrinsic knowledge creation) are relevant aspects in the link between knowledge creation and sustainability performance (Ordieres-Meré et al. 2020). The sustainability gap continues regarding consumption of natural resources and its harmful consequences. With digital technology this gap may be controlled more effectively. Digital technology offers new possibilities regarding how to shape research in sustainability, because the technological and societal complexity require a new approach supported by AI. Hence, digitalization opens entirely new ways to shape, monitor, communicate, and govern sustainability (Seele and Lock 2017). Some avenues for future research are opening in sustainability science with relation to digitalization: 1. The governance of sustainability in a digitalized environment may be a new concern for policy makers. This can help increase sustainability, promote fairness, and increase resilience. However, these approaches are open to further research and need commitments by industry and stakeholders. 2. Sustainable development in the digital age needs answers to the question: How can digital technology help achieve the UN Sustainable Development Goals? This major question must be addressed by researchers and policy makers. 3. Digital technology can promote sustainability with enhanced surveillance technologies due to precision and technological capabilities to obtain data relevant for promoting sustainability. Due to the possibility of data misuse, further research is needed for protecting the public and industry and for effective use of digital data to promote sustainability. The industrial sector, including chemical firms, gain a great deal through the ability to predict failures in equipment. Other asset-intensive industries like power, metals and mining, and transportation can also potentially obtain significant value from optimizing maintenance across the supply chain. That is because critical, costly equipment is key to operational excellence. For heavy process industries with complex turnarounds, these types of options can reduce costs and downtime (Hahn 2020). Enterprise reliability Plant reliability can be achieved through better design and by improved operational feedback, which is facilitated through digitalization and integration. Reliability is one of the three components of asset life cycle optimization. Enterprise reliability modeling helps making decisions on CAPEX for plant improvements and repairs. Data from operations, over time, when properly analyzed, can identify equipment, process units and designs which operate with the best reliability and performance. Employing modeling to understand where conditions for low reliability exist, and to identify and examine alternative process operating cases, can help achieve higher reliability. This applies particularly to areas such as corrosion, and understanding the causative process factors thereof, rather than simply monitoring metal degradation. Probabilistic enterprise reliability modeling identifies the “pressure points” where investments might achieve the highest reliability and profitability returns (Beck 2020b, c).

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Asset life cycle Asset models represent the functional dimension of an asset, the connectivity in terms of process flows, and associated infrastructure. Asset data includes operating procedures such as data sheets and recommendations. Logical connectivity refers to process flow diagrams and piping and instrumentation diagrams (Beck 2020d). Asset life cycle maintains maximum uptime with actionable insights by operating to the limits of performance design, and pushing the boundaries of best practices in industry and manufacturing (see Figure 8.11):

Best possible design

Maintain for maximum uptime

Asset Life Cycle

Operate with best performance

Figure 8.11.  Asset life cycle dimensions (Beck 2020c).

Companies believe that, through a focus on technologies such as machine learning, they will be able to predict equipment failure and to prescribe preventative and maintenance actions. The area of focused digitalization can save 16% or more of operating expenses. Asset optimization and digital twins The digital twin (DT) needs to encompass the entire asset life cycle and value chain from design and operations through maintenance and strategic business planning. The plant DT uses engineering models and enhanced by AI techniques with embedded cost and risk models. They are deployed offline and online, and calibrated to plant operating conditions through autonomous model tuning. The scope ranges from a single piece of equipment or a single process unit, to plant-wide or enterprise wide (Figure 8.12). Some important considerations in asset management follow: • Project: Time and cost can be considered the digital twin’s fourth and fifth dimensions. DT models that effectively represent and simulate the design, resources, timing, and costs of project execution are important for achieving minimum capital expenses and maximum lifetime value. Visualization of workflows provides a useful insight to applying machine learning to complex project digital twins. • Risk, cost, and economics: Risk and cost models together examine the asset and enterprise as a connected, constrained system to identify process, safety, and economic risks, and to predict costs and identify the optimal way to invest available capital. • Safety: Safety models help minimize process safety incidents across an asset by modeling all aspects of a system. The models consider both process and operation, enable analyses of worst-case scenarios, and develop emergency response strategies that are accessible and updatable in an agile manner (Brooks 2020).

292  Chp.8 Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence • Optimization

• Forecast and plan Plant digital twin design and debottleneck

Plant schedule control and optimize

Operation with prescriptive maintenance

Assess production reliability

• Automated execution

• Distribute with value chain optimization

Figure 8.12. Asset optimization, with artificial intelligence with digital twin in plant, for operational excellence and operational integrity.

Stable operations and reliability Stable operations lead to greater reliability and better utilization of equipment, often maximizing return on capital invested by optimizing performance over the full life cycle and across the entire system. Operational excellenceAPPLICATIONS can focus on how to run operations or how they optimize their supply chain. Asset optimization has always been about digital technologies that are being accelerated Intelligent Intelligent agent automation analytics, enabled by high-performance through new advances like AI, ML, and multivariate computing, the cloud, IoT, connectivity, andAI robust cybersecurity. INDUSTRIAL Knowledge representation With the cloud and high-performance computing, it is feasible to start by launching a ML program that focuses on a specific business problem, such as plant equipment failure. For example, MACHINE Robotics failures of theData hyper compressor used in the low-density polyethylene process usually result in high LEARNING Audio ML was deployed, via prescriptive analytics software, and was able to repair costs.analytics In one situation, Expert Signal Deep learning systems provide advance warning of the failures and eliminate unplanned downtime. processing Reinforcement Industries such as pulp and paper, mining, and transportation have adopted digital transformation Supervised after installing sensors in assets. These industries link the sensors to their enterprise systems for the collection of real-time data to conduct the desired predictive analytics. Data management capabilities need to be developed for leveraging datasystems for system optimization, for preventive maintenance, or Predictive for safer operation. Companies can cooperate with technology providers toward data processing. The workforce in the plant can implement the technology themselves or a technology partner with domain specific expertise can Recommendations implement the digital transformation for a specific outcome such Intelligent productsgain. Combined with machine learning and analytics as a highest production rate with economic algorithms, digital transformation can prevent process and equipment failures in real time. This leads to high reliability in operations and hence value creation with improved safety and environmental protection (Golan et al. 2019) As pointed out earlier, a digital2020a). twin is a digital profile of the past, current and future behavior artificial intelligence (Pendyala of a process that helps optimize business performance. DT requires models and real-time data across multiple dimensions, including: • business performance,

• • • • •

asset planning, the physical asset, equipment condition and reliability, process performance, safety and risk,

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• energy and sustainability, • project time spans. DT creates an evolving performance profile and, if necessary, proposes changes in process design, operation, safety, and maintenance. The DT may be updated in real time and integrated with AI agents. Operational digital twin models In various fields there are specialized models of DT. They include the following: • Planning and Scheduling: Planning and scheduling models are powerful drivers of value in refining, bulk chemicals, specialty chemicals, metals and mining and related businesses. Planning DT can evaluate many scenarios rapidly and optimize across the supply chain network of assets. They further provide the automated workflow to tie together the process and planning models, optimize plans across multiple objectives and increase planning accuracy, operational fidelity, and consequently margin capture. • Demand: Demand models are used in collaborative demand planning and management business processes and are key inputs to DT planning. Such models help manufacturers better anticipate customers and their future demand. Advanced demand models can identify patterns that can be forecast based on data and higher-level human input. • Distribution: Distribution models schedule movement of products from a source location to an intermediate plant or warehouse for further processing and/or storage. Distribution models can optimize where to ship inventory based on demand prioritization criteria such as confirmed/ unconfirmed customer orders, company internal requirements, forecasts, and safety stocks. • Energy Demand and Supply: Energy models optimize supply and demand for energy intensive processes. These models can identify opportunities to reduce carbon footprint and assess the impact of operating decision on an asset’s energy intensity. • Control/Optimization: Control and dynamic asset optimization models employ state-of the-art self-learning and self-healing advanced process control (APC) technology and dynamic optimization. These models provide closed loop optimization and operating advice to run assets closer to their economic and technical limits autonomously. Integrated DT combine process models for what-if and APC models to achieve maximum uptime. • Asset condition: Data from a process with associated machine-learning prescriptive maintenance tools provide asset conditions via DT, forecasting processes, equipment, and environmental changes and preventative measures available, in case they are necessary. AI helps process the streams of data to make these models feasible. Some models can integrate with planning and scheduling for operating adjustments to minimize impacts of equipment repair and/or failure. • Sustainability: Water and utility models of the asset implemented online exhibit short- and long-term sustainability metrics of the enterprise and individual assets. These models assess water use, utility choices, energy use and cost, GHG emissions, flaring, and SOX and NOX capture. Combined with process models they report on sustainability performance. A company can build DT to match its market, operational strategy, and return-on-capital requirements. Companies usually do not construct all these models. The best DT emphasizes ease of creation and maintenance of the models. The most fruitful implementation strategy normally determines the scope and domain of DT solutions that addresses significant business needs. The focus may be a quality challenge, a reliability and uptime challenge, or a water or energy use sustainability imperative. Organizations should consider the scale of DT that will provide the best value.

294  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence Scale of digital twins The decision process for an organization is not about DT functionality, but about considerations such as infrastructure and deployment costs and resources, business model agility, and cybersecurity. Starting from detailed and small to large and enterprise-wide, there are several levels to evaluate: • Equipment level: These reveal equipment’s current, future, and historical performances. Examples where the equipment level becomes a priority include heavy-duty-compressors and heat exchangers, which may have impacts on yield and uptime. • Unit level: A process asset’s economic value is typically created at the unit level. Unit level models such as cracking, olefins reactors and chemical distillation are often extremely high value return areas for DT involving process planning, asset condition, control and optimization. • Plant level: Plant level DT provides a digital representation of a plant, several plants, or an entire site; they may cover a subset of the systems involved. Energy, refinery and bulk chemical planning and specialty chemical scheduling are optimized to improve economics at this level. • Enterprise level: Enterprise level DT enables rapid analyses of enterprise profit opportunities and effectively present actionable information to the executive level. Examples include enterprise risk models, combined scheduling, and supply chain models, as well as multi-asset planning models to optimize utilization of a network of plants, transportation and storage facilities for maximum profit and customer satisfaction. Some real-world examples that benefit considerably from DT models include the following: • Upstream yield increases in gas wells, gas gathering, and gas production and relevant transportation networks. • Uptime improvement in crude oil refinery and petrochemical uptime and margins with DT and machine learning. • Energy and water sustainability improvement in an oil company using an asset-wide DT for asset metrics around water use, energy use and hydrocarbon loss, to support better decisions and to attain sustainability milestones. • Order fulfilment and working capital improvement in a specialty chemical producer after optimizing the supply chain daily by using a DT. • Increased quality in a polymer producer by a multivariate analysis-based DT approach. • Improved bioenergy conversion processes, which need innovative and novel new technologies and hybrid modeling, and which combine AI analytics with rigorous process modeling. Technology will play a key role in helping industry navigate a drive towards carbon neutrality, improved energy efficiency, production efficiency, and enhanced energy return on investment. • Using DT monitoring systems and dynamic optimization solutions to save, in total, 5–15 percent of energy use, and reduce carbon emissions by a proportional amount. • Utility supply optimization through modeling multiple utility sources by considering cost and reliability, for example, wind energy, natural gas-based electricity, and diesel combustion.

8.3.4  Industry 4.0 Figure 8.13 shows the four “industrial revolutions” leading to Industry 4.0 (Lydon 2020). Industry 4.0 focuses on higher productivity, efficiency, and self-managing processes where people, machines, and equipment communicate and cooperate with each other directly (Aroma et al. 2019). A major goal is low-cost mass production efficiencies to achieve make-to-order manufacturing. Production and logistics processes are integrated intelligently across company boundaries creating smart value-creation chains that include all the life-cycle phases of the product including the initial product idea, development, production, use, maintenance, and recycling. Hence, the ecosystem

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1st: Mechanization in power generation

Figure 8.13. The four “industrial revolutions” 2020). 2nd: 3rd: Mass manufacturing Computation Comprehensive lines and realand automation utilities

4th: Data processingphysical systems

Figure 8.13.  The four “industrial revolutions” preceding and leading to Industry 4.0 (Lydon 2020).

Automation Robots

Augmented reality

System integration

Cybersecurity

Industry 4.0 Big data

Manufacturing

Internet of things Cloud computing storage

Optimization simulation

Figure 8.14.  Elements of Industry 4.0 (Lydon 2020).

).

can use customer wishes for everything from product idea to recycling to make improvements Green lean six sigma and I4.0 (Figure 8.14). Industry 4.0 helps overall plant optimization and continuous improvement engagements and root cause analysis toward increasing quality and safety (Badri et al. 2018, Lydon 2020, Canas et al. 2021). 31 production to the Comprehensive real-time information enables companies to react during availability of raw materials for optimal efficiency. External linkages enable production processes to be controlled across company boundaries to save resources and energy. The role of automation professionals continues to grow in importance with the increasing sophistication and integration of automation (Cerrafi et al. 2017, Golan et al. 2019, Lydon 2020, Bhat et al. 2021, Sharma et al. 2021). Green lean six sigma and I4.0 Green lean six sigma (GLSS) is a multi-dimensional manufacturing strategy consistent with Industry 4.0, which contributes toward the circular economy by adopting the reduce, reuse, and recycle (3R) concept (Cherrafi et al. 2017, Chiarini and Kumar 2021). In the manufacturing context, the enabling factors of six sigma are its abilities for systematic and structural approaches. GLSS can respond to the required organizational learning and manufacturing improvements to reduce defects and increase economic standing and market share. GLSS also can support circular economy-based models by practicing the 3Rs and hence environmentally sustainable manufacturing (Sanders et al. 2016, Gholami et al. 2021, Letchumanan et al. 2022). I4.0 is aimed at adding fast response to market changes and customer demand, as well as sustainable value creation through the closed loop of the product life cycle with resource efficiency. With I4.0 and digitalization, more data are available for the industrial sector. Figure 8.15 shows the improvements that I4.0 offers for various value drivers in manufacturing sectors, where six sigma is an effective strategy in continuous improvement (Buer et al. 2018, Bhat et al. 2021).

296  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence Asset utilization

Market readiness •20%-50% reduction •3%-5% increase in productivity

Market forecasting •Up to 85% increase in supply/demand match

•30%-50% reduction in total process downtime •10%-40% reduction in maintenace cost

Value drivers in sustainable manufacturing

Cost for quality

Workforce Work force

•45%-55% increase of productivity

Cost of inventory holding •20%-50% decrease

•10%-50% reduction

Figure 8.15.  Industry 4.0 impact on value drivers in the manufacturing sector (Letchumanan et al. 2022).

Four emerging trends are relevant for lean six sigma (Antony et al. 2019, Titmarsh et al. 2020): • The analysis of large data through six sigma. • Implementation of environmental dimension in six sigma 32 analysis, to expand it since it was introduced mainly for productivity and cost reduction. • Deploying six sigma for small- to medium-sized enterprises. • Integration of six-sigma into I4.0. The tools to achieve objectives in sustainable manufacturing are interrelated with the level of available technologies.

8.3.5  Industrial Internet of Things Big data and the industrial internet of things (IIoT) started when the first plants upgraded from analog and paper-based systems to digital instrumentation and distributed control systems, generating vast amounts of data that supported the first wave of digital applications. The opportunity to manage this data emerged through high-performance computing and the cloud and data lakes. The ability to use this collection of data to extend the life of assets and maximize the return on capital was made possible by technologies that fall under “Industry 4.0”. Industrial Internet of Things technology is changing how power plants operate; for example, they incorporate new wireless vibration sensors with existing data to detect problems on a real-time basis. Such predictive maintenance includes infrared thermography and oil analysis beside vibration analysis. Continuous wireless asset monitoring of devices, such as pumps and motors, can provide more frequent data and allow earlier detection of failures, better understanding of the impact of process variability on asset condition, and reduced costs of wiring sensors into the plant’s distributed control system (Figure 8.15). For example, this allows simultaneous visibility of the flow rate of water through a specific pump, and how it is impacting the vibration changes on the pump efficiency and overall power output, leading to effective troubleshooting. When a pump starts operating off its design curve it may develop premature bearing and seal wear, and cavitation. Impact of IoT on artificial intelligence Automation, machine learning, and data science, as well as the convergence between input technology (IT) and output technology (OT) systems, can integrate new capabilities within existing plants. The

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IoT Hub with Cloud-Ready Industrial AI Infrastructure Data Integration and moblity with integrated data from sensors and cloud Cloud ready infrastructure with AI applications Enterprise-wide visualization Industrial AI applications with enpowered collaborations among scientists

Enterprise visualization and collaborative workflow Production and scale Connectivity and edge computing Enterprise data source

Model predictions

Data management and sharing

Data integration and purpose built apps

Supply and demand analysis

Industrial AI applications

Figure 8.16.  Key capabilities IoT.

digital continuum can help pave the way for optimal system designs, stable operations, and the 33 elimination of unplanned downtime. Key capabilities of the IoT are shown in Figure 8.16. As an example, advanced process control can be viewed as the “foundation” for successful digitalization. Digitalization improves process control using AI, ML, and more robust data science modeling. This targets safe, stable operations and meeting environmental regulations, leading to greater financial results and sustainability (Figure 8.16). Market and supply chain volatility requires that capital-intensive industries be more agile (Hahn 2020). Enterprises benefit from the rapid convergence of IT and OT to reduce risk by introducing AI-rich, real-time applications to complex industrial operations (Banker 2018, Beck 2020a, LePree 2020). Data integration and mobility For most of the industrial sector, accessible and useful data infrastructure supports Industrial AI models from training to operation. Through the IIoT, organizations can integrate data from sensors to the cloud and across the enterprise using modeling and process flow structures. This leads to broader collaboration between development, data science and infrastructure capabilities with scalable infrastructure for Industrial AI applications. With help from the IIoT, industrial sectors can translate real-time data into faster, smarter, profitable business decisions and identify risks and opportunities early. For example, fully integrated AI environments transform raw data to AI/ML algorithms and the IIoT hub helps scientists and partners to collaborate and build their own data-rich AI applications (Barrientos 2019). The IIoT helps engineers combine continuous monitoring with AI and ML through comprehensive analytics programs to show the status of each asset and if maintenance is needed. Machine learning is a science-based technology and, with methods for specific application domains, it can be used for “training” data for learning patterns and provide condition-based monitoring effectively. AI can incorporate the full asset life cycle and adds performance engineering and asset performance management tools for production optimization (Figure 8.17). Asset performance management with AI‑assisted development helps predict maintenance to support scalability and focuses on exploratory analytics for identifying, classifying, and analyzing operation, thereby improving sustainability through better reliability and efficiency (Custeau 2019).

better reliability efficiency Energy (Custeau 2019).Artificial Intelligence 298  Sustainable Engineering: Processand Intensification, Analysis, Requires comprehensive infrastructure for maintenance

Reliability -centered maintenance

Strategic Proactive Optimized

Diagnostic to predict Predictive maintenance failure Rules-based logic Condition-based maintenance Using sensor data Based on usage statistics Run to failure

Preventive maintenance Reactive maintenance

Figure 8.17.  Asset performance maintenance maturity pyramid (Brooks 2020).

8.3.6  Multi-Dimensional Optimization Tradeoffs decisionsof across multitude of business are and Tradeoffs and decisions acrossand a multitude businessa goals are needed betweengoals productivity sustainability goals, sustainability goals, contrary to traditionally optimized operations for productivity, quality, and profitability. with awareness of sustainability, well as shifts driven profitability. But with awarenessBut of sustainability, as well as societal andasindustrial climate change, future operations will need to be optimized across multiby climate change, future operations will need to be optimized across multi-dimensional business — including sustainability goals. Some use cases include the following objectives—including sustainability goals. Some use cases include the following (Pietri 2020a,b): • AI • AI-enabled modeling of process data to improve the energy consumption. • • Sustainability • Reducing emissions while also delivering on economic goals. • Deep-learning-optimized APC methods • Sustainability-related risk assessments of company locations. • Deep-learning-optimized APC methods to enable higher throughput with less resource consumption. Furthermore, Industrial AI can be used to optimize materiality and 34 stakeholder analyses, and to significantly improve the accuracy of calculating emissions. As a result of comprehensive plant digitization initiatives, the data necessary for these complex, multivariable decisions are increasingly available and need to be harnessed with intelligent algorithms through industrial AI (Sean et al. 2014, Ferdyn-Grygierek and Grygierek 2017). Plant‑wide modeling and optimization Some signs that optimization is needed in plants follow (Pietri 2020a,b):

• • • • •

Suffering customer service because of not having inventory when a sales opportunity arises Significant off-spec production leading to slow moving inventory being sold at a discount Inefficiency and lost productivity Lost capacity, where sub-optimal transitions eat up capacity Failure to use the full flexibility of a plant

Predictive analytics are important for safety, sustainability, and productivity for asset-intensive industries. For example, the use of predictive analytics generated through Industrial AI can reduce unplanned “flaring.” In addition, early prediction of process deviations helps avoid product quality problems and mitigate unplanned downtime via predictive and prescriptive analytics on critical equipment.

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8.3.7  Event Agent The event agent is a trajectory of user documentation, enabling the automation of knowledge. Some process measurements include feed rate drop, temperature and/or pressure changes, and quality changes. Deviations from expected behavior can result in excessive wear-and-tear on machines and lead to equipment failures and revenue losses. To avoid this, one needs technology allowing the use of process and mechanical sensor signals that identify changes and focus on the event signature. Such technology eliminates the need to manually model events. The event agent technology uses a three-step workflow to address the need and the tools to perform advanced diagnostics of disturbances: • First, the operator selects to create a new event agent and provides any known operation information of the event to estimate how often the event occurs, the average length of the event, and candidate tags around the process boundaries. Then, the analytics engine with each tag processes the data to the relevant sensor tags that show a strong correlation within the event period. • In the next step, operators define search parameters to execute a search for other occurrences of a selected incident. The technology also assigns a metric that measures the percentage by which every incident found fits the initial selected incident. • Finally, once all events are reviewed, end users can choose to accept their event matching results and create a new event agent. Operators can deploy the event agent online to monitor for event recurrences as the event agent extracts the configured tag data at a set frequency and evaluates the shapes in incoming tag data against the event signature stored in the event agent. If the patterns match the event signature, then user instructions on how to resolve the event condition are created. When the root cause of an event is successfully discovered and solved, the user can decommission the event agent. Event agent of a compressor As an example, an operator managing the compressor operation may receive a sudden change alarm and connects to the AI analytics installed in the cloud and collects and deploys the appropriate trends of data related to the event disturbance. The application then proceeds to identify the precise event fingerprint using ML pattern and shape analysis to indicate specific process changes. These changes include step changes, correlated shapes, amplitudes, and time variations. The collection of such patterns is returned to the operator as the key characteristics of the event fingerprint that may stimulate the operator to identify the underlying root cause of the observed change. When there is no clear root cause, other inquiries are made, such as: “Is it new? How did we solve it before?” After identifying the event fingerprint, the operator scan history to find similar past event signatures. If other signatures of the same event exist, event analytics returns them with the dates of the occurrences. Consequently, the operator searches management systems to find a solution. When the scan of history does not find similar events, the operator creates and deploys a new online event agent and start recording any developed knowledge and experience into the event agent. Performance engineering Performance engineering enables companies to solve complex problems and optimize using enterprise data, AI and the cloud, with these features: • Hybrid models that revolutionize the model creation and deployment workflow to build sustainable models with production optimization, • Multi‑case analysis to analyze designs across all operating cases for more optimal performance, • Better cost estimation by continuous organizational learning, and • In‑context guidance toward just-in-time knowledge and direction to accelerate engineering competency development.

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8.3.8  Self-Optimizing Plants Companies often search for autonomous processes that have greater resilience, flexibility, and agility to respond to market conditions. The vision of the ‘self-optimizing plant’ combines data enabled by AI with industry-specific first principles models and domain expertise. AI can reduce the gap between actual and targeted operations with enhanced optimization for industries after synchronizing actual and planned operations for improved performance, reduced environmental impact, and greater reliability and efficiency. A self-optimizing plant can offer several advantages (Pietri 2020a, b):

• • • •

Greater agility to thrive in a volatile environment, New ways to empower the next-generation workforce, The ability to meet safety and sustainability goals, and A pathway to greater profitability

Preparing for a more sustainable future for the manufacturing sector requires digital transformation focused on:

• • • • • • • •

Sustainability replacing or superseding growth and globalization as a key business driver Polymers for the circular economy Applying AI capabilities Navigating the energy transition Supply chain improvements Advanced process control Energy and utility optimization Predictive and prescriptive maintenance

The self-optimizing plant leads to safer, more capable, flexible, and profitable operations with required product specifications. A self-optimizing plant is a self-adapting, self-learning, and self-sustaining set of software technologies working together to anticipate future conditions and act accordingly. Self-learning utilizes data and information from across the field to obtain smarter and increasingly accuracy predictions and corrections in real-time to changing conditions. Engineering fundamentals combined with the AI capability of capturing and converting data to knowledge help optimize across multiple levels, provide recommendations, and automate operation in a closed feedback loop (Banker 2018).

8.4 Cybersecurity Cybersecurity protects critical systems and sensitive information from digital attacks. Sometimes known as information technology (IT) security, cybersecurity measures are designed to combat inside or outside threats against AI network systems and applications. Data breach costs include the expenses of discovering and responding to the breach, the cost of downtime and lost revenue. Cybercriminals target customers’ personally identifiable information (PII)—names, addresses, national identification numbers, credit card information, etc.—and then sell these records in underground digital marketplaces. Compromised PII often leads to a loss of customer trust, regulatory fines, and even legal action (Ouertani 2020, O'Reilly et al. 2022). Security system complexity, created by disparate technologies and a lack of in-house expertise, can amplify cybersecurity costs. A comprehensive cybersecurity strategy, governed by best practices with advanced analytics, AI and ML can fight cyber threats more effectively and reduce the life cycle and impact of breaches when they occur.

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Cybersecurity domains A strong cybersecurity strategy has layers of protection to defend against cybercrime, including cyber attacks that attempt to access, change, or destroy data to disrupt normal business operations. Countermeasures should include the following: • Infrastructure security protects computer systems, networks, and other assets that society relies upon for national security, economic health, and/or public safety. • Network security involves measures for protecting a computer network from intruders, including both wired and wireless connections. • Application security helps protect applications operating on-premises and in the cloud. Security should be built into applications with considerations for how data is handled and how the user is identified. • Cloud security aims specifically at true confidential computing that encrypts cloud data in storage, in exchange from and within the cloud and in use, to support customer privacy, business requirements and regulatory compliance standards. • Information security refers to data protection measures, such as the general data protection regulation (GDPR), that secure the most sensitive data from unauthorized access, exposure, or theft. • End-user education builds security awareness across the organization to strengthen endpoint security. For example, users can be trained to delete suspicious email attachments and avoid using unknown USB devices. • Disaster recovery/business continuity planning involves tools and procedures for responding to natural disasters, power outages, or cybersecurity incidents, with minimal disruption to key operations. The cybersecurity risk surface keeps expanding with new vulnerabilities in old and new applications and devices. Cybercriminals find new attack vectors all the time including Linux systems, operational technology, Internet of Things, devices, and cloud environments. Every industry has its share of cybersecurity risks, and ransomware attacks are increasingly targeting local governments, companies, and non-profits, threatening supply chains, government websites, and critical infrastructure (Ondrey 2021).

8.4.1  Cyber Threats As digitalization becomes more common and remote operations become necessary, guarding against cybersecurity breaches becomes more critical. The information technology (IT) and operational technology (OT) of plants may also be targeted in manufacturing, the utility sector and other industries (Ondrey 2021). Digital transformation typically involves providing more people and more access to data from the industrial control system (ICS) and other OT devices and systems. Worms may infiltrate the system undetected and target sensitive supervisory control and data acquisition systems that maintain control of an operation. IT, OT, and the IIoT lead to more system vulnerabilities. With more automation using these technologies comes increased responsibility to protect them from threats. Attacks are constantly evolving, as commoditized malware and advanced technologies provide new capabilities. Maintaining a competitive edge means leveraging IIoT technologies. More connections and automation mean more potential vulnerabilities that need to prioritize cybersecurity equally alongside their IIoT and digital transformation initiatives.

302  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence Attackers are always looking for new ways to escape IT notice, evade defense measures, and exploit emerging weaknesses, including taking advantage of remote access tools, and new cloud services. These evolving threats are of various types: • Malware refers to malicious software variants, such as worms, viruses, Trojans, and spyware that provide unauthorized access to a computer. Malware attacks are designed to get around familiar detection methods, such as antivirus tools, that scan for malicious file attachments. Sophisticated malwares take advantage of multiple vulnerabilities and accomplish their goals like being a money-making tool. Cyber threats can enter the company’s systems through emails and attachments and from portable storage devices, such as thumb drives and others. • Ransomware is a type of malware that locks down files, data, or systems, and threatens to erase or destroy the data—or release private or sensitive data to the public—unless a ransom is paid. • Phishing and social engineering trick users into providing their own PII or sensitive information, such as emails or text messages, credit card data, or login information. • Insider threats may come from current or former employees, business partners, contractors, or anyone who has presently or has had in the past access to systems or networks. Such people are an insider threat if they abuse their access permissions. Insider threats can be invisible to traditional security solutions like firewalls, which focus on external threats. • Distributed denial-of-service (DDoS) attacks attempt to crash a server, website, or network by overloading it with traffic, usually from multiple coordinated systems. DDoS attacks overwhelm enterprise networks via the simple network management protocol (SNMP) that is used for modems, printers, switches, routers, and servers. • Advanced persistent threats (APTs) may be from an intruder who infiltrates a system and remains undetected and leaves networks and systems intact so that the intruder can spy on business activity and steal sensitive data while avoiding the activation of defensive countermeasures. • A man-in-the-middle attack is an eavesdropping attack, where a cybercriminal intercepts and relays messages between two parties to steal data. On an unsecure Wi-Fi network, an attacker can intercept data being passed between guest’s device and the network. Key cybersecurity technologies and best practices The following best practices and technologies can help organizations implement strong cybersecurity without intruding on the user or customer experience: • Identity and access management (IAM) defines the roles and access privileges for each user. IAM methodologies include single sign-on, which enables a user to log in to a network once without re-entering credentials during the same session. Multifactor authentication requires two or more access credentials. User life cycle management manages each user’s identity and access privileges from initial registration through retirement. IAM tools can also provide cybersecurity professionals with deeper visibility into suspicious activity on end-user devices, including endpoints they cannot physically access. This helps speed investigation and response times to isolate and contain the damage from a breach. • A comprehensive data security platform protects sensitive information across multiple environments, including hybrid and multiple cloud environments. Such platforms provide automated, real-time visibility into data vulnerabilities, continuous monitoring, and simplified compliance with government and industry data privacy regulations. Backups and encryption are also vital for keeping data safe. • Security information and event management (SIEM) aggregates and analyzes data from security events to automatically detect suspicious user activities and trigger a preventative response. SIEM techniques include advanced detection methods such as user behavior analytics

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and AI with prioritized cyber threat response in line with an organization’s risk management objectives. • Zero trust security strategy assumes compromise and sets up controls to validate every user, device and connection into the business for authenticity and purpose. To be successful executing a zero-trust strategy, organizations need a way to combine security information to generate the context that informs and enforces validation controls. Companies often implement a “zero trust” policy when working with vendors and customers, as there can be risks in all parts of the supply chain.

8.4.2  Cybersecurity Response Responding to and investigating a cyber incident is procedural. First, operators contact the digital technology suppliers to see if the system will come back online. The vendors and operators then work together to discuss critical business operations that may help recover from the attack. Plant operators can effectively develop a response plan through a tailorable stepwise framework, often including the following specific steps (Ouertani 2020, Ondrey 2021): • Step 1: Establish communication and partnerships with equipment vendors and employees. Also, leadership should provide clear visualizations for all processes to quickly identify problems should an incident occur. • Step 2: Design incident response drills that help identify issues before a cyber attack. Drills presented as case studies allow teams to respond to an incident and reflect on what went well and what can be improved. • Step 3: Develop and track key performance indicators that may guide risk assessment. This implies management needs to discuss these indicators with security teams in advance to build a culture centered around security and not just react to breaches when they occur. • Step 4: Assemble a team with representatives from all stakeholders in the operation so that each department may input to and enact the security policies. The goal is that these policies are helpful in all departments and carried out regularly, often daily. • Step 5: Create a playbook that is adaptable since cyber attackers are becoming increasingly creative. All stakeholders should have input in these playbooks as they provide a framework for responding and setting boundaries, from operations to legal. • Step 6: Consider the views of stakeholders and maintain clear communication during a cyber event, as this is important to calm or console stakeholders. Some companies have even worked with professional firms that handle customer and stakeholder communications which helps maintain share price and confidence in the company. The objective of robust cybersecurity management is to protect assets with basic cyber hygiene, such as strong remote access, an electronic security perimeter, patching, monitoring, and incident response to ensure it is not more vulnerable. It is necessary to audit cybersecurity at least once per year and mitigate all risks that exceed the tolerance level. Recommendations may include additional network segmentation, endpoint hardening, security logging, proactive monitoring, and robust incident response. Security monitoring is not only an important mechanism to detect threats, but also helps with forensics and preventing similar future attacks. A centralized security information and event management (SIEM) platform can take workstations, servers and network equipment system events, and log and install them in a dashboard for prompt response. Implementing for everyone a foundational set of security controls, like patching, endpoint protection, backups, and inventory management, can reduce the cybersecurity risk by up to 85%. Output technology (OT) cybersecurity trails input technology (IT) cybersecurity and many companies implement the basics of cybersecurity for their OT assets, including collecting a holistic inventory of all hardware and software assets.

304  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence Data integrity Sensor data integrity ensures data integrity for smart and traditional sensors with signal tracing and validation. This addition to automation integrity helps reduce both process safety and cyber risk in support of digital transformation initiatives. For example, a sensor data integrity module provides multi-vendor discovery of smart IIoT and traditional analog sensors. Organizations must verify software update requests with IT by doing the following:

• • • • • • •

Ensure firewalls and other network software are up to date. Make sure to back up systems and data regularly. Use strong passwords, do not share passwords, and change passwords regularly. Do not save passwords on browsers. Do not open links or attachments in emails sent from unknown senders. Never install unapproved software and secure access keys and other physical security devices. For remote access, follow the company’s requirements.

8.4.3  Cybersecurity Training Industry leaders need to prioritize cybersecurity training at all levels of the organization (Bradley 2018, Goh and Bailey 2020). Cybersecurity training needs to be more comprehensive for employees. The idea of “awareness” training is not all-inclusive but should be a guide for better education. When leadership builds a culture around cybersecurity awareness, the whole organization may follow suit. Company should make cybersecurity part of company culture with improved training by developing a baseline for cybersecurity knowledge, providing hands-on training, incentivizing good cybersecurity practices, and investing in training engineers and other skilled labor.

8.5  Artificial Intelligence and Process Intensification A wide range of process intensification technologies consider combined unit operations, enhanced mixing, improved heat transfer, increased chemical reaction rates, reductions in waste generation, advanced separations, and manufacturing process improvement and automation. Artificial intelligence can help with the automation of processes toward intensification. Additionally, predictive analysis can improve safety, reduce emissions and improve sustainability. This can be beneficial in a case like a refinery that experiences an unexpected power outage and emits extensive sulfur dioxide. Predictive maintenance incorporates ML that can detect issues and find the optimal time to take a machine offline to perform requested maintenance (Golightly 2019). Preventative maintenance became the norm in recent years and improved downtime by 10 to 15 percent. Automation and predictive maintenance tools have the potential to significantly decrease costs. Current predictive models tend to be comprehensive and work with the process, equipment, and analytics to detect future failures (Le Pree 2018). An important question is: How to leverage AI and ML technologies for greater benefit in PI? Modeling and simulation use AI and ML by collecting accurate data to enable better decision-making and by developing modeling tools for design, control, and analysis of processes and products. A model-based approach integrates process simulation of new designs coupled with assessments across all sustainability dimensions. Ideally, analyses should be conducted with a life cycle perspective to capture both upstream and downstream impacts in improvements over a base case design and should move toward modular process intensification (MPI). Analyses can be conducted early during conceptual design to screen alternatives and then later during detailed design when specific PI strategies are considered. It may be possible to achieve sustainability in the chemical industry through MPI applications in at least the following three areas: (1) conversion of woody biomass to biofuels, (2) chemical

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recycling of waste plastics, and (3) production of biogas and biomethane from anaerobic digestion of food waste and animal manure. These areas represent opportunities for sustainability through greenhouse emission reductions and circularity of material flows, applying principles of green chemistry and engineering to process and product development (Van Gerven and Stankiewicz 2009, Lopez-Molina et al. 2020). Data induced process intensification is illustrated in Figure 8.18 and includes the following (López-Guajardo et al. 2021): • Design intensified equipment or enhanced current processes • Data-driven decisions • Monitoring process parameters and variables obtained from individual units for behavior prediction • Using information flow for individual operations to understand how to improve models and designs • Utilizing sensors in a reactive distillation column provide gradient information for • Concentration 42 • Temperature • Velocity • Insertion of data into predictive models helpoptimization, determine performance and process control equipment design andwhich process and streamline needs • Utilization of these predictive models to help identify where to utilize structure, energy, synergy, and time approaches for PI improvement • Application of column targeting tools thatautomated use the knowledge operations (including or self- domain and models to improve PI through other Interoperability domains, particularly concert with can thermodynamic meansinthat a process exchange andanalysis comm and leading to thermodynamic optima (Demirel 2013a, b,decisions. Demirel and Gerbaud 2019). synchronized data-driven Structure •Reduced size and structure design

Time •Reduced time for operation

PI4.0 with Data knowledge domain

Energy •Alternative energy sources

Synergy • Multifunctional equipment

Figure 8.18.  Data managementinduced induced process Figure 8.18. Data management processintensification. intensification.

306  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence

8.5.1  Industrial PI4.0 The combined Industry 4.0 and PI is called PI4.0 (Lopez-Guajardo et al. 2021). Industry PI4.0 focuses intensified I4.0 for higher productivity, efficiency, and self-managing processes where engineers, machines, and equipment communicate and cooperate with each other directly (Figure 8.17). Manufacturing and logistics processes are integrated intelligently toward processes with smart value-creation chains. Real-time information sharing and usage enables companies to react during production to the availability of certain raw materials for optimal efficiency. Manufacturing can be controlled across company boundaries to save resources and energy. Reference architecture model for Industry 4.0 (RAMI4.0) helps in increasing the sophistication and integration of automation throughout manufacturing, maintenance, and product development. PI4.0 helps engineers to take a more automated approach by combining continuous monitoring with artificial intelligence and machine learning through comprehensive analytics programs. Also, it can lead to updates that promote bioeconomy and sustainability (Lopez-Guajardo et al. 2021). PI4.0 defines a new generation of PI strategies for system-level transformations. PI4.0 uses data-driven algorithms to understand other physical and chemical processes that improve equipment design, predictive control, and optimization. The use of AI techniques, particularly with ML, can accelerate equipment design and process optimization, and streamline with the emerging framework of the integration between circular chemistry, Industry 4.0, and PI (Lopez-Guajardo et al. 2021). For PI4.0 has an iterative nature due to the computational efficiency and flexibility of the process increases due to the constant, coordinated, and parallelized communication between the different operations (including automated or self-driven), methods, energy, and material distribution. Interoperability means that a process can exchange and communicate information, and leads to making synchronized data-driven decisions. Real-time capability helps capture and interpret the volume of data instantly that could intensify closed-loop systems to drive automated and self-driven plants. This includes the capacity to operate plants safely and remotely, reducing the risk of process harm. Virtual representation of a conventional process could be intensified by sensing different variables and parameters. This “digital-twin” allows data-driven simulations for the in‑silico exploration, design, optimization of chemical processes. This reduces risk of operation and prevents equipment failures (Lopez-Guajardo et al. 2021). Information transparency relates to the readiness of information from both the entire process and background/external knowledge. The data are constantly being generated, processed, and visualized in an interpretable manner that could be used instantly to adapt production process to market needs, and to shift and facilitate process improvements and intensification strategies. In the initial step, the monitored data generate raw information to be used in training different I4.0 methods (such as AI) and developing data-driven models. Next, a learning action is required in which all the insights from the data monitoring, simulations, and experiments are processed to obtain knowledge that could be used to (1) refine and redesign the initial prototype, and (2) filter the monitored data (exclusion of non-significant data) that will lead to obtaining of the “next generation” or epoch of new data and information. So, a defined design is established that minimizes the rate-limiting steps, energy and materials consumption, and cost, and leads to a precise control strategy. Finally, the intensified technology and control strategy is compared with conventional or available technology. After that an assessment helps quantify the impact of the intensified technology vs. conventional technology (Lutze et al. 2012, Sharma et al. 2021). Implementation of PI4.0 The implementation of PI4.0 could result in a series of challenges, mainly: • A competitive increase in the capital cost to achieve a PI4.0 plant due to the integration of sensors for data acquisition. Sensor integration for data generation and development increases cost. An increase in sensing or monitoring instruments as well as the technological infrastructure

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Identify Business and process drivers Rate limiting steps Available data Available intensifying techniques

Monitor Integrate online sensors Complete missing data Monitor and generate insight

Design Concept & protype Experiments and simulations Rerieve feedback Refine design

Learn

Implement

Create insights from data Implement I4.0 Develop data driven models

Compare PI alternatives vs conventionals Cost assessment Implement & control

Figure 8.19.  Iterative strategy for Industry PI4.0 (López-Guajardo et al. 2021).

Figure 8.19. Iterative strategy for Industry PI4.0 (López-Guajardo et al. 2021).

required to meet quality standards of different industries (chemical, biotechnological, food, manufacturing, etc.) could increase processing costs (Figure 8.19). • Typically, equipment, process and products are standardized or trained to achieve the desired quality and economic/sustainable goals. Within I4.0 methods that could benefit PI4.0, AI, particularly ML, is a critical tool for information extraction, data pattern recognition, and predictions in a process industry in the era of information technology. This can lead to knowledge Static, low-volume, discovery in aspects such as sustainability design, system integration, advanced process/quality High volume, high scrutiny, control, decision transactional, real-supports, intensification, and data-driven process modeling (Lopez-Guajardo structured ettime, al. 2021). data

Industry4.PI4.0 tools include the following: Enterprise Resource Planning • IIoT (Industrial internet of things) (ERP) 3. Maufacturing • Cloud computing Execition System • Mobile(MES) devices 2. Operations • Artificial intelligence (AI) 1. Control and field • Cybersecurity • Cyber-physical systems

In conjunction with Industry PI4.0, PI aims to:

Figure 8.22. Data sources used in enterprise resource

1. 2. 3. 4.

Reduce capital investment (for non-retrofitted solutions) Decrease process risk Minimize adverse environmental impacts Reduce energy and material consumption

8.5.2  Challenges of Application of Industry PI4.0 Various challenges exist in the application of Industry PI4.0. These include the following: • Decreasing unit costs may lead to increased capital costs for cyber infrastructure. • PI 4.0 standardization relies on monitored data for all connected equipment, and that may constitute a difficult transition for users. The best indicators for PI are the results of improvements in energy use, cost, and environmental impact (López-Guajardo et al. 2021). Information processing in living system and process intensification Based on what is known about living systems and how they process information, we may be able to adapt some strategies of living systems to the manufacturing sector, which can result in PI. Engineers may be able to develop systems that “self-correct” and use learned behavior to adapt current systems to changes as complexity increases, processes age, and understanding of behavior

308  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence further improves. Approaches that may be vital to future PI systems include AI and ML methods to minimize uncertainties (Demire 2014, Mizraji 2021). Potential machine learning methods for PI4.0 Control, real-time optimization, process scheduling, and supply chain operation areas share reinforce learning (RL) approaches. The main outcomes are the convergence of affordable and powerful computing/communications platforms, data processing, the ever-increasing automation of globally integrated operations, tightening environmental constraints, and business demands for speedier delivery of goods/services to market. Integration of first-principles knowledge with data-driven models to develop hybrid models more easily and reliably is normally a solid strategy. Cases of process intensification with PI4.0 Integration of PI and Industry 4.0 (PI4.0), as applied especially in the field of automated optimization and control of chemical reactions and operations, have been recently reported. These integrations include reactor optimization and process intensification of photocatalysis for capillary-based photo micro-reactors and optimized scandium recovery from bauxite residue in hydrometallurgy processes. PI for sustainable processing can effectively contribute to significant process improvements, with potential to reduce the environmental impacts in industry. For instance, reduced energy usage in the oil and gas industry is a promising way to achieve sustainable development. Moreover, eco-efficient and intensification approaches using I4.0 principles have been recently reported in many industrial applications including the acetic acid purification process, as well as the use of innovative hybrid processes, such as the catalytic reactive distillation techniques for producing ester compounds. Agricultural residue valorization and industrial waste treatment also benefit from PI4.0. Self‑driving laboratory for accelerated discovery The integration of PI and ML strategies offers faster development and more efficient manufacture of various products. In addition, the use of autonomous laboratories can help accelerate the discovery and optimization of new organic and inorganic components for a broad range of applications, such as materials science. The concept of self-driving laboratories capable of designing, executing, and learning from materials science experiments can reduce the time to develop commercially viable materials that involve complex processes and may require lengthy development (MacLeod et al. 2020). Data is at the foundation of PI4.0. Based on the real-time capability principle, the self-driven laboratory can instantly capture and interpret a high volume of data by using high throughput screening and analytical infrastructure. Moreover, the self-driving laboratory can learn and trains itself to find relevant processing parameters. The learning process is based on iterative design and execution of experiments.

8.6  Artificial Intelligence and Sustainable Engineering Artificial intelligence refers to the ability to mimic human capabilities such as learning from examples and experience, recognizing objects, understanding, responding to language, making decisions, and solving problems. With combinations of such capabilities, machines can perform complex functions such as driving a car. Many companies use AI for sustainable operation and business to achieve many important outcomes. These include the following (Beck 2020d): • Reduce the energy load of data centers, including reducing the energy cost of cooling by 40%. • Perform weather forecasting 30% more accurately. This helps renewable energy companies better manage their plants, thereby maximizing renewable energy production and reducing carbon emissions. • Reduce nitrous oxide emissions from coal burning companies and nitrous oxide-emitting utilities by using AI and satellite data to better predict energy consumption patterns and adapt operating systems, thus increasing efficiency by 20%.

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AI plays an important role in achieving the UN Sustainable Development Goals (SDGs). There are total of 17 SDGs that can be grouped under environment, economy and society for which AI has helped achieve most of the targets of SDGs. Regulatory bodies need to oversee the development of AI, which may strongly influence the future of humanity (Khakurel et al. 2018, Lahsen 2020, Nishant et al. 2020). AI can be described as a cluster of technologies and approaches that are statistical and exhibit aspects of human intelligence for performing various tasks that include analytical, intuitive, and empathetic intelligence. Four facets of sustainability are shown in Figure 8.20: • The social dimension refers to relationships between individuals and groups, including the structures of mutual trust and communication in a social system and the balance between conflicting interests (Khakurel et al. 2018, Visser and Crane 2010, Leal et al. 2022). • The economic dimension covers financial aspects and business value. It includes capital growth and liquidity, investment questions, and financial operations. • The environmental dimension covers the use and stewardship of natural resources. It includes questions ranging from immediate waste production and energy consumption to the balance of local ecosystems and climate change concerns. • The technical dimension covers the ability to maintain and evolve artificial systems (such as software) over time. It refers to maintenance and evolution, resilience, and the ease of system 46 transitions. Economic aspects Environmental aspects

Societal aspects

Technology socio-technology aspect, eco-technology aspect, economic-technology aspect

Figure 8.20. of Impact of technology on theline triple Figure 8.20.  Impact technology on the triple bottom of sustainability. Triple bottom line Interest in corporate social responsibility has led to the adoption by many companies of the ‘triple monitoring quantities and reductions of energy-related emission bottom line’, reporting social, environmental, and financial information so as to satisfy stakeholder greener transportation networks, monitoring deforestation, needs, as seen in Figure 8.20 (Badia et al. 2020). Sustainability quality refers to the environmental, (Sloan et al. 2020, Nti et al. 2022) societal, and economic impacts throughout the product’s life cycle, which is in line with a common Climate Change interpretation of sustainable development, as noted earlier: Sustainable development refers to • Use ML to optimize the match between development that meets the needs of the current generations while not impeding the ability of future generations of meeting their needs. A general system uses natural resources as input (I) • and produces outputs, both useful and navigation waste (andwith sometimes harmful). Thissafety type and of system • Optimize AI and ML; increase providealso incorporates dynamic feedback from outputs, in terms of reuse, recycle, and remanufacturing, to Biodiversity and Conservation support sustainable development (Alhaddi 2015, Mayer 2008, Liao et al. 2022). •

310  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence Artificial intelligence and environmental challenges AI can accelerate global efforts to protect the environment and conserve resources by detecting and monitoring quantities and reductions of energy-related emissions, including GHGs, helping develop greener transportation networks, monitoring deforestation, and predicting extreme weather conditions (Sloan et al. 2020, Nti et al. 2022). The main opportunities that come with AI platforms follow: Climate Change • Use ML to optimize the match between generation and demand in real-time, leading to better grid systems with increased predictability and increased efficiency, and use of renewable energy. • Use smart sensors and meters to collect data and monitor, analyze, and optimize energy usage. • Optimize navigation with AI and ML; increase safety and provide safety regarding transportation. Biodiversity and Conservation • Use AI to detect changes in land use, vegetation, forest, and natural disasters, with the support of satellite imagery. • Detect and monitor invasive species using ML and computer vision. Ocean Health • Utilize AI to gather data from ocean and aquatic species and habitats, pollution levels, and temperature. Water Issues • Project water usage, soil, and subsurface water conditions in a geographical area using AI, to support informed policy making and decisions. Healthy Air • Apply AI to record air quality and environmental data in real-time and adapt the highest filtration efficiency. • Send warnings to people living in urban areas, via AI-powered simulations, about the pollution levels of their areas. There are tools that can detect pollution sources quickly and accurately. • Reduce air pollution, using data from vehicles, radar sensors and cameras with AI. Weather Forecast and Disaster Resiliency • Monitor floods, windstorms, and sea-level changes with AI-powered predictive analytics and advanced sensor platforms. This technology can help concerned agencies take timely actions. Digital transformation and automation efforts offer advantages to downstream processing and scaling up of processes. Automating tasks and data in downstream processes improves efficiencies and data processing, allowing improvements in product quality and yield due to the continuous feedback of data. Signature elements of advanced manufacturing include the following (Vinuesa et al. 2020, Nishant et al. 2020): • The ability to manufacture affordable products that meet human needs in a sustainable manner. • Deep integration of manufacturing with process design, with the aim of considerable improvements and leading to intensified processes. • Continued development of advanced sensors, AI applications, and model-based simulation tools. Digital transformation leads to the emerging ability to co-optimize chemical products, processes, and supply chains (Figure 8.21).

Artificial Intelligence  311 Economics, Society, Environmental constraints System Enterprise Investment Input(s)

Output(s)

Production Distribution Materials Use Consumers Feedback Figure 8.21.  Possible system for producing sustainable product(s).

(s).

Industrial AI and sustainability in many capital-intensive organizations Industrial AIstrategies and sustainability strategies in many capital- are evolving toward creating the optimum plant and business model. AI and sustainability both mutually creating the optimum plant and business model. AI are andoften sustainability are catalytic and synergistic, they share the business drivers of creating safer and andas synergistic, as same they underlying share the same underlyin greener operations. The three key industry trends lie at the confluence of AI and sustainability: 1. Predictive maintenance 1. Predictive maintenance is the single analytics andlargest ML to use det case for Industrial AI and exploits advanced analytics and ML to determine the condition of a single asset or an entire set of assets (a a process or a manufacturing plant). The business goal is to predict when maintenance should be performed. Predictiveand maintenance usually combines performs predictive analytics onvarious loggedsensor events.readings including external data sources2.andQuality, performsreliability, predictiveand analytics on logged assurance is the events. second 2. Quality, reliability, and decisionassurance is the second-largest industrial AI use case category. It enables decision-makers to maximize the economics of business decisions by going beyond the 3. Processpredicting optimization equipment level and accurately future asset performance of the whole system. based capabilities across a system, including automating repeated 3. Process optimization is perhaps theacross most obvious and compelling use case involving decisions various applications, augmenting the asset multiple life cycle AI-based capabilities across a system, including automating repeated human tasks, enabling real-time decisions across various applications, augmenting the asset life cycle, and optimizing the value chain across different business dimensions. use assets, case employs advanced ML intelligence from different dataThis sources, and processes. methods, reinforcement learning and sophisticated deep learning neural networks, to infer analyzing data. data sources, assets, and processes. Training a system information and intelligence from different through machine learning is the core. AI can be very powerful in optimizing performance, accuracy, and quality by analyzing data. alm The Energy Information Administration (EIA) in the U.S. forecasts global energy demand to grow by almost 50 percent between 2020 and 2050. This will continue to drive the need for an 47 “energy transition” that complements the sustainability movement with “greener” energy sources. Digital twin models and advanced control have already proven to be important in the reduction of energy use, for instance in liquefied natural gas processing. For highly complex and demanding assets, data and AI contribute to making these investments self-learning, self-adapting, and selfsustaining. Organizations with the goal to reach “zero carbon” operations are increasing the pace of developing renewable power assets supported by AI (Wang and Srinivasan 2017). For example, wind farms adopt prescriptive maintenance methods for asset health alerts to maximize the capacity factor of these large assets, which have not yet established a long-term reliability and maintainability record. This advanced digitalization technology will be crucial in monitoring the health of equipment which is inherently installed remotely, under environmental stresses, and requires maximum uptime to be reliable and competitive in the energy mix (IEA 2019, IRENA 2020).

312  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence Digital technologies play a critical role in driving sustainability and environmental responsibility, which will continue to be a top priority. Industries such as pulp and paper, mining, transportation, and consumer packaged goods are now focusing on accelerating their digital transformation and achieving new levels of operational excellence. These industries benefit by installing sensors in stranded assets and linking them to enterprise systems or cloud-based systems. This enables the collection of real-time data from multiple stranded assets for desired predictive analytics. Through the analysis of data, both normal behavioral patterns and exact failure patterns can be predicted. A technology partner with domain expertise related to operational and business goals can assist effective decision-making. The value possible from asset optimization has increased notably, primarily because of the opportunities available to achieve unforeseen levels of asset reliability. A significant increase in reliability through predictive and prescriptive analytics can improve the health, safety and environment of employees and communities (Stock and Seliger 2016, Kiel et al. 2017, Svenson and Padin 2019, Thamik and Wu 2022).

8.6.1  Sustainability from Knowledge Creation Reliable service is every utility’s mandate although extreme weather conditions and wildfires may make it difficult to achieve. With these variations in mind, utility sector organizations recognize the need to be more resilient. Water utilities, for example, face the most aggressive resilience challenge from climate change, as more frequent droughts and periods of intense precipitation affect their ability to rely on reservoirs, aging infrastructure, and a dwindling workforce. One resilience strategy for both electric and water utilities is the pursuit of expanded and integrated planning across the business. Electric utilities are broadening their planning and integrating supply and demand across central and distributed assets. They also are reconsidering physical grid-hardening strategies and exploring non-wires alternatives to boost resilience. The growing mismatch between customers’ expectations of the benefits of technology adoption, such as advanced meters, and regulatory incentives to adopt those technologies, can lead to negative publicity when outages occur. However, reliability and sustainability often remain in conflict. For example, utilities increasingly are limited by growing public opposition to natural gas, highlighting a potential tension between sustainability and resilience objectives in the near term. Sometimes technology can manage this sustainability-versus-resiliency issue as demand-side resources and new and affordable technologies including energy storage and green hydrogen are brought into utility planning (Simons 2018, Tosic and Zivkovic 2019, Menon et al. 2019). Building on the existing technology foundation As noted previously, digitalization began with upgrading of the first plants from analog and paper-based to digital instrumentation and distributed control, generating much data that supported the first wave of digital applications. Big data and the IoT followed this with the emergence of high-performance computing, the cloud, internet connectivity and mobility to generate deeper insights into this data. This led to the capture of even greater value through improvements in operations and reliability, and ultimately to extending the lives of assets and maximizing the return on capital. Advancements in critical enabling technologies such as automated agents, ML and data science makes it possible to integrate new capabilities within existing applications. The digital continuum can help achieve optimal system designs, stable operations, and the elimination of unplanned downtime. Manufacturing and other industries are enhancing many of their existing applications and technologies with digitalization. As an example, companies are realizing the need for the advanced process control (APC) using AI, ML, and more robust data science modeling. APC may increase profitability, revenue, efficiency, and reliability with the target of safe, stable operations and meeting environmental regulations. Stable operations lead to greater reliability and better utilization of equipment through asset optimization over the full life cycle and across the entire system. Asset

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optimization involves digital technologies, and is being accelerated through AI, machine learning, and multivariate analytics. This is enabled by high-performance computing, the cloud and IoT connectivity (Masood and Egger 2019).

8.6.2  Data Sources Data are discrete and objective descriptions of events, for example, a temperature reading for an instrument in a process. Information is described as data that can be transformed to create a value in a specific context. Data and information are often used interchangeably (Elmendorp 2022). The industry and manufacturing sectors realize that information is of strategic value for resilient organizations. The data they collect, process, analyze, and review help organizations make daily business decisions after Industry 4.0 technologies are established. Figure 8.22 shows the two main types of data: • High-volume, transactional, real-time, and structured • Static, low-volume, high-scrutiny, unstructured The manufacturing sector collects a large amount of data. The types of data may be split into four levels where the inputs generated at one level are provided to the upper level toward a more integrated system (Figure 8.22) (Elmendorp 2022): • Level 1: Control and field involves the input data from the field generated byImplement sensors and Identify Monitor Design equipment. For example, temperature, flow rate, concentration, pressure, and many Business and Learn Comparemore PI Integrate online process drivers Concept & protype alternatives vs variables are recorded at frequent intervals. Create insights sensors Rate limiting steps conventionals Experiments and from data Completelow missing • Level 2: Operations involve level of automation controllers simulations including programmable logic Available data Cost assessment Implement I4.0 data Rerieve feedback (PLCs) that can ensure basic controls for processes and units and operate at high speeds. Available Implement & Develop data Monitor and Refine design intensifying control driven modelscontrol system generate insight system (MES) involves a distributed • Leveltechniques 3: A manufacturing execution (DCS) or supervisory control and data acquisition (SCADA) system to provide control of the facility across multiple systems. • Level 4: Enterprise resource planning provides integrated resource planning and controls for Figure 8.19. products Iterative strategy Industry PI4.0 (López-Guajardo et al. 2021). desired and theirforproduction. For full benefits of industry 4.0 technologies as well as effective use of DTs, data must be aligned at all levels.

High volume, transactional, realtime, structured data 4. Enterprise Resource Planning (ERP) 3. Maufacturing Execition System (MES) 2. Operations 1. Control and field

Static, low-volume, high scrutiny, unstrucured data Update reality

Describe reality

4. Inspections piping information equipment files 3. Piping and instrumentation diagrams 2. Specifications 3D models 1. Electrical one-lines

Figure 8.22.  Data sources used in enterprise resource planning (Elmondorp 2022).

Figure 8.22. Data sources used in enterprise resource planning (Elmondorp 2022).

314  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence Data quality Data quality refers to fitness for use. Quality databases and big data allow management of change (MOC) to identify the trends related to a process or an equipment. Table 8.3 shows the major data quality dimensions. For example, the completeness dimension for company may define a complete block diagram that should contains revisions, approver, reviewer, editor, and team elements of information. A company would define the format dimension for each piping and instrumentation diagram; there is a process flow diagram for each process. Figure 8.22 shows the hierarchy of data sources for structured and unstructured data types, which are related to each other, and which update reality and describe reality. Structured data starts with control and field measurements, followed by operations, manufacturing executing systems (MESs), and enterprise resource planning. Structured data are often used for analytics and for reporting and are stored in databases. It is relatively more difficult to gain information from unstructured data (Res and Kenett 2018). Big data combines different data sources at large volumes with varying complexities across the structured and unstructured data. Big data involves automation technologies and predictive maintenance with major elements. These include the following:

• • • • •

Volume refers to the large amounts of data in transit or stored Variety refers to wide variety of structured and unstructured data sources Velocity refers to the speed at which the volume of data increases Veracity refers to the accuracy and quality of the data sources Value refers to the value created by the data for the company Table 8.3.  Data quality dimensions (Res and Kenett 2018, Elmendorp 2022). Dimension

Dimension

Accuracy

Format

Reliability

Importance

Timeliness

Sufficiency

Relevance

Importance

Completeness

Usability

Consistency

Clarity

Precision

Conciseness

Interpretability

Informativeness

Content

Level of detail

Scope

Freedom from bias

Maturity assessments An organization can measure itself against a set of desired criteria including disciplined process, standard consistent process, predictable process, and continuously improving process. A maturity model manufacturing sector depends on information and communication technology (ICT). The level of optimization of ICT and how its aligned with business goals and constraints are critical for the business success. ICT maturity aims at better management of software systems. Maturity models can improve procedural development when the model considers process specifics. Maturity assessments can be a great management tool for a defined scope in terms of validation and providing direction if necessary. Organizations, for example, set up a goal as business agility to address problems quickly. The purpose of assessment is to provide a roadmap with improvements than

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can be executed toward the goals of the company (Elmendorp 2022). Some possible improvement projects area:

• • • •

Define related specific and realistic goals. Measure the success of the goals. Assign the responsibility for an organization for the goals. Specify a timeframe to complete the goals.

The identified goals lead to a method to complete the specific projects that can increase the level of maturity and the data quality (Weber et al. 2017).

8.6.3  Turning Data into Action Leveraging smart infrastructure including advanced metering infrastructure and sensors to enable data-driven utility operations has long been a work in progress, and advancement has been uneven. The need to gain comprehensive insight into systems leads to creating digital twins of all their assets, deploying technology capable of modeling how their systems might react to major storms, and preventing cyber attacks and other risks. These utilities are enabled by the availability of software as a service and cloud-based solutions from third-party vendors, making it easier to close the digital divide. Still some utilities experience budget constraints, competing priorities and regulatory hurdles, affecting their ability to have smart distribution infrastructure that gathers data from sensors for a range of decision-making activities (Ordieres-Meré et al. 2020). Digital tools with cloud technology make the process more collaborative and allow for a single data storage location that is sharable across an entire organization. Past experiments can be accessed, and researchers have available all relevant research with real time feedback and new ideas for new products with better guided experimentation. In the field of coatings, for example, digital tools can integrate multiple file formats, data types, and sources of data into a single system to generate visual analytics, so researchers can quickly assess pigmentation, surface area coverage, and drying time simultaneously, to determine which coating meets product specifications (Sayyadi 2019).

8.6.4  Workflow Process Poor engineering information could adversely affect the operating budget of a company as it interrupts the workflow (Geotzer and Volk 2019). Leadership must be convinced of the impact of information quality on the workflow, as seen in Figure 8.23, which includes the verification of the correctness of information beside the collection of information. Large information technology may be costly with limited benefits. However, improving the quality of data coupled with digital transformation and improving how workflow progresses would be more beneficial. Industry 4.0 implementations involve the vertical integration of information systems and energy management to ensure a company achieves more advanced and coordinated operation. The second stage involves automation and virtualization toward for greater flexibility, leading to more resilient production processes. Therefore, improvement and ability to transform are dependent on the quality and integration of data (Elmendorp 2022).

8.6.5  Sustainability Focus Digital technologies in the form of e-health services, robotics, or emission reduction techniques can help individuals, organizations, and nations achieve or move towards the sustainable development goals. Information and communication technology (ICT) constitutes the new “digital age”, encompassing a richness of software and hardware and linked processes. Analyses of genetic sequences, personal health data, phone records, and social media profiles have altered the way humans interact with each other and with their natural environment. Digital technology, as in the example of big data, offers new possibilities and pathways of how to shape the future and research

316  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence

Searching database Dublication databases

Searching publications

Verifiying correctness of information

Reviewing standards

Workflow process Resolving interpretation differences

Manually re-entering information

Revising information

Updating information Sharing information

53

2022). (Elmendorp information engineering that requires 8.23.  Workflow Figure Figure 8.23. Workflow that requires varied varied engineering information (Elmendorp 2022).

Ideation Search past experiments Potential product candidate Computational modeling

Documentation Efficient data recording Real time collaboration and data sharing

Analysis Making decisions Data access and analysis Data processing Visual analytics Assess promising product

Figure 8.24.  Enabling digital technologies that support sustainable operations for products across the life cycle.

(Figure 8.24). Algorithmic capacities allow for data processing and analysis, opening unseen cycle. predictive capabilities. ICT and big data can help promote sustainability, because the societal As notedconnected, earlier, AI and are often catalyticeffects and synergistic, sharing complexity is strongly andsustainability these systems mayboth leadmutually to cascading that increase businesstechnology drivers of creating a better plantsustainability for vulnerability. A underlying big data-driven can help enhance in the environmental, operations (Pendyala Morse social, and economic spheres (Seeleand and Lock2020). 2017, Kamble et al. 2018, Demirel 2021). sustainability and profitability with greater resilience, flexibility, and agility. H As noted earlier, AI and sustainability are often both mutually catalytic for andspecific synergistic, explicitly designed to deliver comprehensive business outcomes needs sharing of capitalthe same underlying business better plant for of thecompanies future byaround enabling safer, industries and is drivers expectedof to creating accelerateathe transformation greener, and faster operations (Pendyala and Morse 2020). Operating plants and processes with 2019). digitalization leads to safety, sustainability and profitability with greater resilience, flexibility, and Some avenuesisfor future research in sustainability with relation to digitalization arefor agility. Hybrid technology explicitly designed to deliver science comprehensive business outcomes  specific needs of capital-intensive industries and is expected to accelerate the transformation of makers. A digita companies around thebeing, worldincrease (Birou et al. 2019).

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Some avenues for future research in sustainability science with relation to digitalization are as follows: • The governance of sustainability in a digitalized environment may be a central concern for policy makers. A digital “global participatory platform” making use of ICT can help promote human well-being, increase diversity, inclusion, and equity, and promote sustainability and resilience. • Sustainable development is important in the digital age. Some studies have focused on developing countries, but further insights into the potentials and threats of digital technologies for global sustainable development are needed. • Digital technology comes with enhanced surveillance technologies, affecting legal rights to privacy of individuals and organizations. Therefore, further research is needed both for protecting persons and organizations and making the most out of digital data to promote sustainability. Decarbonization, the circular economy and broader access to electricity and clean water will drive sustainability, with such questions as: • How can I maximize a team’s effectiveness using digitalization and enable long-term remote working? • What is my turndown limit to keep plants running within safe limits, without damaging units and equipment? Sustainable development was defined by the United Nations Commission on Environment and Development in 1987. Since then, various private organizations have incorporated sustainable development into their operations with the goal of continuous improvement. Process intensification supported by artificial intelligence represents an important enabler of sustainability by its potential to address the issues, including maintenance, energy efficiency, productivity, and safety. The future of maintenance is likely to be a combination of preventative and predictive methodologies as more diagnostic tools become available. Predictive maintenance informs better business decisions. Innovations leading to predictive maintenance are the industrial internet of things (IIoT) and AI. Three factors of predictive maintenance are acquire, accumulate, and assess. Acquiring the data requires the use of sensors and a communication system to measure data. Accumulated data may already be in a database, the control system, or a wireless cloud system. Assess refers to the analysis of the data to make decisions. Initially, working process models are provided for correct operation. Next, the generated data in an automated process compare process models and operations through ML where the system detects and knows the differences between normal and abnormal operation. Another potential use of this information is to create a network that involves all parties in the equipment’s life cycle, to develop a DT and to promote data sharing for an asset. Environmental regulations, energy and water conservation, and climate change are concerns for industrial sectors and their customers. In particular, the circular economy requires a “full-cycle” approach to production and conservation of resources and protecting the environment (Beck 2020a). AI-powered innovation and carbon capture can lead to greener operations, sometimes with a strong return on investment. Based on the trends highlighted in Figure 8.25 it is evident that industrial AI and digital solutions are key enablers of sustainability goals. Digital transformation strategies are increasing their emphasis on sustainability-related objectives, mainly focusing on energy efficiency, pollution control, and value chain optimization. The International Energy Agency (IEA 2019) reported that Industrial AI and digital solutions can help improve energy efficiency by as much as 30% for industrial sector operations (Ferdyn-Grygierek and Grygierek 2017, Custeau 2019, Demirel 2021).

. 318  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence Enabling digital technologies

Design phase With new idea

Advanced Process design

Advanced materials & chemical blocks

Industrial value chains

Consumers

Materials Enabling digital technologies Advanced processes Feedstock Figure 8.25.  Enabling digital technologies for sustainability.

An AI An model-based AI model- approach can integrate process simulation of new designs coupled with assessments across all sustainability dimensions. Analyses be conducted with a life cycle across all sustainability dimensions. Analyses shouldshould be conducted with a life cycle perspective to capture both upstream and downstream impacts in addition to the MPI processes themselves. The potential for achieving sustainability in manufacturing applications often lies in three areas: 1) conversion of woody biomass to biofuels, 2) chemical recycling of waste plastics, and 3) production of biogas and biomethane from anaerobic digestion of food waste and animal manure, which permits significant gains in sustainability through GHG emission reductions and circulation of material flows. Evaluating and building the resilience of the sustainable components of a system includes preparing for the fact that less sustainable components are more likely to fail during a disturbance. However, for example, by focusing on investments to build resilience with one type of energy only would be more likely to fail in response to a disturbance in supply chain. Later, the resources that would be used to repair those less sustainable system components can be focused on sustainable objects. By increasing the resilience of AI with cybersecurity, the industrial sector can be more sustainable. Although indicators and indices can be useful in describing sustainability and resilience, a multi-dimensional decision matrix with weighted aggregation strengthens the effectiveness of indicators as a means of performance assessment (Custeau 2019, Kamble et al. 2018). Aligning environmental sustainability with artificial intelligence The environment is being damaged at alarming rates, undermining prospects of reconciling human wellbeing and development and respect for planetary boundaries. Existing institutions are not steering us safely towards environmental sustainability and the common good. The challenge of fostering positive social and environmental policy based on scientific recommendations is growing steeper, not weaker, in the age of AI and social media. Political uses of AI are possible, in the form of data flows, machine learning, and large-scale data analytics (Lahsen 2020). Responsible information and communications technology governance requires, and inherently involves, political decisions that shape the future for the better. Valorization of political neutrality undermines the needed support for action-oriented research on how to nurture the needed institutions and transformations. Definition of what constitutes “good” alternative guiding principles and institutions requires assessment and deliberation. Documented evidence has been reported of the potential of AI acting as (a) an enabler or (b) an inhibitor for each of the SDGs. AI under a sustainability analysis perspective refers to a sustainability

Artificial Intelligence  319 Environment Knowledge management GHG emission reduction Waste management Pollution reduction Efficient resourse use

Artificial Intelligence Economy Rising competitiveness Rising unemployment Globalization Corporate restructuring Asset life cycle IIoT, PI4.0 Predictive manintenance

Society Digital connections Cybersecurity Liability concerns Employment Organizational restructuring Work efficiency Deep learning

Figure 8.26.  Sustainability and industrial AI (Visser and Crane 2010). .

analysis of the AI field using the lens of the three dimensions. Figure 8.26 shows the sustainability analysis diagram of the AI field. However, AI brings not only opportunities but also risks for negative impacts for sustainability, such as personal and cybersecurity risks and reductions in employment computational resources only available through large computing cent (Khakurel et al. 2018). However, positive impacts of AI on the SDGs are considerably higher than that of the negative impacts (Vinuesa et al. 2020). 2020).and societal outcomes Artificial intelligence Sixty-seven targets within the society group of the United Nations SDGs could potentially benefit from AI-based technologies. For instance, in SDG 1 on no poverty, SDG 4 on quality education, SDG 6 on clean water and sanitation, SDG 7 on affordable and clean energy, and SDG 11 on sustainable cities, AI may act as an enabler for all the targets by supporting the provision of food, health, water, and energy services to the population (Vinuesa et al. 2020). A AI can also support low-carbon systems through the creation of circular economies and smart cities that efficiently use their resources. For example, AI can enable smart and low-carbon cities encompassing a range of interconnected technologies such as electrical autonomous vehicles and AI can enable demandis response in the electricity sector (with but benefits across smart appliancesofthat SDGs 7, 11, and 13 on climate action). AI can also help to integrate variable renewables by enabling smart grids that partially match electrical demand when solar and wind energy are available. Fewer targets in the society group can be impacted negatively 56 by AI. Many of these are related to how the technological improvements enabled by AI may be implemented in countries with different cultural values and wealth. Advanced AI technology, research, and product design may require massive computational resources only available through large computing centers. These facilities have a very high energy requirement and carbon footprint. Some estimates suggest that the total electricity demand of information and communications technologies (ICTs) could require up to 20% of the global electricity demand by 2030, from around 1% today. Green growth of ICT technology is therefore essential (Lahsen 2020). Although AI-enabled technology can act as a catalyst to achieve the 2030 Agenda, it may also trigger inequalities that may act as inhibitors and lead to additional qualification requirements for

320  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence any job, consequently increasing the inherent inequalities and acting as an inhibitor towards the achievement of this target. Artificial intelligence and economic outcomes The technological advantages provided by AI may also have a positive impact on the achievement of several SDGs within the economy group of the United Nations SDGs. Benefits from AI on 42 targets (70%) from these SDGs, whereas negative impacts are reported in 20 targets (33%). A net positive impact of AI-enabled technologies is associated with increased productivity, but the literature also reflects the potential negative impacts mainly related to increased inequalities. In the context of the economy group of SDGs, if future markets rely heavily on data analysis and these resources are not equally available in low- and middle-income countries, the economic gap between wealthy and poor nations may be significantly increased due to the newly introduced inequalities significantly impacting SDG 8 (decent work and economic growth), SDG 9 (industry, innovation and infrastructure), and SDG 10 (reduced inequalities) (Vinuesa et al. 2020). AI can also exacerbate inequality within nations. By replacing old jobs with ones requiring more skills, technology disproportionately rewards the educated. Moreover, automation shifts corporate income to those who own companies from those who work, there leading to a transfer of revenue from workers to investors (Vinuesa et al. 2020). Artificial intelligence and environmental outcomes The three SDGs in this group are related to climate action, life below water and life on land (SDGs 13, 14, and 15). Benefits from AI include analyzing large-scale interconnected databases to develop joint actions aimed at preserving the environment. There is evidence that AI advances will support the understanding of climate change and the modeling of its possible impacts. AI will support low-carbon energy systems with high integration of renewable energy and energy efficiency, which are all needed to address climate change. AI can also be used to help improve the health of ecosystems. Preventing and reducing environmental pollution of all kinds, can benefit from AI through algorithms for automatic identification of possible oil spills (Vinuesa et al. 2020). Research on artificial intelligence in sustainable development The more we enable SDGs by deploying AI applications, from autonomous vehicles to AI-powered healthcare solutions and smart electrical grids, the more important it becomes to invest in the AI safety research needed to keep these systems robust and beneficial, and to prevent them from malfunctioning or being hacked. Furthermore, although numerous studies are reported suggesting that AI can potentially serve as an enabler for many SDG targets and indicators, a significant fraction of these studies have been conducted in controlled laboratory environments, based on limited data sets or using prototypes. Hence, extrapolating this information to evaluate the real-world effects often remains a challenge. This is particularly true when measuring the impact of AI across broader scales, both temporally and spatially (Vinuesa et al. 2020). Artificial Intelligence (AI) has become an important area to tackle most environmental sustainability issues, including biodiversity, energy, transportation, and water management. Biodiversity research has developed machine learning to predict ecosystem services, including predicting and optimizing water resource conservation. Area neural networks, expert systems, pattern recognition, and fuzzy logic models are the focus areas in energy. Applications of computer vision and decision support are found extensively in transportation. Timely monitoring of interventions is required to improve environmental sustainability (Kwok 2019, Hardia et al. 2020, Nti et al. 2022).

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Domains of artificial intelligence for environmental sustainability In AI, six domains (logic and methods) exist to tackle environmental sustainability challenges: • Implementing any AI involves the various ML processes, with the purpose of activating past data, experiences, and statistical data. These data, help in the performance of assigned tasks to solve problems, including environmental problems. Thus, ML uses various mathematical approaches to help develop and analyze more advance technology. • An artificial neural network (ANN) is an ideal method and its operations do not form part of the usual conventional computational task that is programmed, but rather is an inbuilt task. Thus, instead of thorough programming, trained neural networks come with many advantages over traditional systems because of their ability to be trained. For example, in terms of parallel reasoning, an ANN can predict and forecast water resource variables. Also, in the field of agriculture, ANNS can be used to predict nutrition level in crops and to differentiate weeds from the crops. • Robotics is the combination of different scopes using cognitive behavior of humans, including emotions, and thinking. Farming is amongst the vast areas in which application of robotics is common, for example, in seeding, planting, fertilizing, weeding, spraying, irrigating, harvesting, and shepherding. The use of robots makes work simple that in many cases would take many human hands to complete. • The use of natural language processing (NLP) is an evolving theme, and its main role is directly associated with the capacity of AI to follow human cognition, while seeking various understandings and using text analysis to create human language (Nti et al. 2022). • Fuzzy Logic (FL) is an approach to human reasoning. This approach allows for more values other than the usual binary Boolean logic. In addressing problems which stem from routing, including mobility on-demand as well as fraud prevention, fuzzy logic decision support systems have been applied. • Expert Systems (ES) have to do with knowledge bases and databases including inference engines and can be used to solve problems that are complex in nature. Recent studies have outlined how decision making is supported better where Expert Systems apply both ML models and fuzzy logic to decision making. Challenges in applying AI for environmental sustainability AI addresses environmental problems based on relying on past data in ML. This is due to the unpredictable and evolving nature of AI because the variability of the human behavior is difficult to incorporate into ML models. Thus, past data before any extensive human activity reflect ages and climate cycles and thus estimating potential climate change is difficult. Also, managing variance where past data is fitted into models as most ML practitioners pay special attention to them. New data associated with these models can be generalized and this is a recipe for inaccurately predicting future scenarios which is technically known as variance-bias tradeoffs. Inadequate measurements of performance and uncertain human behavioral answers to various interventions based on AI-support is another challenge in applying AI for environmental sustainability. Measurement and monitoring of interventions are critical in advancing environmental sustainability. The measurement is complex and often unsuccessful and as such combining both technical and analytical performance in a holistic metric leads to the success of AI for environmental sustainability. AI applications are as intelligent as humans for decision-making, but their focus is quite different in terms of human responses to decisions. However, it is critical to understand the behavioral responses while evading the common problems known to be associated with the advancement of technology because of the trap of rebound effects. Various interventions are required to measure both positive and negative impacts of AI on environmental sustainability. Appropriate metrics go beyond purely technical dimensions since the

322  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence true value of AI is about how it facilitates and fosters environmental governance. Policies should reflect the urgent need to surmount the challenges associated with AI application, in line with shifting towards sustainability (Wang and Srinivasan 2017, Duan et al. 2019, Abduljabbar et al. 2019, Jha et al. 2019, UNESCO 2019, Nishant et al. 2020). IoT and energy management IoT-based energy management with edge controllers provides better matching of renewable energy outputs with power requirements. Renewable energy systems, including biomass, geothermal, solar, and wind, usually generate small and intermittent power, often operating remotely, leading to grid instability in matching output to demand. Renewable power tends to depend on environmental conditions and becomes somewhat unpredictable and difficult to control. Local power storage can help match renewable output to grid demand. However, power storage may be expensive, although battery storage costs are becoming affordable, allowing the technology to be applied more widely, if battery degradation and disposal issues are addressed properly. Therefore, close coordination is necessary among renewable power plants, the grid, and electricity storage to match output with demand (Yamaguchi 2021). In a remote operation center, challenges of a mixed generation portfolio are managed by shared automation, and asset management infrastructure optimizes grid efficiency and cost. There are no standards among renewable power supplies regarding the type of automation, with the existence of various technologies and variations in power outputs. This makes the integration of these various automation systems critical yet essential. Each renewable power site benefits from real-time monitoring and IoT capabilities with various sensors to determine electric current and voltage, flow rates, pressures, temperatures, and vibration readings. The edge controller, as a new automation component, is the main component of IoT-based energy management system. An edge controller can integrate with original equipment manufacturer automation technologies supporting various means of digital communication and hence provide the functionality required to control local equipment with remote operation control. Such controllers provide an IoT-based energy management system and add optimization predictions using big data and analysis algorithms, resulting better sustainability and a lower levelized cost of power. With internet-based communication, cybersecurity concerns must be addressed within national guidelines (Golan et al. 2019, LePree 2020, Demirel 2021). Better design Internally and externally constrained energy and feedstock resources, as well as increased accountability for sustainability, requires the best possible design, leading to better safety and decreased emissions. Large amounts of energy are needed to convert raw materials to valuable products with required specifications. Better design manages the complex interactions among feedstocks, process equipment, utilities, and chemistry and can lead to safer and more efficient startups and operations, helping prevent incidents that can adversely impact sustainability. Asset design and operations with performance engineering can optimize the process. Traditionally, cost savings are related to efficiency efforts, but now companies are moving toward sustainability and to waste reduction from production units as well as efficiency enhancements through digital technology (Nishant et al. 2020). Risk priority Criticality and risk are not just about the mechanical integrity of a single process and its impact on the process. Criticality also includes the integrity of a machine as a component part of a whole system. Comprehensive risk should be considered for operational performance, production impact, feed availability and selection, plans and schedules for manufacturing, inventories, and delivery commitments. Diverse situations occur and may need to be considered individually in real time. For instance, an unspare small pump on a fractionator recycle loop may present as much of a complete unit breakdown risk as the large wet gas compressor. Proper safety and environmental behavior

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are necessary for maintaining a manufacturer’s license to operate. Asset performance management helps in reducing productivity losses by avoiding breakdowns with timely warnings since unplanned shutdowns due to equipment breakdowns are very problematic. For example, there would be no clear prepared plan to avoid potential spills or gas flaring in case of breakdowns (Schwartz 2020). Process analytical technology (PAT) may lead to more sustainable and higher quality chemical syntheses and processes with fundamental understanding of chemical reactions and performance parameters. Sustainable chemistry via PAT leads to quality by design for fine chemical and pharmaceutical product development and manufacturing, with low risks for productivity, quality, and sustainability (Hebrault et al. 2022).

8.6.6  Sustainable Artificial Intelligence Sustainable artificial intelligence (SAI) can be defined as “the ability of a system to interpret external data correctly, to learn from such data, and to use those learnings to achieve specific goals and tasks through flexible adaptation.” While the general definition of AI lacks any normative goal, task description, or application rules, there are more and more attempts at linking the concept of AI with normative ideas, such as those associated with the concept of sustainable development. Responsible AI is concerned with ensuring fairness and accountability. Human-centered AI puts human aspirations, such as human rights, social participation, or environmental protection, at the center of AI design. Ethical AI introduces principles of transparency, justice and fairness, responsibility, and privacy to AI design and use. SAI is basically concerned with exploring AI’s contribution to sustainable development. Promising AI technologies for sustainable industrial development cover several topics, including knowledge representation, machine learning platforms, action recognition, optimization and solving, and identification technology (Vié et al. 2019, Pendyala 2020d, Sullivan et al. 2018). The impact of the use of AI in the context of sustainable development is less known (Duan et al. 2019). Corporate culture is multi-dimensional and multi-level concept, which defines the core values, assumptions, interpretations, and approaches that characterize a company and its interrelations with its members and consumers. Table 8.4 shows some corporate features for achieving sustainable AI. Corporate culture influences the use of digital technologies in companies, and thus also the use of AI, in terms of sustainable development (Varela et al. 2019). Artificial intelligence solutions for society can help reduce pollution, waste, or carbon footprints. There are also risks associated with the use of AI, such as increasing inequality in society or high resource consumption for computing power. How corporate culture influences the use of AI in terms of sustainable development should be considered. As already noted, this type of use is referred to Table 8.4.  Sustainable artificial intelligence and corporate culture (Duan et al. 2019). Corporate feature

Sustainable artificial intelligence

Values, beliefs, attitudes

Environmental protection attitude Managers and employees value environment Transparent corporate value

Public relations

Behavior of company and employees supporting environmental protection

Collaborations

Corporations is willing and able to cooperate with stakeholders

Ethics, leadership

Develop of environmental standard and responsibility AI application rules

Internal capabilities

Environmental knowledge and awareness SAI literacy

Strategic approach

Long term planning Targeted AI usage

Corporate culture and structure

Agile employees and leadership in business organization

324  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence as sustainable artificial intelligence. There are various factors of sustainability-oriented corporate culture in the concept of SAI. Use of AI in a company can create the basis for SAI, for example in influencing the corporate culture that would facilitate the use of AI via SAI. Successful application of AI for decision making may result in a change of culture in organizations and in individual behavior (Duan et al. 2019). If AI improves internal capabilities, the company’s behavior may become more sustainable in the sense of the sustainable development goals (SDGs). For example, the restructuring of workplaces, based on data-driven algorithms that prioritize economic outcomes, would form an unsustainable practice. The conceptual relations between SAI, responsible AI, ethical AI, and human-centered AI influence corporate culture toward sustainability (Beck 2020a, Wu et al. 2019). Artificial intelligence and development of materials Machine learning as a subfield of AI is emerging within the sustainability agenda because it promises to benefit science and engineering through improved quality, performance, and predictive power. The application of AI may lead to the development of new materials in an environmentally friendly way by combining design of experiments with new machine learning modules that support vector machines and a desirability function. The presented AI-based methodology leads to solving the challenge of the materials fabrication-sustainability nexus and facilitates a paradigm shift from the wet lab to the wired lab (Hardia et al. 2020).

8.6.7  Process Intensification and Artificial Intelligence Applications The application type can be determined based on where AI is applied, and multiple application types may be considered. For example, an AI technique can improve the yield of a unit operation, which will affect the mass balance of the entire industrial plant. Then both the unit operations and processes should be considered. Based on the AI application type, potential impact categories need to be selected based on the stakeholders’ interest, preliminary assessment, and engineering experience. Some impacts of PI include economic, energy, environmental, safety, human factors, and time. Figure 8.27 shows the major types of PIs under each impact category. In step 5, to quantify Step 1 Determine artificial intelligence application levels Research and development Unit operations Process and plant Supply chain

Step 4 Evaluate indicators Data collection Data verification Quantify the indicators for the system with and without artificial intelligence adoptation

Step 2 Select impact categories Economics Energy Environment Safety and operaion Time

Step 5 Quantify impacts and uncertainties ∆E = PIAI - PIbase design

Step 3 Select indicators Economic indicators Energy indicators Environmental indicators Safety and societal indicators Time indicators

Figure 8.27.  Major types of process intensifications broken down by impact category.

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the impacts and uncertainties, a base design with PI (PIbase design) is compared with that of improved design with PI and AI (PIAI), Energy and time are separated from the other three categories. Although reducing energy consumption and computational time can improve environmental and economic performance, such benefits need to be confirmed with additional assessments such as life cycle assessment (LCA) or techno-economic analysis (TEA) (Liao et al. 2022). Safety and human factor aspects 62 Safety and human factor aspects range from process safety to employee productivity. Fuzzy logic integrated product design framework improves product safety. AI improves the process monitoring and fault diagnosis, enhancing s the overall production safety. For instance, AI-enhanced process monitoring has been developedrange for a from rectification a chemicalproductivity. reactor, the Fuzzy monitoring processcolumn safety and to employee logic for polymerization processes and refiners. In addition, AI-based fault detection has been developed for reciprocating compressors. Theproduction safety impacts those AI-based models canprocess be quantified by , enhancing the overall safety.ofFor instance, AI-enhanced monitoring forfault a rectification column and a chemical the Golightly monitoring2019, for polymerization indicators such as detection rate and reliability (Badri reactor, et al. 2018, Beck 2020b). - to different application types, various PIs are available to For each impact category according assess the specific impacts of AI technologies. Figure 8.28 shows the major types of PIs under each (Badri et al. 2018, Golightly 2019, Beck impact category. These additional aspects can be considered for 2020b). indicator selection:

Material flow assessment Life cycle assessment Process design and simulation Property estimation methods Energy/exergy analysis Failure analysis

Process and material efficiency Economic indicators Energy efficiency indicators Emissions Life cycle environmental impact Product safety/health impact

Data source

Techno economic assessment

Performance indicators

Methodology

• availability. The data to requirement of specific PIs and evaluation methods are shown in eachData impact category according Figure 8.27. Primary data is obtained 8.28 through direct secondary data can be shows themeasurement, major types and of PIs under each impact acquired from the literature, existing databases, or engineering estimations. The primary data of • Data PIs oravailabi TPs by direct measurement should be used whenever available. Some data could be used Figure 8.27.more than one indicator. For example, if the process data of energy and material flows to evaluate are available, it is highly likely that different economic, energy, and environmental indicators can be estimated using TEA, LCA, and material flow analysis (MFA). A brief introduction of methods shown in Figure 8.27. are available, it is highly likely that different ec • Analysis capability. A variety of analytical methods for different PIs are provided in Figure 8.27. of those methods shownMost in Figure 8.27.methods require specialized tools (e.g., engineering simulation software) and human resources experienced practitioners). resources • Analysis capability. A variety of (e.g., analytical methodsLCA for different PIs areWhether providedthose in Figure are available for analyzing a specific PI in a reasonable timeframe is another factor to be considered. analyzing specific PI inSome a reasonable is another beenergy considered. • Indicator aapplicability. PIs aretimeframe highly generic (e.g.,factor yield to and efficiency), while • others are limited to specific application types (e.g., isentropic efficiency for compressors and turbines). Generic PIs may be more suitable for comparisons across different application types turbines). Gener (for one or multiple AI techniques), while application-specific PIs will be more suitable for comparisons within the same application type. Physical measurements Questionnaire Expert consultation Market data Public/private databases Literature Engineering estimates

Operational risk indicators

Figure 8.28. Implications of methodologies and performance indicators with data requirements.

326  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence Indicator evaluation and impact assessment Different economic indicators can be evaluated by techno economic analysis to quantify technical and economic feasibility based on process data. Such data are also needed by LCA. A major barrier to conducting LCA for AI applications is data availability. How to leverage those methods to fill the data gaps for AI evaluation, especially leveraging state-of-the-art machine learning and data analytics, is a research question to be explored. Process simulation is a powerful method to provide the process data needed by techno economic analysis (TEA) and LCA (Demirel 2018, 2021). Process simulation uses mathematical models to represent physical/chemical transformation processes in unit operations. Process simulation requires detailed process information such as operating conditions and stream properties. Energy and exergy analyses are included in the framework. Energy analysis relies on the first law of thermodynamics, while exergy analysis relies on the second law of thermodynamics. Both analyses provide energy-related PIs, but exergy analysis is increasingly popular given its ability to analyze the energy quality and irreversibility aspects of thermodynamic processes. However, exergy analysis may require more data including the temperature profile of the environment. Therefore, the selection between energy and exergy analysis needs careful consideration of the analysis scope and data availability (Demirel 2021). The environmental impacts and safety of products such as toxicity are highly related to chemical structures. A large variety of first-principle or empirical methods can estimate different physical/chemical properties and are useful to assess the impacts of AI applications related to the design of chemical synthesis. Failure analysis methods such as fault tree analysis can be used to evaluate risk-related PIs. Data envelopment analysis (DEA) is a nonparametric linear programming technique for evaluating the relative efficiency and productivity of decision-making units and can be used to evaluate human/labor productivity, eco-efficiency, and economic efficiency in other industries. With available process data DEA could be used to evaluate the impacts of AI on PIs related to humans, the environment, and economics (Sharma 2021, Liao et al. 2022). The evolution of automation involves the development of AI, which can work synergistically with humans and nature to analyze situations and respond sensitively to real-time data. AI systems and sustainable development are interconnected (Leal et al. 2022). Assessing the impacts of AI requires an understanding of how AI interacts with systems that generate economic, environmental, and social impacts. Such an assessment is still limited. Most pertinent studies have focused on the economic, energy, and safety aspects (Liao et al. 2022). A practical application of the framework requires real-world case studies with sufficient process information, which may be challenging due to the confidentiality of most AI projects in industry. Industrial ecology methods and LCA may be powerful tools, which is a promising direction to broaden the applications of industrial ecology approaches and address the data challenges of assessing AI’s impacts. More AI case studies are needed to understand which AI technique should be used to support what specific industrial ecology practice (Ahmad et al. 2021, Leal et al. 2022, Liao et al. 2022, Sullivan et al. 2018). As more and more renewable energy sources come online, the need for speed and agility to produce electrical power grows. Changes in weather sometimes happen quickly. A sudden decrease in wind or sun can mean traditional power generation must respond just as fast. This is where aeroderivative turbines bridge the gap to their more powerful counterparts. For example, an aeroderivative turbine can go from start up to full power in as little as 5 minutes. By comparison, a typical medium-speed reciprocating engine can have a ramp rate of about 5 MW/min, whereas the aeroderivative is around 50 MW/min. That is possible because aeroderivative turbines are essentially grounded jet engines that have been reconfigured to run on natural gas. They can be mounted on a trailer and quickly employed and connected to the grid. This makes them a good choice for on-demand power requirements when renewable generation varies (Saker 2022). The operability window of any turbine is bounded by emissions, lean blowouts, acoustics, fuel variations and drifts in fuel valve calibration. Gas turbines tuned on a particular day may go out

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of their operability window as ambient conditions and seasons change. This can require multiple seasonal tunings to bring the turbine back into emissions compliance. Additionally, fuel variations can impact machine performance relative to its operability boundaries. Finally, load and mode combination vary with ambient conditions and fuel composition and with the control settings provided by the tuning engineer. In short, unlike a broken watch that is correct twice a day, an aeroderivative turbine is only efficient for the exact conditions in which it was tuned. Out of 365 days a year, this can mean only a handful of days for optimal performance (Saker 2022). Artificial intelligence and machine learning for maximum efficiency Just as aeroderivative turbines serve a vital role in the energy transition, software is critical to ensuring those turbines run as efficiently as possible. Artificial intelligence and ML can be used to continually find the ideal flame temperatures and fuel splits for optimal combustion. By sensing changes in ambient temperature, gas fuel properties and degradation, a program can send real-time adjustments to the turbine controls. As Figure 8.29 shows, by applying building after learning, AI/ML can be fully bound by the controls system safety-critical programming to ensure no harm comes to the turbine (Saker 2022). Autonomous tuning works Autonomous tuning has two modes of operation: learning and control. These modes are linked by a 64 human-supervised model building step (Figure 8.29). The modes of operation are sequential. The learning mode must be executed first to map the controlofsettings provided the tuning engineer. in In learning short, unlike watchtothat is a neural space operation of the by turbine. Data collected modea isbroken then used build network model of the turbine’s behavior. Once the models have passed quality checks, they are used days a year,loop this to canadjust meanthe only a handful of days for optimal (Saker 2022).The goal is in a closed turbine’s flame temperatures to performance ensure optimal behavior. to allow for tracking of the turbine’s “sweet spot” (operational conditions with low acoustics and Artificial intelligence and machine learning for maximum efficiencyfuel properties, or physical low emissions) in response to changes in environmental conditions, degradation, and to reduce the need for seasonal retuning. Table 8.5 shows autonomous tuning solutions for various challenges. Customers are already reaping noteworthy savings from the application autonomous tuning. Some examples follow: programming to ensure harm controls • 0.5%system to 1% safety-critical reduction in fuel consumption and COno 2 emissions. • Up to 14% reduction in CO emissions. Autonomous tuning works Autonomous • Up to 12% reduction in NO x emissions. tuning has two modes of model or building step downtime. (Figure 8.29). human-supervised • Zero manual tunings associated Building-Build machine learning model with learning data

Learning-Define the scope of operation

Figure 8.29.  Autonomous tuning steps.

.

Controlling-Find optimal operating conditions

328  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence Table 8.5.  Autonomous tuning solutions for various challenges. Challenge

Autonomous Tuning Solution

Degradation, high combustion dynamics

Maintaining combustion dynamics within limits

Increased emission due to combustion instability

Improved robustness against energy supply changes

Maintaining emission compliance under changing conditions

Maintain emissions of GHGs at or below set points

Maintaining earning and profitability under fluctuating market dynamics and energy costs

Heating rate optimization with energy utilization savings

Unexpected events preventing production

Substantial reduction on manual tuning events

Artificial intelligence, machine learning and energy transition For energy producers to achieve decarbonization, every tool in the toolbox must be applied. Aeroderivative turbines are a part of the solution to bringing more renewables online while satisfying the need for power, which at times can seem insatiable. But without digital solutions to ensure aeroderivative turbines are continually optimized for lower emissions and fuel consumption, they cannot fully contribute to the energy transition. Digital solutions are no longer optional. The energy transition demands we employ every measure for efficiency. This is good news for power generators. Lower emissions and fuel consumption from AI and ML may optimize operation and maintenance, as well contribute to sustainability. A 30 percent improvement in efficiency would qualify as a PI change. The main target for sustainability includes the improvement of energy efficiency and environmental stewardship, and optimizing the value chain. This shows that sustainable options align well with profitability by reducing production waste and the ability to seek out energy sources with reduced carbon footprints (Demirel 2018, 2021, Saker 2022). Sustainability metrics are easily quantifiable with digital solutions. Many digital tools can minimize GHG emissions. AI can help companies meet sustainability goals, generate better plant data, reveal predictive maintenance needs to avoid hazards or shutdown, and decrease GHG emissions. AI deep learning allows for greater optimization of advanced process control, increasing efficiency and production output (Beck 2020b,d).

8.7  Case Studies Constrained energy and sustainability require designs that can satisfy product specifications, while being safe and environmentally benign. Such designs use renewable material and energy, while understanding and controlling the interactions among feedstocks, process equipment, utilities, and chemical reactions. This can lead to optimized, safer, and more efficient startups and operations, with less safety incidents. The following case studies are some examples of successful applications of AI for enhanced sustainability (Pendyala 2021). Pump monitoring Instead of a reactive maintenance schedule, a preventative maintenance schedule can reduce unnecessary downtime and loss of production. Some measurements taken and analyzed for maintenance are vibration, flow, temperature, and valve placement. This approach is helpful for the life of the pump after installation. The conditional approach not only saves money but also protects employees and the environment from unnecessary hazards. Technicians used to perform condition monitoring on each pump using a handheld device and uploading the information to a database for later analysis. Sensors are now taking the place of these monthly rounds and doing so on a more frequent basis. These sensors work with advanced hardware technologies and have better data transfer, including cloud computing capabilities with integrated analytical software running continuously. This allows for remote monitoring from anywhere and even analysis of the asset based on multiple conditions for an even better idea of maintenance needs (LePree 2020).

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Preventive maintenance With reactive maintenance, a component in a plant would break and technicians would fix it. However, this maintenance causes unnecessary downtime and decreased profit. The future of maintenance is likely to be a combination of preventative and predictive methodologies as more diagnostic tools become available. Some technologies will still have a preventative maintenance schedule which improves asset condition, while predictive maintenance will decrease equipment downtime (LePree 2018). AI algorithms sift through data to predict which problems are most likely to arise and what they should do about it. Predictive maintenance may include monitoring of rotating machinery such as pumps through vibration and temperature measurement in real-time and are used to detect malfunctions. Collected data can be used to plan maintenance schedules, helping to reduce overall downtime and significantly improve a company’s ability to meet production targets. Innovations leading to predictive maintenance are the IIoT and AI. Predictive maintenance requires one to acquire, accumulate, and assess data. Acquiring the data requires the use of sensors and a communication system. Accumulation of data may be carried out by a database already in the control system or a wireless cloud system. Assess is the analysis of the data to make decisions. Initially, working process models are provided for correct operation. Next, the generated data in an automated process compare process models and operate through ML, so the system can detect and know the differences between normal and abnormal operation. Another use of this information would be to create a network that involves all parties in the equipment’s life cycle to view what is called a digital twin promoting better communication between all departments concerning the particular asset. Predictive maintenance is comprehensive and works with the process, individual components, and analytics to make informed decisions (LePree 2018). Advanced analytics and prescriptive maintenance technology is helping mining companies to identify impending equipment and process failures. Machine learning algorithms are analyzing data so they can provide alerts to impending failures, in advance. For example, one of the world’s largest fully integrated zinc and lead smelting and refining complexes has used autonomous agents created through ML to predict the failure of a process-critical pump and avoided considerable cost. Applying ML to the operational data of specific equipment can potentially eliminate unplanned downtime, enabling mining companies to schedule more efficient and sustainable maintenance (LePree 2018, Custeau 2019). Predictive analysis Equipment failures and process disruptions are creating unplanned downtime in manufacturing. With elevated insights, managers can make the hard decisions on:

• • • • •

Minimizing risk. Moving forward with major capital projects. Reducing the capital investment required to meet the production targets. Improving plant availability and/or utilization. Facilitating throughput for various design or operational scenarios.

Predictive analytics are essential to the future of safety, sustainability, and productivity. One example is the use of predictive analytics generated through Industrial AI, which can substantially reduce unplanned “flaring.” Early prediction of process deviations helps avoid product quality issues and mitigate unplanned downtime. Consider a refinery that experiences an unexpected power outage and emits extensive sulfur dioxide. Predictive maintenance incorporates machine learning that can detect issues and find the optimal time to take the machine offline to perform the maintenance within the scope of production, maintenance, supply chain and process engineering. This leads to increased production and safety, lower maintenance costs and emissions, and increased

330  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence asset life. One can achieve sustainability by replacing unplanned downtime with planned downtime (Golightly 2019). Wildfires Wildfires are increasingly becoming natural disasters. Utility operators are exploring to avoid wildfires that may be caused by power grids, which are threatened by changing weather patterns. Sometimes, power lines pose a risk, as dry and windy conditions can cause fires. Utilities are looking to manage the grid more effectively during wildfires. One possible way to limit the need for public safety power shutoffs is to use data effectively and to replace manual and in-person line inspections. Using IoT, sensors, imaging, light detection, and weather predictions with grids help operators identify disturbances, analyze activity in the grid, and receive real-time alerts. This can prevent potential sparks or flames by taking timely action to mitigate the risk and protect employees and consumers. Therefore heavy-asset industries are investing in data management for analyzing and converting knowledge, with the technology developing and deployed consistently (Laborie 2021). Materials discovery with artificial intelligence An AI algorithm identified a potentially useful new material called a germanium-antimonytellurium alloy (Ge4Sb6Te7) that is optimized for phase-change applications in data storage and photonic-switching devices. The algorithm contains knowledge related to previous simulations and laboratory experiments, equipment operation and physical concepts, such as phase mapping, or the behavior of atomic arrangement with changing chemical composition. Tasked with evaluating 177 different materials, the AI algorithm performed 19 experimental cycles over ten hours, representing a nearly tenfold reduction in the time required compared to a scientist running the experiments in a laboratory. This self-learning AI accesses and processes data from a combinatorial library of material compositions, using prediction and uncertainty to determine which experiments to run next. It then facilitates the experimentation procedures, such as x-ray diffraction, and collects the data. At this point, the algorithm can request additional information, such as data on a material’s crystal structure, before running the next experiment (Pendyala and Morse 2020, Bailey 2021). Digital manufacturing sector Lean manufacturing and six sigma analysis can also reduce cost and waste. Building simulation models of key areas of a plant can resolve issues of reliability and safety and implement process improvements during startup, shutdowns, and turnaround. A digital journey of the manufacturing sector can help transform businesses and create value for stakeholders. Using the data generated over years of operation, these organizations leverage technology to run their assets in safer, greener, longer, and faster manners. Industry 4.0 technologies blend physical and chemical principles with analytics capabilities of advanced technologies to identify operational excellence. For example, expanding refining operations of petrochemicals and chemical assets is complicated and is constrained by low-carbon initiatives, the need for energy and water efficiency, air quality regulations and climate change restriction. This evolution requires implementing advanced digital technologies for innovative approaches and new corporate strategies (Aroma et al. 2019, Tosic and Zivkovic 2019, Masood and Egger 2019, Sanders et al. 2016). The digital enterprise employs Industry 4.0 and technologies that can be combined with lean management techniques to achieve operational excellence and cost reductions of up to 40%. Technologies are relying on high-performance computing, AI, and analytics to generate insights into operating safely, minimizing environmental impact and ensuring greater reliability and efficiency. Semi-autonomous and autonomous processes created over time collect, aggregate, and condition data and feed it into digital models to evaluate scenarios and gain insight for continuous operational improvements. The optimal scenarios presented to a workforce support the decision-making and set new operating targets when needed. With the incorporation of AI and ML, today’s methods also set the stage for cognitive guidance systems that will empower personnel, extending their capabilities

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so they can make faster and more accurate decisions. An AI enabled workforce applies digital technology to accomplish better productivity throughout an organization, transforming businesses. However, domain expertise combining physics and chemistry is critical for digital technology to be able to optimize successfully and raise operational efficiencies. The physics and chemistry is the “infrastructure” for safe and efficient operations, while AI capabilities act as the enablers of semi-autonomous or autonomous processes. In process industries, the design characteristics and capacity limits of an asset are dictated by the physics, chemistry, and real-world engineering principles of the process (Jabbour et al. 2018, Gholami et al. 2021). Energy industry and digital transformation The energy sector is accelerating investments in digitalization. In the diversified strategy that many companies are following, investing in renewable energy sources, in biofuels, in electric power and natural gas distribution, and in battery storage technologies is proceeding in the short-term while maintaining revenues (Ahmad et al. 2019, Tremblay 2020, Beck 2020d). The sustainability goals in the energy industry are driving companies to consider new and alternative sources to meet the energy needs of the future. Integrated systems and interfaces can allow them to meet demand with feedback throughout the entire system, enabling instant corrective actions to create optimal conditions. This process also helps an organization to coordinate multiple units with higher-level insights on properties and economics. Extending the reach into the improved supply chain allows coordination across multiple manufacturing assets through adaptive value chain optimization, where ML derives insights from real-time information to enable decision-making on demand scenarios or logistical adjustments. System-level thinking, and design enabled by the digital options, can lead to closed-loop execution, and reduce the impact of disturbances (Rietbergen and Blok 2010, Reynolds 2020, 2022, Demirel 2021). The high-performance computing ability of the cloud drives AI applications and empowers engineers to instantly evaluate thousands of design options to find the best return on investment. With knowledge embedded in the assets through IIoT sensors to capture data locally, companies can also leverage the power of shared model components across design and operations, becoming safer, faster, and more agile. The following enabling technologies are driven by digitalization (Tremblay 2020): • Companies will have to focus on the complete life cycle of both the asset and the products produced. • A rising to meet the demand-supply balance requires new research and technology (e.g., to achieve the potential depolymerization of plastics and to drive greater conversion of crude oil to chemicals). • To remain competitive, organizations will need the capabilities from embedded AI and analytics with semi-autonomous or autonomous systems, along with advanced decision-support capabilities to enable greater agility. • New business models will be anchored on engineering principles, physics and chemistry, which have supported so much innovation, enhanced with digital capabilities. The skills required to adopt and sustain these new technologies will demand a focus on organizational excellence to build and sustain them. Circular economy with artificial intelligence As a society’s standard of living increases, so typically does its plastic use. Concerns about managing plastic waste are growing, along with regulations and compliance mandates. This can involve incorporating sustainability targets into business goals, including safety, asset integrity, emissions management, and waste reduction. A circular economy approach can reduce waste and carbon emissions while increasing re-usability (Figure 8.30). Digitalization and AI are key players

-

332  Sustainable Engineering: Process and Intensification, Energy Analysis, Velenturf Purnell 2021, Wilson et al.Artificial 2022). Intelligence

Process

Collect, recycle, reuse

Manufacture •Artificial intelligence

Consume

Figure 8.30.  Circular economy and its elements.

.

in the circular economy with a “full-cycle” approach to processing and conserving resources (Beck 2020a, d, Tremblay 2020). While processes like pyrolysis and gasification allow plants to convert plastics to fuel, they are high-energy processes that often produce harmful emissions. Chemical recycling offers new ways to break down plastics into their original components, eliminating downcycling and feeding a circular economy. A relationship exists between a polymer’s properties and its chemical structure ones of which • to analyzing to determine howrequests. best to adjust batch and continuous and mapping allows a company responddata quickly to customer Depolymerization allows • companies to break down polymers including polyester, nylon, and polystyrene into valuable • be used to produce new polymers. Multivariate analytics enables monomers, which in turn can • plants to monitor complex processes and make real-time adjustments to keep batch processes on • target and to improve consistency and yield (Demirel 2021). Many producers are focusing on developing green polymers based on sustainable fermentation products such as lactic acid and butanediol, which can be derived from agricultural byproducts. Many new products are inherently more biodegradable than traditional synthetic polymers, reducing the accumulation of microplastics in the biosphere. Optimizing the product sequence and the grade transition procedures can improve operating agility and save companies millions of dollars by minimizing the generation of off-spec product. Polymer makers can use the power of the same digital methods to help them streamline product development. and optimize operations to improve sustainability (Tremblay 2020, Velenturf and Purnell 2021, Wilson et al. 2022). Food and beverage industry with AI Food processing on a large scale is particularly complex, with extensive quality control. Considering the focus on food safety, food manufacturers have a challenge in what they produce. Digitalization in food and beverage processing is increasing efficiencies with improved supply chain processes, streamlined production and simplification (Kumar et al. 2021). Nonetheless, various challenges 69 remain, some key ones of which are:

• • • • •

analyzing data to determine how best to adjust batch and continuous processes for all variables reducing off-spec product minimizing product rework needs enabling more proactive schedule changes decreasing lead time for customer orders

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Pharmaceutical and life sciences companies are turning to advanced analytics, AI, and ML for help. Multivariate analytics software can help address process and product quality issues and can help optimize production. This can be done through various measures (Stridiron 2020):

• • • •

increasing equipment reliability and throughput, preventing batch loss, finding the “hidden factories” inside the plant, reducing capital expenditures.

Asset performance management tools based on advanced analytics create manufacturing efficiencies that often deliver strong returns. With IoT-based sensors and machine learning a biopharmaceutical manufacturing organization can put these data to work for advanced capabilities. Manufacturers can avoid unexpected shutdowns, reducing maintenance costs and preventing production losses. Predictive maintenance software captures and analyzes data quickly, while eliminating the need for a specialized workforce in data processing. Multivariate analytics Multivariate analytics software analyzes and continually monitors discrepancies in material properties, variations in procedures, and the impact of them on the final product. These tools can help identify and troubleshoot process and product quality issues. For complex processes, multivariate analytics takes a broad view of many process variables to identify those that are critical to reduce off-spec production and lower waste. Some examples of real-world successes from the application of multivariate analytics follow (Sean et al. 2014, Ferdyn-Grygierek and Grygierek 2017): • Oil companies are using steam-assisted gravity drainage steam injection for production to lower CO2 emissions by 4% and reduce water consumption by 20%, while increasing overall production. • An oil company reduced energy demand considerably and optimized the sourcing of energy between available utilities to increase energy efficiency and reduce its carbon footprint. • A petrochemical company used advanced process control to lower the energy consumption of an ethylene unit by 20%. Safety and reliability With the use of digital technologies, engineers can ensure designs comply with industry safety standards. For example, integrated system analysis can generate comprehensive safety plans for various devices across a plant, including pressure relief and safety valves, and flare systems. Monitoring technologies can help optimize unit operations to maintain them within safety limits and to issue alerts about equipment failure that can lead to unexpected incidents. The same models can be useful for preparing new operators to manage unexpected process disruptions that often lead to safety incidents. Operator training systems using digital twins of current and future operations are becoming the standard training practice across the manufacturing industry (Badri et al. 2018, Beck 2020b, Sharma 2021). Safety and reliability opportunities of using digital technologies include the following: • Predicting potential incidents: Use AI-enabled insights to recognize patterns in operating data and obtain warnings of possible breakdowns and associated emissions and hazards. • Optimizing for asset integrity: Simulate processes to avoid runaway reactions and build in safety with in-depth analysis and design of flare and pressure-relief systems. • Preparing operators: Simulate operations to train for unexpected conditions and avoid possible hazards.

334  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence Some resulting safety and reliability outcomes of applying digital technologies follow: • Companies used dynamic modeling of liquefied natural gas operations to enable faster startup while ensuring critical safety standards are met. • Chemical companies assessed potential sulfuric acid hazards with dynamic simulation. • In a polymer process, 27 days of warning for a central valve failure avoided an unplanned shutdown and probable emissions release. The focus was primarily on emissions of CO2 and NOx. Decarbonization and artificial intelligence Digital solutions help decarbonization by modeling and comparing alternative processes for various metrics, such as costs and GHG emissions for the delivery of energy. Modeling can screen alternative energy sources, while accounting for associated emissions and resource demand for each (IEA 2019, Demirel 2021). For decarbonization methods, energy analysis enables better heat integration to reduce utility use and planning for new plants and consequently reductions in GHG emissions. The following examples show how companies can reduce energy consumption and GHG emissions: • A company used a DT of existing oil and gas operations to capture efficiency opportunities and cut GHG emissions and water use each by 10% and energy use by 5%. • Modeling helps the emerging-market growth area of carbon capture in power and industrial facilities. The integration of sustainability targets with business goals can transform the energy and manufacturing sectors, as well as businesses across industries. Digital technologies are expected to play a critical role during the energy transition. ARC reports (Reynolds 2020, 2022) state that AI capabilities improve operational safety and decarbonization, support a circular economy, lead to adoption of cleaner energy sources, and reduce waste and water usage. Digital transformation through expanded use of sensors, data, and analytics enhances process control and maintenance in sustainable manufacturing. DTs help improve sustainability by:

• • • • • •

Optimizing supply chains Advancing process control Optimizing energy and utility production and usage Enhancing wearable and sensing technology Predicting and prescribing maintenance Creating digital twins

Sustainability initiatives of global energy sectors mainly focus on sources with lower carbon content and renewable energy use, while the manufacturing sector focuses on developing products with a low carbon footprint and extensive use of renewable feedstocks that can support a circular economy. For the manufacturing sector, improving operational safety is another focus point that is a part of a license to operate. For both the energy and manufacturing sectors, digital transformation has become an important enabler for sustainability. Regulatory pressures, customer pressures, and pressure from the investment community are forcing the manufacturing sector to undertake initiatives so as to move toward sustainability. With transparent and forward management initiatives, education, and societal cultural change, sustainability efforts may succeed if the manufacturing sector allocates adequate capital expenditures and other resources. One of the early indicators may be the reduced imbalance between supply and demand. Examples: • Refining reactor models for catalyst degradation and life extension can create considerable economic value by extending catalyst life and improving yields/performance.

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• Many online applications for units can advise operators to improve operational yields, address performance and safety problems and improve compliance. For example, a heat exchanger train model can provide a fouling and cleaning schedule for considerable economic gain. Energy policy with targets Industrial energy policies set quantitative targets to reduce energy use and greenhouse gas emissions. The taxonomy includes volume reduction targets, physical efficiency improvement targets, economic intensity improvement targets and economic targets. A policy design process determines the fundamental targets in concert with quality objectives. In addition, policy makers decide upon the concrete policy strategies consisting of the main activities with measures. Quality objectives can be worked out through several strategies, including energy conservation and promotion of renewable energy. For example, a national target may be set to improve energy efficiency by 5% on an annual basis and to reduce CO2 emissions by 3% in a period. The strategies include policies and measures stimulating energy conservation (Rietbergen and Blok 2010, Demirel 2021). Retrosynthesis and artificial intelligence In retrosynthesis, a synthesis starting from a precursor molecule is devised by working backward from the target compound, Therefore, retrosynthesis is important for bringing effective products to market. It is a challenge to identify the chemical processes required to synthesize a compound. Several computer-supported retrosynthesis resources are available to help chemists identify synthetic pathways that can save time. Within any given synthesis route proposed by retrosynthesis software, users can analyze the reaction step details including the structures of starting and ending products, the existence of any similar reaction chemistries, and the possibility of side reactions. Software programs may rely on artificial intelligence to enhance retrosynthesis planning. For example, in ML, computer algorithms improve as they accrue experience by analyzing data sets. A rule-based AI system mimics the reasoning of a human expert solving a problem; its knowledge comes from a set of rules telling the software what to do or what to conclude in different situations. As technology has advanced, AI has been applied to more complex problems, including chemical synthesis. With retrosynthesis software, new chemistries may be discovered quickly and can lead to novel chemical process developments (Peiretti and Brunel 2018). Energy companies with digital capabilities and virtual and remote workforces can focus on sustainability, including decarbonization and a circular economy. This can drive refineries to transition their operations to incorporate chemicals and to shift away from conventional transportation fuels. Companies are looking at a range of efforts for increased productivity, such as the following (Beck 2021b):

• Balanced OPEX and CAPEX for an agile business. • An effective and capable workforce for long-term and connected remote working. • Limited turndown to keep plant operations safe. • Optimum supply chains, with appropriate business and economic tradeoffs. • Using the newest technology.

Agility and flexibility are key in the industrial sector. Digitalization is a tactical tool for achieving more agility and flexibility, in part because digitalization enables companies to be effective with technology on-site or via remote workers. For example, Digital twins DTs can be implemented in the manufacturing sector for safe operations, agility in the face of uncertainty, and sustainability. Unexpected equipment failures, environmental releases, and spills cause disruptions, extended downtime, equipment integrity problems and corrosion. Business must be sufficiently agile that they can respond adequately to changing conditions. Extended asset downtime can cause significant loss in revenue and disrupt supply chains of manufacturing industries. Manufacturing sectors are increasingly using digital tools to improve

336  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence safety, meet production targets, and eliminate unplanned downtime. Some major challenges in the manufacturing sector are the workforce skill gap for digital technologies, maintenance, safety, as well as supply chain reliability (Hahn 2020, Sharma 2021). Digitalization, industrial artificial intelligence and sustainability AI solutions are built for-purpose. As the industrial sector focuses increasingly on sustainability, digitalization is emerging as a key enabler. Also, the International Energy Agency (IEA 2019) recommends that digital approaches can help increase energy efficiency as much as 30% for industrial operations. In addition, digitalization is a key tool to meet sustainability goals in the chemicals sector. To remain competitive, companies must work to grow the “triple bottom line” by investing in alternative energy sources and implementing the circular economy (Varela et al. 2019, Beck 2020a). The integration of sustainability targets with business goals will be transformational for the manufacturing sector. AI enables businesses to convert data to information to drive higher productivity and safer operations by combining domain expertise and first principles-based models to deliver purpose-oriented solutions. Embedding AI in process models lead to more efficient production options that use less energy and resources, as well as enable comparisons of process options. Deep-learning advanced process control helps companies apply the optimization of production and business. Energy and utility optimization uses process modeling and simulation technologies with opportunities for heat integration across assets and consider alternative lower-carbon energy sources. AI-based technology learns from existing design and operations data and then integrates process knowledge to deliver prescriptive maintenance solutions. Digital twin technology uses real-time data to provide an evolving digital profile of the past, current, and future behavior of an asset or process. The connected workforce can gain insight, optimize operations, predict performance of assets, and achieve the best possible performance while reducing energy consumption (Kamble et al. 2018, Titmarsh et al. 2020, Costa et al. 2020). Digital solutions Organizations find that profitability and sustainability are interrelated. Restructuring of current business models requires digital solutions as a key enabler for sustainability objectives, largely because digital tools targets corporate sustainability, mainly focusing on energy efficiency, decarbonization, and value chain optimization. The manufacturing sector needs robust training to ensure a qualified workforce capable of understanding and implementing digital technology. Digitalization solutions provide the analysis, insight, and process optimization needed to address sustainability objectives. Success depends on harnessing available data and creating and empowering the workforce to make the timely decisions. In addition, digital solutions can assess progress on sustainability goals, for example, tracking and optimizing GHG emissions (Kamble et al. 2018). Continuous flow chemistry The inherent advantages of continuous flow chemistry have far-reaching implications for improving pharmaceutical manufacturing pertaining to product quality and sustainability (Akwi and Watts 2018): • Light is energy efficient, nonhazardous, and environmentally friendly. • Light is sufficiently energetic to enable reactions without extreme conditions as is often required with thermal activation. • Catalysts are used, avoiding stochiometric quantities of reagents. • Highly reactive molecules are formed under milder conditions for safer reactions and reduced waste.

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• Poorly reactive moieties are more easily activated. • Overall advantages include shorter synthetic routes, greater atom economy, and use of renewable feedstocks. Sustainable catalysis The U.S. Environmental Protection Agency states that a principle of green chemistry is the use of catalysts in place of stoichiometric reagents. Waste is minimized since only small amounts of catalyst are required and are used repeatedly in a reaction cycle. One key objective in these areas is the development of first-row transition metal compounds that replace precious metals in existing reactions or potentially even mediate entirely new reactions, which are not yet accessible. For example, in situ FTIR spectroscopy can be used to provide kinetic information enabling the calculation of turnover frequency and insight into the reaction mechanism. Another example is a Zn-based binary catalyst that employs various ionic liquids to synthesize cyclic carbonates from urea and diols. Through in situ FTIR studies, it was found that the controlling step in of the synthesis of ethylene carbonate involves the elimination of ammonia molecules from the intermediates. This observation provided key mechanistic insight into effectively synthesizing cyclic carbonates from urea and diols via a more sustainable pathway (Mazheika et al. 2022). Biocatalysis Biocatalysis has emerged as one of the most important methods for greener synthesis by enabling reactions under far milder conditions than chemical catalysts. Biocatalysts minimize the issue of waste toxicity and cost of metal catalysts, and reduce energy requirements associated with chemical reactions. Additionally, enzymatic biocatalysts often have rapid kinetics and can be chemo- and enantiospecific (Demirel and Gerbaud 2019). Intensification using membrane separators and reactors In a system involving gas-liquid, liquid-liquid, and liquid-solid interactions, where usually mass transfer resistances are high, a membrane contactor can be used to maximize the mass transfer rate without dispersion of one phase into the other. Most membrane processes are driven by pressure difference that require less energy compared to thermal processes, making the overall processes highly energy efficient. The membrane is characterized by high level of compactness, ability to address thermodynamic limitations, and high contact area owing to drastic reduction in the size of the unit. However, this last point usually comes at the expense of generally high membrane cost. This technology has been employed for carbon capture in photochemical electrochemical, and thermochemical CO2 conversion processes, and is aimed at overcoming mass transfer resistance and enhancing energy efficiency (Adamu et al. 2020). Intensification of the CO2 stripper by reducing the energy penalty The intensification of carbon capture and utilization technologies have focused on developments relating to photochemical, electrochemical, thermochemical, and biochemical routes. In photochemical process intensification, microreactors, monolith reactors and development of novel materials, such as graphitic carbon nitride, are approaches being investigated to intensify photocatalytic CO2 reduction. Gas-diffusion electrodes, ion exchange membranes, and microfluidic devices, as well as the development of highly stable electrocatalysts, are leading the way in improving Faradaic efficiency, current density, and selectivity in electrochemical CO2 conversion. Cold plasma, used for catalyst activation in thermochemical CO2 conversion, can reduce the costly thermal energy sources. The development of bio composite structures applied to intensified reactor technologies offers one promising pathway of intensifying CO2 capture and potentially conversion via biochemical routes (Demirel 2018, Adamu et al. 2020, Beck 2020b).

338  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence Advancements in coating technologies Advancements in coating technologies are helping the industry move toward more sustainable business opportunities. The advancements have come after various restrictions on the emissions of volatile organic compounds. Some base coats on plastics can suffer from loose adhesion due to the high surface energy of water. Sustainable coatings depend on the effectiveness of total solutions. Waterborne and solvent borne paints and coatings have different physical properties and a multi-step process that can assess the raw materials and application requirements for specific paints and coatings is being used (Sloan et al. 2020). Developing hydrogen technology Developing hydrogen as an energy carrier involves the complete value chain from production to end-use with reliable operation at scale. Digital technology is helping in delivering the hydrogen economy, accelerating and reducing the risk in adopting innovation and enabling faster and better scale-up and optimization of the hydrogen value chain. Digital technology can maximize commercialization, design, and supply chains, which leads to sustainable production and economics. Innovations in asset optimization software can improve design, operations, supply chain and maintenance challenges. Figure 8.31 shows the digital technology solutions addressing the hydrogen value chain by incorporating modeling of hydrogen production and carbon capture processes, and by risk and availability assessment across the value chain, incorporating stochastic modeling and asset health monitoring (Demirel 2018, Beck 2021a). • Accelerate transport and storage of hydrogen • Improve conversion process • Consider alternatives • Improve fuel cell

• Need to scale • Operate complex plants with capable workforce • Process modeling and collaborations

Accererate hydrogen production and innovation

Blue hydrogen and ammonia production optimization

Optimum design and operation

Optimize hydrogen value chain

• Optimize energy usage • Optimize production • Carbon capture • Use digital twin, energy modeling, advance process control

• Built right value chain • Optimize and integrate with natural gas network • System risk modeling and planning and scheduling

Figure 8.31.  Digital technology impact on a hydrogen economy.

Digital solutions for hydrogen economy Innovation, scale-up, and advanced operations are necessary to advance the hydrogen economy by decreasing the cost of hydrogen, evaluating and optimizing many value chain alternatives, and * removing constraints and safety issues (Demirel 2018, 2021, Beck 2021a). Digital technologies can expedite the transition to hydrogen, impacting key functional areas (https://www.aspentech.com/ en/resources/white-papers/accelerating-the-hydrogen-economy-through-digitalization). This can be • done in various ways: • • process simulation software can represent hydrogen electrolysis and hydrogen • Rigorous • reformer processes, • Innovative approaches to hydrogen synthesis, hydrogen liquefaction and pipeline transport are accelerating commercialization and improving access to capital.

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Several specific digital technology opportunities to accelerate innovation exist. These include (Figure 8.31): • Hybrid models incorporating AI together with first principles models for new processes, including membrane technology, combining reforming, and carbon capture. • Rate-based separation systems simulation modeling for carbon capture. • Powerful, rigorous models for electrochemistry. • High-performance computing for evaluating alternative process. • Integrated economics for screen technoeconomic alternatives during concept design and pilot plant testing. • Integrating collaborative engineering workflows with cross-functional engineering teams. • Facilitating advanced, integrated supply chain planning, along with integrating the hydrogen economy value chain with existing natural gas and power networks. • Evaluate hydrogen production, transportation, and storage, as well as the risks to achieve reliable energy goals. Electrolysis and fuel cells, handling dynamics and considering stochastic variation are all important. • Advanced modeling and digital twin solutions can play a major role in hydrogen generation research and development, from electrolysis and steam reforming to carbon capture and fuel cells. • Automating processes in electrolysis, carbon capture, crude to chemicals processes and industrial scale fuel cells can accelerate a shift to a hydrogen economy. Hydrogen challenges Central challenges for green and blue hydrogen production include the identification and rapid evaluation of the highest efficiency electrolysis and/or membrane conversion approaches, evaluations of economics of catalyst and adsorbent options, and improvement of economics based on highest cost choke points. To accelerate hydrogen production while achieving favorable economics, industry will need to focus on several areas, including: • conversion efficiency, process optimization and scale-up of hydrogen production, including measures that will drive down production costs. • development of low-cost and efficient hydrogen distribution and storage value chains, including evaluations of carrier alternatives such as ammonia. • implementation of low-risk, high-reliability, and cost-competitive hydrogen technology. Transitions to hydrogen in industry needs asset optimization software that extends across the entire value chain, addressing the key areas of production, distribution and storage and usage (Table 8.6). Within the framework outlined, there are several key alternatives. These include: • Distributed hydrogen production modules versus large centralized production. • A phased approach, leveraging both blue and green hydrogen options initially and migrating to green hydrogen as economics improve over time and technical risk diminishes. Table 8.6.  Challenges and benefits of energy transition to a hydrogen economy (Beck 2021a). Path to net-zero carbon Challenges with production, Improve timely deployment storage, and distribution Optimize process and cost efficiencies of hydrogen Operate innovative processes effectively

Benefits Accelerated cost analysis Improved energy efficient process Maximized uptime and safety with minimized risk

340  Sustainable application Engineering: Process Intensification, Energy Analysis, Artificial Intelligence levels • Research and development to drive down the cost of fuel cells for end-use and electrolysis for production, and to reduce risk in transport and storage. • A focus on larger scale, concentrated end-uses such as power generation and grid storage in the shorter term. Industrial AI‑poweredEnergy workflow Artificial intelligence can identify strategies for implementing and debottlenecking hydrogen distribution supply chains. Advanced control and optimization technology will be important in the control and reliability of hydrogen production and carbon capture. Many refiners use adaptive process control on their existing hydrogen plants and are∆leading the implementation of dynamic optimization, which has significantly reduced energy use, hydrogen loss and flaring at sites (Elmendorp 2022). Integrated supply chain Time indicators The hydrogen economy will require an evolutionary approach for migrating existing energy distribution supply chains into ones that can handle grey, blue, and green hydrogen. Today’s advanced planning, scheduling and supply chain tools provide energy companies a unified platform to handle the end-to-end supply chain across businesses in a unique way. Additionally, enterprise 8.27. Major typeswill of process intensifications broken down by impact category. riskFigure modeling systems be play a key role in understanding success factors in supply chain implementation (Figure 8.32). • Separation efficiency in operation • Improved capture process • Reporting carbon capture and accountability

• Apply advanced control for energy efficiency • Optimize energy efficiency

Digital twins Online process simulation

Control and optimizion Production optimization

Process intensification and innovation Process and hybrid modeling Economics and risk Process modeling and feasibility analysis

• Optimize carbon capture • Scale up capture process • Detect best carbon capture technology • Carbon capture process and equipment design • Feasibility analysis

• Digitally evaluate the scale up, economics, and risk

Figure 8.32.  Digital solutions for affordable and sustainable carbon capture.

Beyond hydrogen The energy industry needs to drive to net-zero carbon, macroeconomics impacting global demand for hydrocarbons, and an energy transition that is gaining momentum and building demand for renewable electricity and zero-carbon mobility options. This may need to shift very large capital investments into low carbon sources (Figure 8.33). Carbon capture technology needs innovation in solvent chemistry, column design and optimization for energy efficiency, carbon capture efficiency, and cost reduction. Advanced process modeling can be applied for improving economics, ensuring operational integrity, and energy optimization and improvement. Companies need to apply rate-based modeling, which is the most rigorous, accurate and efficient method for modeling processes, e.g., solvent-based carbon capture processes. Additionally, custom unit modeling as well as AI-based hybrid models can be used to model the advanced membrane technologies currently being tested for carbon capture (Beck 2021a).

Power from renewables Oils to chemicals production and integration Energy efficiency indicators Biofuels Innovative energy storage Decarbonization Hydrogen economy

Circular economy

Sustainability tracking Energy and water efficiency Waste management Process intensification

Energy transition

Resource efficiency

Artificial Intelligence  341 Plastic recycle and material re-use Bio-based feedstock Innovative material, process, and product development Carbon dioxide to chemicals and materials

Figure 8.33.  Sustainability value creation.

Summary Industrial Artificial Intelligence (AI) is a systematic, collaborative, and integrative discipline focusing on developing, embedding, and deploying various purpose-oriented machine learning algorithms and domain‑specific industrial applications to industries. This often includes a focus on adding sustainable business value to capital-intensive process industries. Industrial AI combines the fundamentals of engineering and science with AI capabilities and purpose-built software leading to the self-optimizing plant. Industrial AI enables companies to solve complex problems more easily; increase value creation with higher-quality data, greater accuracy, more speed and improved access to enterprise data; automate and simplify the creation and sustaining of industry models and reduce the total cost of ownership. This chapter provides an understanding of AI in general, through coverage of such topics as industrial Artificial Intelligence, information System Engineering, digital industry platforms, and cybersecurity. Descriptions are also included of the three elements for industrial AI: data science and AI, software at scale, and domain expertise. Coverage is also provided of topics that link sustainability and artificial Intelligence, including AI and process intensification as well as AI and sustainable engineering. Finally, case studies are provided to illustrate and demonstrate AI and its applications (Varela et al. 2019, Pendyala 2020d). The bottom line of this chapter is that AI can play a significant role in shifting society and industry towards sustainability in general and in achieving the 17 UN Sustainable Development Goals in particular, across the areas of the environmental, economic, and societal sustainability. Put another way, AI can help in moving society towards sustainable development wherein the needs of the current generations are met while not impeding the ability of future generations to meet their needs. Interest in corporate social responsibility has led to the adoption by many companies of a focus on social, environmental, and financial information so as to satisfy stakeholder needs and move toward sustainability. This approach can incorporate feedback from outputs, in terms of reuse, recycle, and remanufacturing, to support sustainable development. As AI may notably impact the future of humanity and its development in a sustainable manner, it is important that regulatory bodies be one of the stakeholders and helps oversee the development of AI.

Nomenclature AI ANN APC API APT CAPEX DDoS DEA DM

Artificial intelligence Artificial neural network Advanced process control Advanced programming interface Advanced persistent threat Capital expenses Distributed denial-of-service Data envelopment analysis Digital master

342  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence DS DT EIA ELN ES FL GDPR GHG GLSS IAM ICT IIoT IoT IT KPI LCA ML NLP OPEX OT PAT PI PII PSM SAI SCADA SDG SIEM SNMP TBL TEA

Digital shadow Digital twin Energy Information Administration Electronic lab notebook Expert system Fuzzy logic General Data Protection Regulation Greenhouse gas Green lean six sigma Identity and access management Information and communication technology Industrial internet of things Internet of things Input technology; information technology Key performance indicator Life cycle assessment Machine learning Natural Language Processing Operational expenses Output technology Process analytical technology Process intensification Personally identifiable information Process safety management Sustainable artificial intelligence Supervisory control and data acquisition Sustainable Development Goal Security information and event management Simple network management protocol Triple bottom line Techno-economic analysis

Problems 8.1 Search for a paper on the application of artificial intelligence methods to a specific industry. List and describe the ways in which AI helps identify potential improvements in that industry. 8.2 Artificial intelligence is applied by some engineers, sometimes to a great extent, but only in a limited way or not at all by others. Identify the benefits to using AI. Identify the reasons for the reluctance by some to using AI. 8.3 Apply artificial intelligence to a specific industry that has not been examined previously in the literature using AI. What are the improvements attained by applying AI? Explain them qualitatively and quantitatively. 8.4 Develop ideas on how artificial intelligence concepts and methods can be usefully applied to develop policies for the following industries: (a) manufacturing, (b) iron and steel, (c) chemical and petrochemical processing, and (d) cement. 8.5 Explain how artificial intelligence can be a tool of better management of social media and social media platforms. 8.6 Select an engineering plant. Which areas in the plant could the application of artificial intelligence benefit? Which areas in the plant would not benefit from AI?

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8.7 Using artificial intelligence, design and analyze an improvement to the website of a commercial company that is used for promoting and selling the company’s products. The design of the improvement should involve at least conceptual development and more a detailed design where possible. The analysis of the design improvement should provide information that can be useful in evaluating its merits. In your work, make reasonable and appropriate assumptions and approximations, list data utilized and/or assumed, and use relevant software to assist where appropriate and useful. You may wish to utilize some or all the following steps (or others): • Design the improvement to company’s website using AI. • Identify relevant performance parameters for assessing the improvement and perform an analysis of the design improvement. • Compare the results with those for the original website of a commercial company. • Discuss the results and findings, verify and validate the results, draw conclusions, and make recommendations (if any can be drawn from your results and conclusions). 8.8 What is the impact of corporate culture on the use of AI? 8.9 What is the relationship between corporate culture and AI? 8.10 How does power agility affect energy efficiency?

Research Projects 8.1 Investigate the impact of artificial intelligence on corporate culture in terms of sustainable development. 8.2 Investigate the impact of artificial intelligence on energy transition and decarbonization. 8.3 Investigate the impact of artificial intelligence on society.

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Chapter 9

Workforce in Sustainable Engineering INTRODUCTION and OBJECTIVES Sustainability is the collective willingness and ability of a society to reach or maintain its viability, vitality, and integrity over long periods of time, while allowing other societies to reach or maintain their own viability, vitality, and integrity Kates (2011). A new workforce with special skills is necessary to achieve sustainability. Sustainability education requires the addition of life cycle assessments, green chemistry, a move toward renewable fuels and feedstocks and other relevant topics to the curriculum. This may be possible through a curriculum that is inclusive of sustainability issues at every level of courses in STEM education, as well as with training in energy systems and manufacturing sectors. This will help develop a common language related to the various dimensions of sustainability and resilience. The objectives of the chapter are: 1. 2. 3. 4. 5.

Competency in system thinking in sustainability analysis, Elements/indices of sustainability, Metrics/Indicators of sustainability, Cause-effect chains, and Feasibility analysis with sustainability metrics/indicators.

9.1  Key Competencies and Skills in Sustainability Some key competencies in sustainability (Wiek et al. 2011, Ploum et al. 2018) can enable the workforce to make contributions to resolving challenging problems and developing sustainable engineering. These key competencies are varied and include the following: 1. Systems thinking competence: This is the ability to collectively analyze complex systems across society, environment, and the economy, from local to global scales. Therefore, this competence involves cascading effects, inertia, feedback loops and other systemic features related to sustainability issues. The main objectives for a system thinking competency in sustainability in engineering are to understand sustainability analysis, elements of sustainability, indicators/metrics of sustainability, cause-effect chains, and the use of sustainability analysis in feasibility analyses. The main outcomes of system thinking competence would be to: • Identify/order steps (processes) from input to output in a manufacturing plant, in a block flow diagram. • Identify sustainability indicators/metrics and relate them to sustainability dimensions.

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• Estimate emissions of GHG/toxic materials in designs, to support assessments of environmental impact. • Develop societal impact metrics and safety impact metric designs. • Analyze cause-effect chains of the materials/energy usage in the sustainability of designs and create multi-criteria decision matrixes to permit comprehensive feasibility analyses of designs. 2. Anticipatory competence: This is the ability to collectively analyze, evaluate, and anticipate future issues for sustainability problem-solving frameworks. 3. Normative competence: This is the ability to collectively map, specify, apply, reconcile, and negotiate sustainability values, principles, goals, and targets. It is usually done based on acquired normative knowledge including concepts of justice, equity, social-ecological integrity, and ethics. This competency identifies which practices can be transformed or discarded and which must be maintained to sustain the viability of life-supporting systems. 4. Strategic thinking competence: This encompasses the ability to collectively design and implement interventions, transitions, and transformative strategies aimed at sustainability. It requires an intimate understanding of strategic concepts such as intentionality, systemic inertia, path dependencies, barriers, carriers, alliances, and knowledge about viability, feasibility, effectiveness, efficiency of systemic interventions as well as the potential of unintended consequences. 5. Collaboration competence: This is the ability to motivate, enable, and facilitate collaborative and participatory sustainability research and problem solving. This competency includes advanced skills in communicating and is closely linked to all other competencies in order to create ownership for intermediate results, to leverage implementation, and to build joint capacity to cope with complex sustainability challenges. Collaborative competence leads to using and integrating the five key competencies for solving sustainability problems and fostering sustainable engineering (Wiek et al. 2014). 6. Cultural competence: Equity, diversity, and inclusion (EDI) refer to the importance of cultural diversity for the development of cultural competence among individuals. Infusing EDI in the syllabus may promote an inclusive learning environment and helps establishing goals toward cultural competence. In academic settings, ongoing training in EDI should consult with the campus teaching and learning center, forms faculty learning communities, and reviews the diversity resources for teaching. EDI activities should be a policy priority of institutional action plans and performance reports with a joint and continuous effort toward a more inclusive educational approach (Tamtik and Guenter 2019, Mehta et al. 2020, Fuentes et al. 2020). Competencies need to be translated into specific learning outcomes for curriculum development. Concerted efforts to train faculty in the design of competence-based courses allow adoption across the curricula. Courses designed with sustainability competencies in mind are not necessarily more difficult to create, but there are fixed costs associated with learning about the competencies, how they are operationalized, and how to teach more project and problem-based courses, with learning activities that are coordinated with stakeholder partners. The sustainability engineer with a deep understanding of the system itself knows what is required to operate and manage the system, and what impact it has on such sustainability related factors as the global climate and climate change.

9.1.1  Equity, Diversity, and Inclusion Equity, diversity, and inclusion (EDI) should be integrated within education and training for sustainable engineering. A well established understanding of equity leads to a sustainable society

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with active participation of all members of a community. This leads to numerous benefits including (Fuentes et al. 2020): 1. Social identity towards the collective characteristics for which a person is recognized by others. 2. Ability to decide who will have access to resources. 3. Capacity to influence the behavior of others, oneself, and/or the course of events. Inclusive communities have social identity activity that encourages reflection on social identities and how these identities may be felt or perceived. Further, personal identity activity encourages reflection on how one identifies oneself and is most effective when used in conjunction with the social identity. Considerations of the interactions between identities and context can help strengthen inclusion in a community. Diversity leads to understanding and respecting demographic and philosophical differences of everyone in the community. Wide acceptance, respect, and cooperation may remove prejudices. From the synergy of its people, diversity creates innovative solutions and practices and leads to understanding and to sharing experiences from the interdependence of humanity, cultures, and the natural environment. This helps naturally remove personal, cultural, and institutionalized discrimination for the benefit of communities (Felder et al. 2002, Tamtik and Guenter 2019, Mehta et al. 2020, Fuentes et al. 2020). Equity, diversity and inclusion and sustainable engineering Linking EDI with business practices related to natural resources and human resources may lead to development that ‘meets the needs of the present without compromising the ability of future generations to meet their own needs’. Many resources of the Earth need to be used responsively and integrated with all the three aspects of EDI. Doing so may lead to sustainable engineering that achieves a balance between environmental, economic, and social dimensions (Tomei et al. 2015). Incorporating equity into sustainability assessments of biofuels Equity may be defined in terms of matters of recognition, distributional and procedural justice, as well as a greater focus on equity in assessments of social and environmental dimensions of sustainability. The latter can have notable significance to sustainable development ideals and equity matters. Renewable energy sources can help reduce global social inequalities through the provision of access to energy for all whilst reducing greenhouse gas (GHG) emissions. It is important to consider how sustainability criteria might be strengthened to incorporate equity matters and thus ensure the benefits for some are not outweighed by worsening the economic, environmental, health and social wellbeing of others. However, such information is key to fully integrating equity impacts into sustainability assessments and awareness of the importance of equity (Tomei et al. 2015). If biofuels are used, for example, rates of consumption should not hinder the natural replenishment of the environmental systems on which they rely. Their consumption should also contribute to the pursuit of social and economic development that improve qualities of life, reduce social inequalities, and reduce poverty. Furthermore, biofuel developments should respect environmental justice principles, especially distributive justice to promote inter- and intra-generational equity. The inclusion of equity in sustainability assessments of biofuels can present numerous theoretical, methodological, and practical challenges, but it is important that equity matters are incorporated into a unified framework that considers the distribution of ecological, social and economic outcomes in different contexts (Tomei et al. 2015, Demirel 2018, Mubarik et al. 2021).

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9.1.2 Environmental, Social and Governance Environmental, social and governance (ESG) initiatives pursue responsible investing enhanced with screening of portfolios based on ESG criteria. These criteria can include:

• • • • •

Environmental and sustainability track record Corporate governance, business ethics and transparency Product safety Fair and safe workplaces with responsible business practices Human rights and community involvement

ESG criteria help many socially responsible individuals, families and organizations invest in a way that aligns with their values, often so that they are in line with EDI. Sustainability and EDI are a way forward for companies that involves focusing on the social or ethical aspects of sustainability. Sustainability and EDI are intertwined and best addressed together when implementing sustainable engineering as a part of a sustainability strategy. EDI can help organizations attract more investors and customers, and become responsible players in the community by effectively communicating and collaborating with people from diverse backgrounds and expertise (Tamtik and Guenter 2019).

9.1.3  Equity, Diversity, and Inclusion in the Industrial Sector A diverse, equitable, and inclusive workplace can help improve the environmental impact of a company. This statement involves several points (Tamtik and Guenter 2019, Fuentes et al. 2020): 1. Equity and inclusion help create equitable and inclusive processes: To successfully optimize the organization, it is important to include all stakeholders and create processes that enable each individual with the support required. 2. Inclusive leaders possess higher cultural intelligence and skills to manage diversity: To improve the environmental footprint of a company, leaders need to effectively communicate with many individuals from different backgrounds, externally as well as internally. It is important for them to understand how to manage diverse teams and possess cultural intelligence to succeed in their goals. 3. Diversity helps build better strategies: Having employees representing communities or locations where the company operates helps to better understand the positive or negative impact on the surroundings of the company’s operations. This builds more trust and helps companies develop better strategies to support the customer. 4. Diverse teams are often more innovative and better prepared to take bold actions: Environmental efforts often require bold actions like rethinking product design and supply chain and changing behaviors within the organization to be more in line with sustainable choices. To take bold actions and solve challenges, diverse teams are more prepared.

9.1.4  Work Ethics Ethics is one of the most overlooked aspects while developing sustainable strategies. Ethics is measured by the concept of social license, meaning that the company and its measures should be supported by its employees, stakeholders, and the community in which it operates. To have an ethical social impact, companies need to work on treating their employees fairly, promoting non-discrimination policies, supporting flexible working hours where appropriate, investing in local communities, implementing fair wages, and understanding supply chains and their problems.

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A diverse, equitable, and inclusive workplace improves the ethical or social impact of a company in numerous ways: 1. Promoting equity in the company, ensures that everyone has access to the same opportunities and treatment. It also allows everyone to participate fully in the company’s sustainability efforts. Employees feel valued and heard and, therefore, they are much likely to support the measures of the company, and work towards a shared goal. 2. Inclusion leads to conscious decision making: Leaders who understand the dynamics of inclusive leadership and are aware of their own unconscious bias and privilege, make more conscious and fair decisions. 3. Inclusive workplaces have better psychological safety: Feeling safe is one of the key human requirements to perform efficiently. When employees feel safe within the company, they share vulnerabilities without fear of repercussions and are not afraid to fail. This usually increases team performance and risk-taking ability. 4. Diversity and inclusion help a company reach a wider audience and avoid discriminatory actions. People from different backgrounds may provide a company with insight into services marketing campaigns or practices. Sustainability relies on the maintenance of the resilience of the natural environment as well as stable and acceptable relationships between people. Sustainability is a specific development and requires meeting the basic needs of all and extending to all the opportunity to satisfy their aspirations for a better life. The economic aspect of sustainability is not just about being profitable, but also about having good governance within the company. Such a company is normally transparent and avoids conflicts of interest (Nelson and Vucetich 2012).

9.1.5  Skills for Sustainability Among many skills, the followings may give sustainable engineering an edge (Mellet and Finnel 2018): 1. Technical knowledge: Work in the industrial and energy sectors requires a range of technical skills to excel, and to lead teams and projects. This requires an excellent knowledge of scientific principles and concepts to perform engineering and scientific analyses based on solid data, and a good understanding of the industrial problems and trends. Technical knowledge includes a detailed understanding of global energy markets, economics, and the wider business context, as well as knowledge in cost analysis, regulatory policy, and liabilities (Hillman and Werner 2017). 2. An inquiring mind: As sustainable engineering is constantly evolving, a natural curiosity for how things work and for exploring concepts is beneficial. An inquiring mind should actively engage to explore, discover, and try out new systems and processes. 3. Ingenuity: To progress, sustainable engineers must be able to collaborate with others to improve results rather than competing, by being able to develop beneficial and innovative ways to reduce costs, improve social well-being and protect the environment, while meeting customer requirements. Therefore, one needs to demonstrate creativity to apply out-of-box-solutions to emerging problems, both anticipated and unanticipated. 4. Communication: Excellent communication skills, including the ability to articulate ideas, both in writing and verbally, are critical to making real change, establishing networks and building relationships with colleagues and clients. Engineers, project managers, consultants and experts need to have content prepared for the audience in order to communicate well, to manage projects and to ensure new ideas are clearly conveyed. 5. Motivation: Motivation and commitment to work are important for devising innovative solutions even when projects do not work out as expected or problems occur. One needs to

354  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence identify and establish which motivators work best and encourage productivity and promote creativity. For example, a company can provide employees with more freedom in decision making, mentoring, and rewards.

9.1.6  Workforce with Sustainability Strategy Project sustainability and learning and development are invested in what members of a workforce do, and how they do it. Engineering and constructing critical human infrastructure are critical for sustainable outcomes. A new workforce needs to be cultivating leadership and knowledge of sustainability throughout all levels of industry and manufacturing, along with a culture of continuous improvement for innovation. Upskilling a workforce with good governance can be rewarding (Galpin and Whittington 2012, Murray and Holmes 2021), and could lead to improvements in the following areas among others:

• • • • • • • • • • • • •

Business ethics and anti-corruption Business continuity, risk management, and emergency preparedness Sustainability policy for operations and project execution Information security and cybersecurity Innovation in environmental stewardship Water stewardship Carbon footprint and climate change Biodiversity/habitat social progress Health, safety, and security Talent management and learning and development Work/life balance Diversity and inclusion Labor rights and relations

9.2  Education for Sustainable Engineering Education in sustainable engineering is multidisciplinary and covered to varying degrees by many engineering programs. Such education builds on sustainability research and education and offers students the flexibility to develop tracks in areas such as renewable energy systems, systems modeling and analysis, process intensification, energy analysis, product design, artificial intelligence, and engineering policy and management. A sustainable engineering degree is designed to accomplish numerous educational objectives, including the following: • Heightened awareness of issues in areas of sustainability, including GHG emissions, global warming, ozone layer depletion, deforestation, pollution, gross domestic product, economics and the economy, technological development, and public health. • Clear understanding of the role and impacts of various aspects of engineering design, technology, and engineering decisions on environmental, societal, and economic factors and problems. Emphasis is often placed on the potential trade-offs between environmental, social, and economic dimensions. • Strong ability to apply engineering and decision-making tools and methodologies to sustainability-related problems. • Demonstrated capacity to distinguish professional and ethical responsibilities associated with the practice of engineering.

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Sustainable engineering science should integrate science, education, society, economy, environment, and technology. In addition, sustainable engineering should emphasize: • Research on equity, ethics, inclusion, and diversity. • Resilient systems and interconnections that affect or support sustainability. • An interdisciplinary approach regarding societal, ecological, and economic aspects of sustainable engineering science. Therefore, a new or updated curriculum design for sustainable engineering education would be beneficial for students from various backgrounds and from different engineering and science programs. In addition, such a curriculum can benefit from having an emphasis on research and development (Allenby 2011, Brundiers and Wiek 2011, Barth and Michelsen 2013, Wiek et al. 2013, Birou et al. 2019, Saputri and Lee 2020). Plan of study A program in sustainable engineering equips engineers with the tools they need to meet the challenges associated with delivering goods, energy, and services through sustainable means. This is in addition to basic course work in engineering analysis, energy analysis, process intensification, life cycle analysis, artificial intelligence, public policy and environmental management. Students would normally be required to complete a capstone project directly related to sustainable design challenges impacting society, the environment, and the economy. Many of these projects can be incorporated into sustainability themed research including process intensification, energy efficiency and management, life cycle engineering, and sustainable process implementation. Students would also have course work to complete. Education can lead to shifting toward or achieving sustainability and is a high priority for many industry professionals and in science, technology, engineering, and mathematics (STEM) education (Demirel 2004a,b,c). Industry often considers near- and long-term steps toward sustainability, through education in such areas as energy efficiency, use of renewable feedstocks and energy, green chemistry, life cycle assessment (LCA) and others (Demirel 2005, Demirel 2021, Curran 2015). Modular teaching technique The engineering discipline has evolved to embrace science and engineering analyses with artificial intelligence, energy analysis, process intensification, and process safety. Engineering graduates are working in more and more diverse industries, including biological and health sciences, biofuels, data processing, and life cycle assessments. Graduates may work as product engineers to define products, understand property-structure relationships using chemistry, biology, and physics. They may also work as process engineers, designing processes using unit operations, optimization, and cost analysis. Consequently, this emphasizes the constant need for revisions of teaching strategies and instructional materials in engineering core courses. One of the ways of achieving this goal may be a modular teaching technique. A module is a student text based on level, prerequisites, and learning objectives of a topic and accommodates institutional and temporal variations. It responds to diverse and fast changing learning objectives, the dynamics of accreditation in engineering education, new teaching techniques, and new advancements in technology. Each module helps students to transfer and synthesize knowledge across courses. The modules provide the students with examples of open-ended design problems, safety, and maintenance problems, and develop strategies in making engineering decisions under uncertainty (Demirel 2004d,e, Demirel 2005).

356  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence Workbook strategy All educational institutions emphasize the importance of developing effective learning and teaching strategies. Among others, the following issues are widely observed in engineering education and training (Felder et al. 2002, Demirel 2004b,c,d,f,g): • There are often mismatches between the learning and teaching styles. Most instructors are intuitive learners, and yet students are in majority visual and sensing learners. Textbooks also have their own styles in delivering knowledge. • Students often learn problem solving using cook-book procedures instead of learning how to solve problems by understanding concepts. • Students mainly need to learn the skill of transferring and synthesizing knowledge at a higher order within a course or across courses. • Student’s ability, background, and the match between the learning and teaching styles may determine the level of learning. Effective teaching may include a high level of creativity in analyzing, synthesizing, and presenting knowledge in new and effective ways. This helps ensure the ability to be analytical, intellectually curious, culturally aware, employable, and capable of leadership. The effectiveness of teaching may be improved by incorporating a multi-style teaching and learning approaches to engineering education, since the strengths and dimensions of learning styles varies from one student to another (Felder et al. 2002, Ellis et al. 2003). The workbook strategy aims to enhance effective teaching and active learning in engineering education by integrating the following elements: • analysis of classroom structure, • use of workbook in teaching, • group work for cooperative learning. The workbook strategy may enhance the effectiveness of instructor and textbook by making the course material more visible and easily extractable as well as relevant with applications. This can help reduce or mitigate mismatches between learning and teaching styles. The workbook starts with a detailed course syllabus containing the breakdown of topics to be covered from the textbook. It presents these topics with all the essential verbal and visual elements taken from the textbook in a systematic and organized way to teach students with various learning preferences and diverse backgrounds. The visual elements consist of graphs, diagrams, algorithms, charts, tables, pictures, figures, and data. The verbal elements include theory and analysis, definitions, and equations. Visual and verbal elements support each other in a categorized way, and hence (i) stimulate easy understanding, (ii) relate fundamentals to applications, and (iii) reduce mismatches between learning and teaching preferences. This may facilitate connecting the pieces in the classroom by searching equations, data, and concepts, which sometimes may be spread out on several pages within the textbook. If this vital connection, when needed the most, fails or is incomplete, then effectiveness of teaching and learning decreases at best, or may fail completely (Demirel 2004b,c,d). Some of the anticipated and observed benefits of learning and teaching environment with workbooks are as follows: • The workbook provides students with objectives, visual elements, equations, analysis, and applications in a categorized way to access easily without searching for them through many pages and parts. Hence, it can offset mismatches between the learning and teaching styles, regardless of student background. • Instructor and students collaborate actively during the lecturing as they complete the missing or incomplete visual and verbal elements and discuss applications.

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• The workbook provides students with organized course notes, thereby leaving more time for critical thinking and interactions with other students and the instructor. This enhances deep learning of the course material, and the skill of transferring knowledge within or across courses. • The workbook provides easy access to definitions, analyses, applications, synthesis, graphs, diagrams, figures, tables, data, examples, and homework problems, leading to effective review and use of the course material. • The workbook provides examples and homework problems and relates them to fundamentals. Group work for cooperative learning Group work consist of two or three students with different learning styles and preferences and encourages cooperative learning. Group work activity splits into two types: (i) in-class group work, and (ii) out-of-class group work. For in-class group work, the instructor prepares and distributes group work packages containing some of the graphs, diagrams, and data that are to be used to solve problems and record group activities all through the semester. Practically in every lecture, groups solve a short problem related to a freshly introduced topic, fundamentals, and analysis. They, usually, work about 10 to15 minutes on their collaborative learning, and submit the packages at the end of each lecture. The instructor checks and returns the group work by the next lecture. Further, during ‘two-minute breaks’, students talk with each other, reflect on what they are doing, and ask questions. Sometimes they answer questions, such as ‘what are the three important keywords within the last chapter?’ Out-of-class group projects on engineering analysis and computation are assigned for each group. Groups prepare the projects in two or three weeks, and often present them using a power point presentation in front of other groups (Bean 2001, Ellis et al. 2003, Demirel 2004e,f,g). Assessments of the workbook strategy A proper assessment is essential for measuring the true effectiveness of the workbook strategy and developing the best format and procedure for a particular course. Therefore, the workbook may gain maturity after it is implemented for the first time and assessed properly with the feedback from the students. These summative assessments can be compared with each other to identify trends for improving the workbook strategy, its implementations, and its course-based format (Bean 2001, Demirel 2004b,c,d). Sustainability and education Engineering is the application of technical and mathematical principles for practical aims, while addressing environmental, economic, sociological, health and other constraints. Engineering activities are significant contributors to economic development, standards of living and the well-being of society, but they also impact the environment, cultural development, and sustainability. Engineering is always advancing, as evidenced by the fact that both engineering education (Kishawy et al. 2014) and the engineering profession (NAE 2018, Conlon 2008) are continually evolving. For instance, universities now are focusing on contributing to sustainability (Rosen 2020a). Industry predicts permanent changes in the roles of the workforce as on-the-ground teams become increasingly virtual or remote. Industrial AI also helps create a new type of workforce with added ability for decision-making and interactive operator training capabilities based in part on past industrial experience. Frontline workers rarely have the levels of training in engineering and analytics, or the appropriate tools, to be able to respond quickly and effectively (Vega and Navarrete 2019, Birou et al. 2019).

358  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence As noted earlier, sustainability can be viewed as having three components: environmental, economic and social. Engineering is connected and often central to each component (Rosen 2012c). Examples: • Environmental: Resources for engineering activities are obtained from the environment and wastes from engineering activities are typically emitted to the environment. • Economic: Engineering uses resources to drive much if not most of the world’s economic activity, in virtually all sectors of the economy. • Social: Engineering services often permit good living standards, foster cultural and social development, and support social cohesion and stability. These usually figure prominently in engineering sustainability education. Sustainability in engineering is a critical aspect of achieving sustainable development. Kreith (2012) states that “no subject is more important to the engineering profession or the wider world that we live in.” Yet, despite its importance, engineering sustainability is not well understood or widely accepted. The concept of engineering sustainability involves more than the application of general definitions of sustainability to engineering. Engineering sustainability involves the provision of engineering services in a sustainable manner, which requires that engineering services be provided for all people in ways that, now and in the future, are sufficient to provide necessities, affordable, not detrimental to the environment, and acceptable to communities and people. Universal agreement has not yet been achieved on an engineering sustainability definition. Some related definitions have been presented in some areas. For instance, various definitions have been considered for energy sustainability (Rosen 2017a, 2021, Kreith 2012). Rosen (2021) defines energy sustainability as the provision of energy services for all people in a sustainable manner, i.e., in ways that, now and in the future, are sufficient to provide necessities, affordable, not detrimental to the environment, and acceptable to communities and people. Engineering sustainability Engineering ssustainability has been investigated in recent decades, and many of these studies can form bases for education on engineering sustainability. Some examples of the breadth of investigation follows: • Fundamentals: Science and engineering sustainability are examined by Kajikawa (2012), while engineering ssustainability is viewed with sustainability science as a new emerging discipline (Saputri and Lee 2020) for addressing sustainability as a requirement of engineering processes. • Engineering practice: The relation between sustainable development and professional practice is investigated by Laws and Loeber (2011). The theory and practice of sustainable engineering is described by Allenby (2011) and Graedel and Allenby (2010). The application of engineering to problem solving related to sustainability is examined by Seager et al. (2011). Teaching can be coupled to transport and rate processes (Demirel 2004a). The workbook strategy can be applied in engineering education (Demirel 2004b,c,f), and include modular teaching and product design (Demirel 2005). Teaching styles and workbook strategy in are important in engineering education (Demirel 2004d), and a modular teaching experience can be useful in capstone design (Demirel 2004g). • Indicators: Appropriate indicators for the sustainable use of natural resources are described by Harrison and Collins (2012). • Goals: Much work has been done recently on the United Nations Sustainable Development Goals (Rosen 2017b, Alvarez-Risco et al. 2021, Di Fabio and Rosen 2020). • Efficiency: The utilization of engineering sustainability to help improve efficiency is investigated by Rutkauskas (2012).

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• Social factors: The socio-technology of engineering sustainability is considered by Bell et al. (2011). • Engineering design: A guide for sustainable engineering and design is developed by Jonker and Harmsen (2012), while a conceptual framework for sustainable engineering design is proposed by Lindow et al. (2012). • Processes and systems: Zhang et al. (2020) have examined challenges for process systems engineering, in the context of sustainability. The extension of systems engineering for sustainable development is described by Pearce et al. (2012). Applications of sustainable practices to large-scale engineering operations have also been reported, e.g., sustainable development of the Red-Mediterranean-Dead Seas Canal project has been examined by Rosen and Abu Rukah (2011). • Resource use: The sustainable use of natural resources is described by Harrison and Collins (2012). Some of these investigations are focused on specific discipline areas: • Energy: Research is reported on improving the sustainability of energy processes (GEA 2012, Rosen 2017a, 2021). A methodology is proposed for evaluating the sustainability of a national energy conversion system and applied to Canada (Gnanapragasam et al. 2011, Gnanapragasam and Rosen 2017). • Manufacturing: Sustainable manufacturing has been studied (Rosen and Kishawy 2012) and applied (Luisser and Rosen 2016). • Transportation: Sustainability aspects have been investigated for energy conversion in urban electric trains (Bulucea et al. 2016). • Food and water: Rezaie and Rosen (2020) examined the energy-water-food nexus, as a framework for sustainable development modeling. • Cities: Alvarez-Risco et al. (2020) have examined characteristics of sustainable cities, considering social, economic, and environmental factors. Industry impact on education Links with industry are necessary for producing industry-ready graduates who understand risk and process safety. Such industry-influenced education typically has the following dimensions: • People dimension focuses on the public, stakeholders, and industry representatives that may interact with students. Students should be exposed to industry guest speakers and policy makers. Tutorial sessions led by tutors who are industry experienced engineers can be helpful. • Spaces dimension incorporates both physical and virtual places and spaces, which can enhance learning and systems-based thinking. Students appreciate viewing different industry sites and spaces. This can be done virtually through the Chemical Safety Board website and company websites as well as from first-hand discussions with industry personnel. Industrial site visits can be instructive, such as visits to ethanol plants and petrochemical refineries. • Delivery dimension refers to the learning methods and teaching methods employed, including problem and project-based learning as well as individual and team-based learning. Teaching approaches can include lectures, tutorials, and intensive sessions with active learning tools like industrial training sessions. Individual and team activities include conducting research, performing modelling, and identifying and assessing risks. The latter also include tools risk matrices, hazardous operability (HAZOP) analyses, and what if analysis used by industry. In addition, technical communication skills are emphasized by preparing oral presentations and written research reports, to help graduates develop professional engineering competencies (Hassal et al. 2020).

360  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence Problems and projects provide opportunities for students to learn, embed knowledge and develop skills in engineering competencies. Well selected projects can challenge thinking, decision making and communication. The problems and projects may be industry-based and may involve risk assessments of actual industry activities and/or systems and industrial incident analyses. Industrial problems may involve design, optimization, and safety analysis. The performance of students can be assessed by team projects and individual exercises (Lozowski 2017, Lund et al. 2017, Aroma et al. 2019, Roldan et al. 2019).

9.2.1  Required Topic Areas for Engineering Sustainability Education There are several distinct requirements for engineering to be practised sustainably. They can form a basis for education in engineering sustainability, through outlining required topic areas. These topic areas are discussed individually in this section, following the approach of Rosen (2012c). Tables 9.1 and 9.2 list some of the basic topics related to sustainability (Allenby 2011, Lozowski 2017). Table 9.1.  Sustainability topics covered in engineering courses, addressing different levels of decision-making. System Size

Topics

Gate-to-Gate: Decisions are made within a single facility or corporation by engineering and/or business units

Process design, including material and/or energy reduction Material or chemical selection Product design for a single phase of a product’s life (e.g., design for recycling) Pollution prevention Media-based (i.e., air, water, solid waste) regulations

Cradle-to-Grave: Decisions are made by different entities over the life of a product or sector activity; activities are typically analyzed as sequential events (e.g., life cycle analysis)

Resource availability and economics Consumer behavior Product utility Reuse and recycling options Product based legislation (e.g., Waste Electrical and Electronic Equipment) (WEEE) and standards (e.g., ISO 14000) Life cycle inventory development

Inter-Industry: Decisions are made by two or more entities (corporations or other stakeholders), often involving multiple sectors

Material flow analysis Byproduct synergy Eco-industrial development Multiple/nested life cycle analysis Input-output analysis (either physical or economic)

Extra-Industry: Decisions are made by multiple types of stakeholders, industry, nongovernmental organizations (NGOs), policy makers, and consumers

Policy development (current and historical) Consumption patterns and preferences Eco-industrial development Multiple/nested life cycle analysis Input-output analysis (either physical or economic)

9.2.2  Sustainable Resources Most engineering activities use natural resources, like water, materials, and energy. The degree to which resources are sustainable depends on many factors, including their scarcity and importance to ecosystems. Scarcity is an important sustainability issue for endangered species where they constitute resources (e.g., rainforests). Indicators for the sustainable use of natural resources have been developed (Harrison and Collins 2012). Waste material and waste energy can sometimes be used as inputs to engineering processes, reducing the use of new natural resources. Energy resources constitute an important subset of resources, and various types of renewable and non-renewable energy sources exist. Sometimes engineering resources are sustainable, in that they can be replenished at a rate equal to or greater than the usage rate, e.g., certain biomass resources. However, most resources used in engineering activities are available in finite quantities and not sustainable (e.g., minerals, fossil

Workforce in Sustainable Engineering  361 Table 9.2.  Sustainability topics covered in engineering courses that are drawn from non-engineering disciplines. Discipline

Description

Metrics

Topics

Life Sciences

Human, animal, or plant health

Mortality and reproduction rates are the primary metrics

Toxicology Biological ecosystems Nutrient availability

Physical and Environmental Sciences

Mechanical and chemical properties, activities, and interactions

Mass, energy, and time are the primary metrics

Chemical reactions and behavior in the geo-biosphere Physical and environmental sciences Perturbations and flows within the geo-biosphere Physical input-output analysis

Economics and Business

Exchange of goods and services that accounts for natural and/or man-made capital at micro and/or macro levels

Economic currency is the primary metric

Cost analysis Economic input-output analysis Life cycle cost analysis Economics and business

Sociology and Policy

Control and analysis of human behavior

Values are typically expressed as counts or fractions relative to a desired target

Environmental regulations and legislation Sociology and policy Consumer behavior Cultural and other value systems

Humanities and Aesthetics

Consideration of elements that provide comfort and pleasure

Values are typically expressed as counts

Architecture Design Leisure Humanities and aesthetics

fuels). Sometimes resources available in finite quantities can be viewed for practical purposes as sustainable, depending on the reserves available and the rate of use. For instance, Graedel and Allenby (2010) suggest that a resource can be treated as sustainable when the ratio of these quantities exceeds some value, e.g., 50 or 100 years, although this is contentious and may not be reasonable for many resources (Demirel 2018, 2021).

9.2.3  Sustainable Processes Resources are used in engineering processes. An important requirement of sustainable engineering is the use of sustainable processes, i.e., processes that are sustainable in terms of their operations and the resources they utilize. Sustainable processes usually utilize widely available materials and appropriate technologies for the corresponding tasks. Waste outputs of sustainable processes should not hinder sustainability. Sustainable processes involve activities like sustainable transportation, distribution, storage, design, monitoring, control, and manufacturing (Rosen and Kishawy 2012). Sustainable processes normally utilize energy carriers (electricity, secondary chemical fuels, thermal energy) that are sustainable. Hydrogen is considered by many to be a sustainable energy carrier because it facilitates the use of non-fossil fuels through conversion to a chemical fuel (Scott 2007, Gnanapragasam and Rosen 2017, Sadeghi et al. 2020, Moharramian et al. 2021, Valente et al. 2021). Many energy carriers do not exist naturally and need to be produced. Cogeneration and district energy systems deal with thermal energy as an energy carrier (Rosen and Koohi-Fayegh 2016), as do many geothermal energy systems, especially those that support heating and cooling (Rosen and Koohi-Fayegh 2017).

9.2.4  Increased Efficiency High efficiency supports efforts to achieve engineering sustainability by permitting resource utilization to provide the greatest benefits regarding products or services. From a broad perspective, efficiency increases encompass not only efforts to increase the efficiency of processes, devices and systems, but also measures for enhanced resource management, resource conservation and

362  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence substitution and better matching of resource supply and demand. The latter point includes matching energy supply quantity and quality to energy demand quantity and quality (e.g., supplying moderate temperature space heating at 21°C using moderate temperature thermal resources at say 50°C instead burning fossil fuels at 800°C) These concepts are often best considered via the use of advanced methods and tools (e.g., exergy analysis as well as exergoeconomic and exergoenvironmental methods, lost work analysis, and pinch analysis). For example, exergy analysis can identify insights that help improve process efficiency. Also, for many processes and systems, resource storage can be an important element of improving sustainability. For energy processes and systems, for instance, energy storage is often necessary for improving sustainability (Rosen 2012a, Dincer and Rosen 2021, Demirel 2021).

9.2.5  Reduced Environmental and Ecological Impact Numerous environmental and ecological impacts—affecting the atmosphere, hydrosphere and lithosphere as well as biota—are associated with engineering processes. These impacts can be exhibited in many forms, for example, harm to health of people and ecosystems, damage to engineered systems and structures, and degradation of aesthetics. Many of these environmental and ecological impacts are of concern and must be addressed in efforts to attain engineering sustainability (Rosen 2012b). For holistic and meaningful assessments of environmental impacts, life cycle assessment or related methods are necessary (Jianu et al. 2016, Singh et al. 2018, Sadeghi et al. 2020). This is because environmental impact assessments of engineering operations need to view the entire life cycle of the operations to capture all environmental impacts. The life cycle begins with the acquisition of resources to construct systems and operate them and ends with their ultimate disposal post utilization. Thus, LCA is often referred to as a cradle-to-grave assessment. Some of the most significant environmental impacts linked to engineering systems and processes follow: • Climate change: Climate change on a global scale is driven predominantly by global warming, which in turn is driven by greenhouse gas emissions to the atmosphere. Climate change on a global scale is thought by many to be the most important environmental issue for both civilization and humanity (Rosen 2020b). • Stratospheric ozone depletion: This type of ozone depletion is due to diminishment of the ozone layer in the upper atmosphere, particularly the stratosphere. This raises the levels of ultraviolet reaching the surface of the Earth. • Abiotic resource depletion: Abiotic resource depletion is a consequence of the extraction of limited and non-renewable raw materials and resources from the environment. • Acidification: Acidification is driven mainly by emissions of acidic substances, and substances that can react in the environment to form acids. Acidification adversely affects the water and ground as well as living species in them. • Ecotoxicity: Ecotoxicity leads to various types of health problems in many living species. Ecotoxicity is attributable to exposure to toxic substances. • Radiological effects: Radiological effects of most concern are in the form of radiogenic cancer mortality or morbidity, but they also include other effects. The effects are caused by radiation exposure (external or internal). These environmental impacts particularly impact the degree to which engineering sustainability can be approached.

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9.2.6  Addressing Other Sustainability Facets Numerous other facets of and factors related to sustainability need to be considered in efforts to enhance engineering sustainability. Unlike many of the factors described above, these facets are in large part non-technical in nature. But they nonetheless need to be addressed to support engineering sustainability. Some of these follow: • Health and safety. Engineering must be healthy and safe, and facilitate the ability to be healthy and safe, for sustainability. Avoiding harmful health effects is particularly important. • Ability to satisfy and/or curtail increasing resource demands. An important challenge is to be able to satisfy the increasing future demands for material and energy resources. This is likely to be particularly challenging as populations grow, and as developing countries become more industrialized and their living standards rise. • Suitable land use. Balance is needed between land use for engineering-related activities and for other activities, which can vary from recreation and enjoyment to through to functional activities like forestry and agriculture. For example, the balance of land use for food production vs. energy plants is an important sustainability consideration. • Economic affordability. For sustainability, engineering services for the provision of basic needs must be economically affordable by people in all countries. • Equity. Equity is required for sustainability in terms of geography, time, level of development, and other factors. For instance, equity is needed between developing and developed countries regarding access to engineering services, and all countries, regardless of location, need to be able to access engineering services. Furthermore, future generations be able to access engineering services and resources for engineering sustainability. • Population. Increasing population needs to be accounted for or addressed for sustainable engineering, since population growth places stresses on the carrying capacity of the planet and the environment. • Societal involvement and acceptability. People and the societies in which they live and work must be consulted, involved in and supportive of major engineering-related decisions if engineering is to become more sustainable. • Accommodation of human needs. The human dimensions of the new technologies, beyond their engineering aspects, need to be accommodated as part of efforts for engineering sustainability (Webler and Tuler 2010). • Aesthetics. As the aesthetics of the environment, including cleanliness, affects the well-being of people, it needs to be addressed as part of engineering sustainability. • Lifestyles. Although the aspirations of people tend to increase with time, modifying lifestyles and managing engineering-driven desires can also support engineering sustainability. But transforming behaviour usually occurs only if present development directions are recognized as unsustainable. Such transformation is challenging, however, as translating future threats into present priorities is usually very difficult for policy makers. Note that these factors are sometimes related and overlapping, both among themselves and with the previously described requirements for engineering sustainability. For instance, determining permissible levels of climate change for sustainability needs to consider factors like equity, reserves of resources, economics, and stability. But taken together, the requirements for engineering sustainability discussed in this section can provide a basis and structure for education on engineering sustainability.

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9.3  Training in Manufacturing Engineers will need more training in new technologies, including design and operation of intensified and modular processes, modeling and simulation, advanced controls, and automation. Many techniques related to advanced manufacturing, process intensification, and artificial intelligence are not part of formal university training programs. Industrial manufacturing training must fill those gaps with workforce development programs. For example, the rapid advancement in process intensification deployment (RAPID), is working to create and deploy educational curricula for engineers, operators, and technicians (Provost 2017, NAE 2018, Birou et al. 2019, Roldan et al. 2019, Goh 2020). Advanced manufacturing and design education focus on process intensification, smart manufacturing technologies, artificial intelligence, and the global need for sustainable processes. This new focus affects how we should think and teach about advanced manufacturing (Demirel 2004d,e,f). Advanced manufacturing technology is driven by the continuing need to improve product quality; the importance of sustainability, including social responsibility, across the supply chain; and the need for design methods that produce inherently safer processes and products. Signature elements of advanced manufacturing include the following: • The ability to manufacture affordable products that meet human needs in a sustainable manner, • Better integration of transport and reaction processes with process design with process intensification, • Development of advanced sensors/actuators, AI applications, and model-based simulation tools, and • Co-optimization of products, processes, and supply chains. Companies are looking for the appropriate skills and normally want to take advantage of new sources of talent and new methods of training. Companies are recognizing that they need mentoring, including workforce planning programs with detailed strategies. For example, numerous companies are reinforcing the digital aspect of jobs, in part to make themselves more current and ready for the future through the integral nature of digital technologies. Further, there is concerted effort to create jobs in line with what workers are passionate about, and hybrid positions that combine engineering and information technology are being created. Besides, some programs involve cross-training to offer experience for workers on a variety of projects and functions that include research and development, engineering, safety, sales, and marketing. This provides a sense of engagement and impact. Here, the main goal is to match a company’s value proposition with the talents and interests of its workers (Roldan et al. 2019). Event agent The event agent is a living thread of user documentation, enabling the automation of knowledge. When the root cause of an event is successfully determined, the user can decommission the event agent. The technology uses a three-step workflow to address the need to provide frontline workers with the right tools to perform advanced diagnostics of plant disturbance events. First, the operator selects to create a new event agent and provides any known information such as one or more examples of the event, an estimation of how often the event occurs, the average duration of the event, and candidate tags around the process boundaries. Once defined, the event agent executes its analytics engine through information for each tag trend and distills the data into the relevant sensor tags that show a strong correlation within the defined event period. In the next step, operators define search parameters to execute a historical search for other occurrences of the precise signature of a selected event. The technology also assigns a metric that measures the percentage by which every identified event fits the initial selected event signature and a measure of how close an event matches the source event signature. Finally, once all events are

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reviewed, end users can choose to accept their event matching results and create a new event agent. Operators, then can optionally deploy an event agent online to monitor for event recurrences as the event agent extracts the configured tag data against the event signature stored in the event agent. If the patterns match the event signature, a notification is created, with predefined user instructions on how to resolve the event condition (Golan et al. 2019). When the operator cannot establish a root cause of an event and after identifying the event fingerprint, the operator proceeds to scan history to find similar past event signatures. If other signatures of the same event exist, event analytics returns them with the dates of the occurrences. Consequently, the operator may look back to find how the issue was solved last time. When the scan of history does not find similar events, the operator knows this is a new event and deploys a new online event agent.

9.3.1  Mentoring in Industry Integrated console, virtual-reality simulation offers good learning and measurable training efficiency (Figure 9.1). As a modern technique for a realistic plant training for control room operators, field operators, and operations and maintenance personnel, simulation leads to 78% learning retention rate, which is higher than that of other training options, including audiovisual (~ 20%), reading (~ 10%), and lectures (~ 5%). On-the-job training is unstructured, while experiential learning is valuable and can be accomplished using simulation. The personal effectiveness model considers technical competency as a high priority (Sayyadi 2019). The connection of research to industrial experience can be very powerful to prevent undertaking research which is not an industrial concern. Interactions between academia and industry provide students with better career opportunities. Industry sometimes partners with universities for training the students in needed skills such as the Industrial Internet of Things (IIoT). Artificial intelligence (AI) enabled engineers design optimal plants at every level of detail in analyzing the root cause of events in the plant without years of data history (Lozowski 2017, Jenkins 2017, Rentschler and Capitano 2020). A complete, integrated simulation platform can train control room and field operation and maintenance personnel. Such a platform consists of portions of control panel-based simulation integrated with small virtual-reality training environments and targets learning of high consequence, difficult and important items for both operations and maintenance training. A simulation platform can be deployed globally over the ‘cloud’, is always available and can be set up for trainee self-study (Provost 2017). Training platforms can benefit from setting up a performance-based competency grid, as shown in Table 9.3, and identifying exceptional items that should be physically experienced based on the sector’s priorities. A competency grid serves as a foundation for an effective training program in line with the sector priorities. The main steps include: 1) detail Review key performance indicators forevents the sector, of in analyzing the root cause of Jenkins 2) Review existing job-position performance analysis data, 2017, Rentschler and Capitano 2020). Review key indicators for the site Deploy training with learning management system

Review job performance analysis data

Conduct job task analysis/ hazard analysis

Develop operator training simulators

Develop competency grid based on training

Develop/integrate training and simulation

Assign effectiveness rating

Assign minimum competency rating

Categorize and weigh training items

Figure 9.1.  Training steps in industry with personal effectiveness competency model.

366  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence 3) Conduct a simulation and training needs analysis that includes job task analysis, key priorities, job hazard analysis, post problems, and a new simulation training plan, 4) Develop training program requirements with combined simulation and virtual reality, 5) Develop a competency grid based on training items related to sectors priorities, 6) Categorize and assign weighting criteria and values to all training items in the competency grid. 7) Assign minimum competency ratings, 8) Assign an initial effectiveness rating to all trainees considering previous experience and training, 9) Develop and implement initial and continuing training programs, 10) Integrate the simulation and training platform with a learning management system, 11) Deploy the personnel effectiveness simulation and training platform. As a capital expense, a good virtual reality environment can allow connection to more than one control systems. A more competitive workforce with effective training can increase the quality and value of production and other targeted sector priorities (Provost 2017). Table 9.3.  A performance-based competency grid for training. Trainee name

Operation & Maintenance (O&M) Effectiveness Rating 77.3%

Competency area

Competency group

Integrated O&M scenario team training (itemized)

Team training

Trainee score Operations personnel

Maintenance personnel

O&M personnel

Total effectiveness score

Training competency item

Process flow 10

Safety 10

Environment 10

Data analysis 10

Area effectiveness rating

Max. Score

9/10

8/10

8/10

7/10

32

40

Integrated panel op./ field training

8/9

8/9

8/9

8/9

32

36

Panel operator training scenarios

4/8

7/8

4/8

6/8

21

32

Field operator training scenarios

4/8

7/8

4/8

7/8

22

24

Operator fundamentals

4/4

4/4

2/4

2/4

12

16

Maintenance procedures

4/7

4/7

6/7

6/7

20

28

Maintenance fundamentals

4/4

4/4

2/4

2/4

12

16

Past problems/ incidents

4/6

4/6

5/6

6/6

19

24

System-based fundamentals

4/5

2/5

3/5

4/5

13

20

Core training

2/3

2/3

2/3

2/3

8

12

Site EH&S

1/1

1/1

1/1

1/1

4

4

Site orientation

1/1

1/1

1/1

1/1

4

4

198

256

198/256 = 77.3%

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9.3.2  Diversity Training Diversity training focuses on creating awareness of diversity issues in the workplace, and their own beliefs on diversity. Diversity training may provide skills to enable people to interact, collaborate, and work with people who have different backgrounds, cultures, and qualities. Such training may help reduce the level of prejudice. This may help those from underrepresented groups for protecting against discrimination and feeling more confident in the workplace. Training should focus on celebrating the differences rather than reinforcing the differences between people, through offering equality, diversity, and inclusion topics. This type of training is requested by corporations, government organizations, and universities (Ragins and Ehrhardt 2020). Some advantages in actively implementing diversity policies are:

• • • •

strengthening cultural values within the organization and universities enhancing corporate reputation and public relations helping to attract and retain highly talented people improving motivation, innovation, creativity, and efficiency amongst employees

9.3.3  Training in Energy Management The ISO 50001 energy-management system standard is an international framework that helps industrial plants, commercial facilities, and organizations manage energy, reduce costs, and improve environmental performance. Some of the specific benefits include assisting organizations in optimizing their existing energy-consuming assets, as well as offering guidance on benchmarking, measuring, documenting, and reporting energy intensity improvements and their projected impacts on GHG emissions. ISO 50001 creates transparency and facilitates communication of energy resource management techniques and practices, helps facilities evaluate and prioritize the implementation of new energyefficient technologies, and facilitates energy-management improvements in the context of emissions reduction projects. The plan-do-check-act management system provides a methodical structure for solving problems and implementing solutions. During the plan stage, the problem is identified and analyzed, and a plan is developed. Then, during the do stage, steps are taken to implement the planned actions. To ensure that the best possible solution has been developed, a series of verifications and analyses are conducted during the check stage, and potential improvements are identified. In the final step, the act stage, necessary changes are applied (GEA 2012, Lund et al. 2017, Rosen 2017a, Rezaie and Rosen 2020, Demirel 2021). Energy management training focuses on various areas. Some examples follow, ranging from detailed technical issues to broader environment-related ones: • describing how energy is absorbed, stored, and moved in Earth’s climate system, • distinguishing how the amount of energy stored determines the temperature in a thermal storage, • interpreting the importance of feedback mechanisms that make the climate system sensitive to forcing, but also provide a stabilizing influence, • inferring how temperature responds to changes in solar input and greenhouse gas concentrations, • evaluating how simple models can be used to make projections of climate variables.

9.3.4  Cybersecurity Training Most people working on the industrial control system (ICS) and operational technology (OT) side of operations state their plants have been affected by a cyber incident, according to a survey. The use of information technology (IT) combined with OT and the industrial internet of things often leads

368  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence to more system vulnerabilities. With more automation, using these technologies brings increased responsibility to protect them from threats (Goh 2020). Cybersecurity training needs to be more comprehensive for employees. The idea of “awareness” training is not all-inclusive but should be a guide for better education. When leadership builds a culture around cybersecurity awareness, the whole organization usually follows this effort. Improving training by getting a baseline for cybersecurity knowledge, providing hands-on training, and incentivizing good cybersecurity practices hold leadership accountable to making cybersecurity a part of company culture (Goh 2020). Operators need to be proactive in defending against cyber-attacks. Responding to and investigating a cyber incident is procedural. First, operators contact the digital technology suppliers to ascertain if the system will come back online. The vendors and operators then work together to discuss critical business operations that may help recover from the attack, such as frequency of back-ups, how often employees have training or participate in cyber attack drills, whether the logs are comprehensive, and others. Knowing this will be the information required during an attack, plant operators can effectively develop a response plan through a tailorable stepwise framework, as outlined by Ouertani (2020): • Step 1 Establish Communication and Partnerships: Because cyber-attacks can target any component of a plant, it is best to communicate clearly with equipment vendors and employees. Also, leadership should provide clear visualizations for all processes to quickly identify problems should an incident occur. • Step 2 Design Incident Response Drills: An incident response drill is helpful to identify issues before a cyber-attack. Drills presented as case studies allow teams to respond to an incident and reflect on what went well and what can be improved. • Step 3 Assemble the Team: While a security team is essential, it is not the only team needed to respond to a cyber incident. A response team needs to have a representative from all stakeholders in the operation so that each department may enact the security policies. The goal is that these policies are helpful in all departments and carried out daily. Information technology combined with the industrial internet of things leads to more system vulnerabilities, in all types of industries. For example, cyber threats are a significant concern in the chemical process industry, and there have been cases where attacks have damaged and caused major setbacks in infrastructure. Some worm type threats target sensitive supervisory control and data acquisition systems that maintain control of a plant. This type of high-complexity worm can infiltrate the system undetected if vulnerable software is running prior to an attack. This reinforces that industry needs to prioritize cybersecurity training at all levels of the organization. The entire supply chain is vulnerable to attacks, so many companies implement a “zero trust” policy when working with vendors and customers. Companies normally do not require the same standard of care for cybersecurity by their suppliers. By enacting a “zero trust” policy, there is clarity of risks in all parts of the supply chain (Bradley 2018).

9.3.5  Training in Energy Analysis Training in energy analysis may focus on all the aspects of energy, such as energy production, conversion, conservation, distribution and storage (Demirel 2018, 2021). Training should supply an excellent knowledge of scientific principles and concepts in industrial and energy sectors, and good facility with energy balances and energy efficiencies. A detailed understanding of global energy markets, economics, cost analysis, environmental impact, and liabilities can help the workforce understand the energy transition and be able to contribute productively to decarbonization (Alhajji and Demirel 2015).

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Sustainability jobs There are numerous types of jobs in green engineering and sustainability (https://unsdg.un.org/jobs) (Rentschler 2018, 2019, Forsythe 2020). Some examples follow: • Solar Project Manager: Coordinates the development, construction, and management of large-scale solar projects throughout the public and private sector. • Managing Director: Oversees sustainability efforts at organizations specializing in marine, nuclear, energy and renewables. An operational management post involves the development and delivery of financial and commercial objectives, maintaining performance, driving excellence and ensuring full compliance with industry standards. In addition, a managing director collaborates with clients, leads growth initiatives and develops talent. • Sustainability Professional: Makes existing technology and infrastructure (e.g., buildings) more energy efficient, resilient and sustainable. Many businesses have implemented key sustainability strategies as part of their organizational objectives and require consultants, energy managers and experts who are accountable for achieving these goals and making technology, infrastructure, and businesses more energy efficient. • Engineer: A green energy engineer solves problems and advances technologies, usually in mechanical, electrical, civil, chemical, and environmental areas, focusing primarily on research and development. Engineering also plays an important role in the creation of new technologies including the design of solar cells and wind turbines and the development of advanced hydroelectric dams. Such engineers can work with a variety of organizations including non-governmental organizations, governments, sustainability businesses, energy facilities and those in technology and development.

9.3.6  Training in Process Intensification A thorough analysis of the thermodynamics, kinetics, and transport phenomena in intensified processes affords new opportunities to illustrate the core precepts of engineering in the processing and manufacturing sectors. Often there must be a clear advantage like a bottleneck to convince companies and investors to adopt process intensification (PI). Also, companies accept the link between PI and sustainability, for example, as a tool to achieve the United Nations Sustainable Development Goals (UN-SDG), because PI offers strategies to implement technologies with lower capital expenses (CAPEX) and in a safer and more efficient manner than conventional processes. The feasibility of PI options can be assessed for a process, considering the sustainability goals and their economic, environment, and societal dimensions. A traditional risk assessment typically follows. This procedure can help in training students and the workforce. Modelling during the design of industrial process reduces time requirements. Proper modeling and simulation require a solid education and understating of process engineering, scale up, and simulation techniques (Kiss 2016, Keil 2018). If there is not a specific course with its own curriculum, then PI-specific case studies can be integrated in capstone design projects with clear applications and assessments of PI in processing and manufacturing, in support of sustainable engineering practice (Rivas et al. 2020).

9.3.7  Training in Artificial Intelligence Industry is partnering with universities to train students in needed skills, such as data collection, data processing, and the IIoT. AI enabled engineers design optimal plants considering all relevant details; they can also analyze the root cause of events in the plant without years of data. This is particularly necessary for a remote workforce. Nonetheless, both remote and connected workforces require the skills of understanding data and knowing how to use data collected by sensors, for example in the maintenance of assets and in reducing downtime (Urso 2017).

370  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence Environmental, social, and governance (ESG) issues are now commonly evaluated for investments, as the need for sustainability in industry is increasingly widely recognized. This is a changing trend and presenting challenges on a new workforce. Many industrial sectors are data rich but information poor, driving the need for new advanced analytics tools to complement the rise of sensors and other equipment connected via the industrial internet of things (IIoT) (Dick 2019). Out of the rise of Industry 4.0, self-service data analytics tools help plants create an analytics-enabled workforce which can address production issues and improve process operations without the need for a data scientist. This can provide one way of maintaining a sustainable market condition by finding new ways to remain innovative and competitive. Here the objective is connecting the information hidden in data properly to knowledge and creating subject-matter experts (SMEs). Self-service analytics is a plug-and-play option and interface to create value from real-time data that a plant already creates. This may be possible by creating a level of operational intelligence and understanding of data necessary to improve overall performance and efficiency and may be possible when engineers are able to visualize all related plant events by unlocking the information in the sensor-generated time-series data with data analytics (Grosjean et al. 2019). Four common types of analytics exist (Dick 2019). They can be identified and differentiated as follows: • Descriptive analytics: What is happening in the operation? It provides the insight and key metrics to understand the operation by basic reporting the who, what, when, where, how, and how many at all levels of operation. • Diagnostic analytics: Why is this happening? This is used to uncover the reason why an incident occurs and to determine the root cause of the problem by using time-series data to identify the cause and effect and to identify patterns. • Predictive analytics: What will happen? This uses the findings from descriptive and diagnostic analytics to predict equipment failures in production, based on trends and interrelations. • Prescriptive analytics: What do I need to do? This uses all the data with insights and trends to determine future action that companies can take. For every new incident knowledge expands, leading to faster and better action in the future.

9.4  Workforce Soft Skills As a part of the curriculum in engineering, a new intellectual framework is needed for advanced manufacturing processes that builds on more-traditional designs. Transport processes, reaction kinetics, and interfacial phenomena should be taught in the context of designing chemical products and manufacturing processes. A capstone design experience may emphasize the integration of chemical product and process development through life cycle analysis using model-based systems engineering design languages, methods, and tools. Design methods are the approaches to implementing an engineering project and define the scope of the life cycle model (LCM), its elements, and the order in which the processes are to be carried out. These methods can include Stage-Gate approaches and innovation maps to formalize design activities within the product/process concept exploration, design, and implementation phases of the project life cycle. The formulation of equation-based frameworks for direct optimization of product properties and manufacturing process design also can be considered design methods, as they rely on methodologies for context development and the definition of the appropriate optimization program (Urso 2017, Jenkins 2017). There is the strong degree of overlap between design method development for products and processes. Consider the project life cycle model shown in Figure 9.2. The concept phase of stagegate (SG) corresponds to the stakeholder and system requirements identification stages of the vee with the system functional review (SFR) acting as the gate review. Feasibility is assessed in the preliminary design phase of the vee with feasible products screened during the preliminary design

Workforce in Sustainable Engineering  371 Stakeholder requirements System requirements

Validation System verification

SFR Element Preliminary verification design PDR Component verification Detailed design CDR

System acceptance System integration Element integration Component integration

Implementation Figure 9.2.  Life cycle vee model: Interpreting the state gate vee model in the context of a product/process life cycle model 9.2. cycle vee model: Interpreting the state gateconnections. vee model in with theFig. design andLife development phases as well as the verification/validation

review (PDR). Development in the SG process corresponds to the detailed design phase with the A wide critical design review (CDR) screening the winning manufacturing options. The manufacturing phase of the SG process is split hierarchically among component, element, and system integration phases of the vee with the requirement verification tests corresponding the manufacturing gate review. Finally, the product introduction phase of the SG process corresponds to the systems acceptance phase of the vee, with product validation serving as the final gate review. We note that an analogous connection between the sustainable engineering vee LCM and innovation maps also can be formulated. Soft Skills in Energy Analysis A wide range 9.4.1 of systems engineering methods and tools may be used in formalization of product/process design, particularly in the concept development phase. The LCM vee model shown in Figure 9.2 provides a useful roadmap for structuring a product/process design project. Initial and sustainability sectors need to demonstra design activities should focus on identifying the product and process functional requirements consistent with stakeholder needs. Product and process safety can be addressed early on in terms of 21 stakeholder requirements. Verification and ultimate validation of the requirements can be handled in a systematic and rigorous manner (Yadav 2012).

9.4.1  Soft Skills in Energy Analysis As the demand for renewable energy continues to increase, industry is looking to recruit welltrained candidates to drive green energy progress. Individuals looking to enter these professions in the clean energy and sustainability sectors need to demonstrate competency in communication, an active interest in green energy, and a natural aptitude for understanding changing technologies related to energy and environmental systems. To pursue leadership roles, one should be able to manage geographically diverse teams, coordinate complex technical projects and possess the ability to identify and run with new ideas (Mellet and Finnel 2018, Forsythe 2020, Demirel 2021).

9.4.2  Soft Skills in Process Intensification Process design, simulation, and optimization packages can help student understanding of process configuration and the consequences of process intensification (PI). Basic skills for that include engineering analysis with material and energy balances, sustainability analysis, and economic analysis. Modelling with computer aided design packages, as well as the Industry 4.0 approach and capability, help as effective teaching tools. Laboratory sessions can also be effective to demonstrate practically the impact of intensified devices, including micro-structured mixers, reactors or spinningdisc reactors on the selectivity of chemical syntheses. Another new and important link is PI and materials (Stankiewicz and Yan 2019) as several intensification strategies are directly related to various aspects of materials properties, such

372  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence Table 9.4.  Comparison of special skills on process intensification and process synthesis. Process intensifications skills

Process synthesis skills

Effective use of resources

Efficient use of resources

Equipment techniques and materials focused

Information and software focused

Experiments and modeling techniques

Computing

New processing and materials methods

New simulation and sensitivity analysis

Process units, catalyst, integrated process units

Integrated design of process and product

Creation of spatial structures

Control over time

Compact and robust structures

Optimization

Multiscale structures

Multi-scale integration

Modeling and sensitivity analysis

Model based systems behavior

as thermal conductivity for heat transfer, hot spots control, tortuosity, and porosity for catalytic applications. Materials can be formed to have “shape-selective” geometries, from the molecular to the mesoscale, such as zeolites, which have cavities that are both size and shape selective. Other properties such as super-wettability, super-hydrophobicity, magnetic and paramagnetic properties, magnetocaloric and metamaterials offer unique opportunities for PI. Table 9.4 compares the skills for process intensification and process synthesis skills needed in the workforce to implement sustainable engineering.

9.4.3  Soft Skills in Artificial Intelligence Digitization enables a workforce to work remotely, often leading to cost savings and efficiency improvements. Data analytics is core to operations and yet requires training and supporting of the workforce with digital knowledge, which is an important criterion for new hires. The roles of on-the-ground teams are virtual or remote. Industrial AI also helps create a new type of workforce with added ability for decision-making and interactive operator training capabilities. Frontline workers may not have the levels of training in analytics to respond quickly and effectively. AI enabled engineers can analyze the root cause of an event in the plant without data history (Forsythe 2020). There is the demand for a hybrid simulator arrangement where there is a virtual plant running alongside the actual plant, and quickly identifying/calculating optimal conditions, allowing workers to compare the optimal versus actual plants. This needs an interface like ‘supervisory control and data acquisition’ or excel that is accessible to workers. Here, the operator needs to understand what changing values in the actual plant mean. For example, the simulator can be set up to run automatically, access historical information and calculate the optimum for a worker to decide if it is worth to make changes to the process for better operation, maintenance, and predictive analytics (Ondrey 2019, Ordieres-Meré et al. 2020). By combining both process simulation software and data-driven techniques of artificial, intelligence and machine learning, processes in manufacturing and industrial sectors and be intensified in terms of control and scheduling decisions. This also bring about security issues in data processing (Rivas et al. 2020). This may be called user-driven machine learning by enabling all the workforce, including plant engineers, safety engineers, and maintenance engineers, to utilize the data without needing to acquire sophisticated analytics skills. Analytics from descriptive to predictive can be performed by engineers and operators using self-service analytics software that can provide tools, filters, and advanced queries to identify failures, create early warnings and troubleshoot incidents. For example, data from best performance may create process fingerprints and best operating zones to be used as monitors and prevent shutdowns (Usman et al. 2019). A workforce should be able to assist in the task of decision making on both problems and solutions on all relevant temporal and spatial scales. This skill means that sustainability at the system level can be assessed toward: analysis of deeper-lying structures of the system, projection into the

Workforce in Sustainable Engineering  373

future, assessment of sustainable and unsustainable trends, evaluation of the effects of sustainable policy and the design of possible solutions through sustainable strategies (Peiretti and Brunel 2018, Tosic and Zivkovic 2019).

9.5  Workforce and Digital Transformation Industry 4.0 refers to the fourth industrial revolution without the need for invention. The success of digitalization tools, including artificial intelligence and machine learning, depend on workforce engagement and involvement. Many sectors implement digital technologies and are seeking to create the right culture for adoption of them by workers and to reduce fears of job losses. Many see digitalization is an opportunity for workers to undertake more valuable, higher-level functions requiring more creativity and for maintaining the human element in the sector. To engage the workforce with models of artificial intelligence (AI) may require that AI tools be built for the workforce so they can develop their own AI applications naturally, with hands-on-learning and simulator-based training and without forcing them to learn new tools or techniques (Urso 2017, Jenkins 2019). Simulators are designed to de-instrument the process to permit workers to understand what the automation is designed to do. A performance learning platform is a portable training tool to reinforce competencies for digital transformation and help close the workforce skills gap. Efforts are also underway to explore ways to leverage digital tools to enhance the curriculum, content, and evaluation (Dick 2019). It is often thought to be best to train a team on an agile development process, so team members can collaborate effectively. This makes learning the digital platform easier. By observing an experienced mentor developer in action, a workforce learns how to engage business stakeholders effectively and deliver better solutions. Another way of enabling a new team is with training and certification. Introductory courses and rapid developer certifications help upskill employees on how to rapidly develop applications and use the digital platform. The most effective rapid application development teams are onsite together working through frequent iterations based on user feedback (Jenkins 2019, Sayyadi 2019). Usually, the objective is connecting the information hidden in the data properly to knowledge and creating subject-matter experts. This may be possible by creating a level of operational intelligence and understanding of data necessary to improve overall performance and efficiency. This in turn may be possible when engineers are able to visualize all related plant events by unlocking the information from sensor-generated time-series data with data analytics (Provost 2017, Usman et al. 2019, Rentschler and Shahani 2019). Four types of analytics for the workforce to train on are: • Descriptive analytics, which provide insight and key metrics to understand the operation by basic reporting. • Diagnostic analytics, which uncover the reason why an incident occurred and determine the root cause of the problem. • Predictive analytics, which use the findings from descriptive and diagnostic analytics to prediction of equipment failures to production progress, based on trends and interrelations. • Prescriptive analytics, which use all the data with insights to determine future action that organizations can take. A workforce can create and share real-time operational data and use it to analyze situations and make decisions. Decision may be for various tasks, e.g., predicting fouling of heat exchangers, yield increase through cycle-time reduction, reducing emissions by improving off-gas treatment, energy monitoring, and production quality optimization (Dick 2019).

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9.5.1  Connected Workforce The purpose of creating a connected workforce is to maximize efficiency and profitability, while minimizing operational risk (Figure 9.3). Key areas of focus include compliance, asset integrity, maintenance, operations, and capital projects. Comprehensive workforce strategies in process and asset intensive industries focus on training, safety, and a contingency plan for remote work via a distributed workforce within the Industry 4.0 approaches. A mature ‘connected workforce’ needs a wireless mesh, web meeting services, and hardware with their software and applications (Murray and Holmes 2021). Connections to data integrated with the transformation of data into actionable information is a critical step. A connected workforce can send and receive critical information at the point of work. Artificial Intelligence is additive as it helps enable anomaly detection and pattern recognition and helps set up organizations for prescriptive analytics and asset health monitoring. Beside the training, reliable access to full procedures when needed is important for complex operations and maintenance tasks (Rutkauskas 2012, Jenkins 2017). Approved mobile devices to access point of work enable organizations to execute more efficiently and unifies the board operator with the field operator (Urso 2017, Usman et al. 2019). When integrated with conduct of operations, asset performance management, engineering content management, and a competency-centered workforce through total workforce management, a connected workforce would be (Urso 2017): • Resilient, because manufacturing sectors are no longer dependent on experiential knowledge or ability of any one person. A resilient plant emerges where visualization and analytics create sustainable excellence. • Agile, because data and information are available in context whenever and wherever needed. Decisions can be made faster, and work can proceed when the right data at the right time be available to the right field person within the right context. Decisions can be made based upon data and can be communicated much faster. The outcome is a reliable, profitable, and safe plant. The core elements in this integrated data set include:

• • • • •

Management of change Procedural automation and engineering content management Competency-based culture/human factors (situational risk and awareness) Operational human-machine interface/decision support Conduct of operations/human factor issues (situational awareness/responsiveness)

Conduct of operations

Total workforce mangement

Connected workforce

Assest performance management

Engineering content management Figure 9.3.  Holistic connected workforce.

Workforce in Sustainable Engineering  375



• • • • • •

Team collaboration Safety life cycle management/safe operating limits Process safety management Mechanical integrity/asset health Production loss management Enterprise asset management

A new workforce needs information to be readily available, keeping them engaged and creating a work environment, for long-term success in industry (Usman et al. 2019, Dick 2019). The elements of a connected workforce include (Figure 9.4):

• A mobile-enabled strategy and platform • Procedural automation • Competency-based culture • A robust operations management system • An asset registry • Regulatory compliance strategy and supporting systems to manage operational risks

The following may provide a basic template for interconnection of the workforce: 1. 2. 3. 4. 5. 6.

Assess and prioritize work processes across the fleet Define benefits of automation for each work process Look for interoperability points that drive efficiency Create key performance indicators Determine stakeholder impact and have an onboarding plan Pilot concepts before planning a rollout maturity path to build a connected workforce

Analytics

Operational technology • Automation systems • Robots • Edge computing

Work area connected devices • Sensors • Local cloud

• Image analysis • Data lakes • Predictive analysis • Atrificial intelligence

Connected worker interaction modes: Workststions Mobile device Wearables

Infrastructure • GPS • Cloud • Remote centers h • Wi-Fi, Wi-Fi, bluotoot bluetooth

Wearable technology • Location devices • Wearables with Sensors sensors

Applications (software) • IT systems • Collaboration platforms • IoT applications

Figure 9.4.  Workforce interactions.

376  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence

9.6  Sustainable Engineering Curriculum with Process Intensification and Artificial Intelligence A possible curriculum for sustainable engineering education emphasizes various skills. Higher order cognitive skills enable students to create, evaluate, and analyze, while low order cognitive skills help students apply, understand, and remember. Some soft skills include being able to manage teamwork, and to be innovative and creative. The curriculum should teach fundamental knowledge in thermodynamics, heat and mass transfer, fluid mechanics, chemical kinetics, math, safety, product and process design, life cycle analysis, technoeconomic analysis, society, and sustainability. Table 9.5 shows that the design of a personal mobility system involves decisions at different stages of the design process and consideration of factors related to various disciplines (Bell et al. 2011, Allenby 2011, NAE 2018). Engineering practice and education can be improved by combining mathematical skills, domain expertise and proper communications. Models represent reality with conceptual or mathematical models or a combination of them to better understand and predict how the system works and what it produces. Students should be able to apply mathematics with core engineering principles including material and energy balances, optimization, and safety to translate the system under study to its representative equations. Such extensions of modeling, simulations, automation, advanced process control, and optimization are leading toward IIoT or Industry 4.0 for improved operation and prediction of failures before they occur. By emphasizing digital technologies, enhancing partnerships with academia, and developing new job-related approaches, industry can attract and retain required talent. Simulation tools enable workers in optimization, advanced process control, and performance monitoring, once the design is complete and becomes the digital twin of the plant. With dynamic simulation, one can see how the plant will react, in real time, to any changes one might make, including switching from winter to summer conditions, and emergency shutdown. For example, simulators for plant optimization with all the constraints can deliver improved recovery, efficiency and yield, and reduced cost of operation and maintenance. Some simulators run ‘what if’ scenarios regarding simultaneous optimization of safety, asset availability, and energy efficiency. Also, there is a move from process optimization to asset optimization, which allows workers to view the equipment in a holistic way, not just from design and operation, but also from monitoring, maintenance and reliability, as well as scale up, points of view (Ordieres-Meré et al. 2020). Table 9.5.  Design process and consideration of elements related to various disciplines (Allen et al. 2009). Discipline area

Gate-to-Gate (Design for the Environment)

Cradle-to-Grave (Life Cycle Analysis)

Inter-Industry Interactions (Industrial Ecology)

Extra-Industry Interactions (Cultural and Social)

Example Discipline

Paint type

Electric vs gasoline

Fuel or power Infrastructures

Highway design

Life Sciences

Toxic releases from painting process

Exposure to toxic materials during automobile recycling

Land requirements for different fuel types

Land-use patterns

Environmental Sciences

Air pollutant dispersion

Ore and fuel extraction

Impacts of energydelivery systems

Impervious cover, water supply

Sociology and Policy

Consumer preferences

Patterns of use by individual drivers

Energy independence

Access to services

Economics

Manufacturing costs

Material and disposal costs

Capacities of energy delivery systems

Community business and development.

Humanities and aesthetics

Color and finish

Driving performance

Land-use changes

Paved surface vs. green space

Workforce in Sustainable Engineering  377

For successful value delivery, the modeler and the end user jointly identify the following: (i) clear design problem statement and objectives, and (ii) the model’s limitations on application and prediction uncertainty. The decision on success would depend on the end use and should be supported by focused experimental data after a critical interaction between process intensification and artificial intelligence. As modeling tools and their usage vary, only effective and targeted communication between the modeler and user would deliver the value required. This emphasizes the importance of skill of communication in engineering practice (Grosjean et al. 2019). Achieving the United Nations sustainable development goals requires industry and society to develop tools and processes that work at all scales, enabling goods delivery, services, and technology to be provided to large conglomerates and remote regions. PI is a technological advance that promises to deliver a means to reach or approach these goals. The increasing number of successful commercial cases of PI highlight the importance of PI education for students in academia, industry and elsewhere (Laws and Loeber 2011). For the development of effective teaching in PI, active learning approaches should be implemented with project-based learning, problem-based learning, team-based learning, and case studies. These approaches engage students cognitively and are more likely to support higher order cognitive skills (Kiss 2016). Preparing students to join the creative and open-minded workforce requires flexibility in the university environment and learning conditions with flexibility in material, schedules, and academic tasks. PI is related to a multidisciplinary program and a PI course is important for both Po undergraduate and graduate students. Implementing PI technology includes a research phase and workforce training that require cooperative efforts in academia, industry, and certification agencies. Problem‑based learning In problem-based learning, students analyze and discuss a real problem with an expected scope and solution, defining the academic concepts to learn. The acquisition of knowledge is a focus along with a challenging problem (Rivas et al. 2020). This requires internal and external resources and the development of an execution plan to find a solution, such as investigating, acting, and engaging. In investigating, students understand the problem search of a solution method (Barth and Michelsen 2013). The acting phase is aimed at designing, implementing, and testing the proposed solutions, while the engaging phase requires students to interact with the tutor and peers to solve problems to 2014, Vega and Navarrete 2019). (Figure 9.5) (Wiek et al.

Act

Challenge Document Reflect Share

Investigate

Engage

Figure 9.5.  Cyclic phases of challenge-based learning.

Summary Sustainability is the collective willingness and ability of a society to attain its viability, vitality, and integrity over long periods of time, while not hindering other generations and societies from attaining theirs. A new workforce with special skills is necessary to achieve sustainability. Sustainability education requires the incorporation of topics in the curriculum like life cycle assessment, green

378  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence chemistry, and renewable energy and feedstocks. This may be possible through a curriculum that is inclusive of sustainability issues, to help students become active and conversant in the various dimensions of sustainability. It is particularly important that students achieve competency in system thinking in sustainability, as well as an understanding of and ability to apply sustainability indices, metrics and indicators. This chapter covers such topics as key competencies and skills in sustainability, as well as education and training for sustainable engineering, including such topics sustainable resources, sustainable processes, increased efficiency, reduced environmental and ecological impact among others. The importance of including process intensification is also covered, as is the importance of related topics in sustainability education for engineers like manufacturing, energy, cyber security, artificial intelligence, and digital transformation. The bottom line of this chapter is that the key competencies and skills in sustainability covered in this chapter need to be translated to learning skills and outcomes for curriculum development. Programs and courses designed with sustainability competencies considered require the investment of time and effort for learning about the competencies and their operationalization, and how to teach more project- and problem-based courses and to coordinate learning activities with stakeholders. The United Nations Sustainable Development Goals establish a sound framework for achieving or shifting industry and society towards sustainability and can form the basis for programs and courses designed with sustainability competencies. Process intensification is a tool that can help efforts to reach or approach the SDGs, highlighting the importance of including PI in education and training. Efforts to train faculty in the design of competence-based courses can permit adoption of a sustainability focus across a curriculum and can allow the sustainability engineer to develop not only a good understanding of systems and processes and what it takes to operate and manage them well, but also the impact systems and processes have on such sustainability and its factors (especially environmental, economic and societal sustainability).

Nomenclature AI CDR EDI ESG GHG HAZOP IIoT ICS IT LCA LCM PDR PI RAPID STEM UN-SDG

Artificial intelligence Critical design review Equity, diversity, and inclusion Environmental, social and governance Greenhouse gas Hazard and operability Industrial Internet of Things Industrial control system Information technology Life cycle assessment Life cycle model Preliminary design review Process intensification Rapid advancement in process intensification deployment Science, technology, engineering, and mathematics United Nations Sustainable Development Goal

Problems 9.1 Sustainable engineering is taught to engineers in some education programs, while it is covered in only a limited way or not at all in others. Identify the benefits to teaching sustainable engineering to engineers. Explain reasons why some programs do not cover sustainable engineering. 9.2 Conduct research to determine, for engineering textbooks written several decades ago, if sustainability is covered and to what extent.

Workforce in Sustainable Engineering  379

9.3 Process intensification is appearing more and more frequently in undergraduate engineering textbooks. If you were the instructor of an engineering course, would you cover PI? Provide reasons for your answer. 9.4 Do you favor changing the way engineering is taught by adopting a sustainability-based approach? Why? 9.5 Explain how a better understanding of sustainable development by engineering students can help improve public awareness and appreciation of sustainable development. 9.6 Explain how a better understanding and appreciation of sustainability by the public and by policy makers can help foster greater efficiency and reduced environmental impacts. 9.7 Identify government policies in the different countries that have used sustainability concepts or methods, at least in part, and summarize the key features of those policies. 9.8 Propose potential policies for governments that would support sustainable development. Describe the policies in detail, and outline for each the benefits and drawbacks.

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Index A absolute temperature 222 acetogenic bacteria 201 acidification 38, 362 acting phase 377 action-oriented research 318 active learning 356 active learning approaches 377 activity coefficient model 128, 230 actual and the ideal operations 238 adhesives 279 adiabatic columns 132 adiabatic turbine 213 adsorption by zeolite 162 adsorptive separations 141 advance electrification 250 advanced analytics 329 advanced analytics tools 370 advanced biofuels 174, 201, 202 advanced control 340, 364 advanced demand models 293 advanced digital environment 278 advanced hydroelectric dams 369 advanced manufacturing 364 advanced manufacturing processes 370 advanced manufacturing technology 146 advanced methods 362 advanced pattern recognition 289 advanced persistent threats 302 advanced process control 282, 285, 328, 376 advanced programming interface 280 advanced sensors 364 advanced technologies 330 advances technologies 369 Aesthetics 363 Affordable energy supplies 173 agile 271, 278, 291, 331, 374 agile business 335 agile development process 373 agile processes 7, 10 agility 8, 9, 272, 278, 285, 300, 314, 332, 335 Agility enabled models 272 AI platforms 273 AI technique 324 AI-assisted development 297 AI-based technologies 319 AI-enabled initiatives 269 AI-enabled technologies 275, 320

AI-powered healthcare solutions 320 AI-powered simulations 310 air separation 233 algae cultivation 149, 150, 166 algal biofuels 149 algal biomass 72, 149, 203 Alkaline electrolysis technologies 200 allocative efficiency 57 alternative responses 276 ammonia 174, 252, 253, 256 ammonia fuel storage 256 anaerobic digesters 170, 245 anaerobic digestion 151, 166, 169, 201, 203, 305 analysis capability 325 analysis of deeper-lying structures 372 analytical hierarchy process 106 analytical model building 267 analytics skills 372 analytics-enabled workforce 370 annual fuel utilization efficiency 218 annual water consumption 244 anticipatory competence 350 application security 301 appropriate optimization program 370 appropriate skills 364 aquatic acidification 38 artificial intelligence 19, 23, 126, 267, 272, 279–281, 304, 308, 317, 319, 320, 324, 327, 328, 330, 334–336, 340, 354, 355, 364, 373, 374, 377 artificial intelligence solutions 323 artificial neural network 321 Assemble the Team 368 assessment of sustainability 108 Assessments of the workbook strategy 357 asset conditions 293 asset health monitoring 338 asset integrity 333, 374 asset intensive industries 274, 374 asset life cycle 297 asset management 5, 20, 268 asset management follow 291 asset operation and management 266 Asset optimization 20, 292 Asset performance management 270, 297, 323, 333, 374 Asset performance management solutions 270 asset-intensive industries 290 assets failures 270 atmospheric acidification 38 atomic level 276

384  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence atomic level phase-changing phenomena 278 audit cybersecurity 303 automated manufacturing environment 278 automated optimization 308 automated workflow 293 automation 278, 296 automation technologies 314 autonomous laboratories 308 autonomous processes 330 autonomous tuning 327 autothermal methane reforming 173 availability 131 available energy 222 available heat 224 available recoverable heat 222 awareness 368

B battery technologies 277 Bayesian analytics 267 binary Boolean logic 321 binary power systems 201 Binary-cycle power plant 200 Bio break model 70 bio-based carbon 73 Biobased fuels 207 Bio-based hydrogen peroxide 157 bio-based materials 144 Biocatalysis 337 biochemical processes 179 biochemical routes 337 biodegradable 169 biodegradable lactic acid-based polymers 161 biodegradable material 201 biodegradable waste materials 201 biodiesel 203 biodiesel plant 238 biodiversity 65, 73, 164, 320 Biodiversity and Conservation 310 bioeconomy 11, 24, 65, 66, 72, 73, 306 bioenergy 69, 70, 201 Bioenergy conversion 255 Bioenergy systems assessment 211 bioenergy with carbon capture and sequestration 250 bioethanol 218 biofuel energy 59 biofuel fuel pathway 202 biofuel production 151, 209 biofuels 69, 177, 331, 351 biofuels compounds 276 Biogas 201 bioglycerol 171 biomass conversion 151 Biomass conversion processes 167 Biomass feedstocks 149 Biomass utilization 70 Biomass-based diesel 202 Biomass-to-liquid 202 biomaterial 144 bio-oil 202

bioplastics 144 bioproducts 153, 177 biorecycling process 151 biorefineries 69 biorefinery 70, 79, 165 Biorefinery and petrochemical industry integration 244 biorefinery plant 79 biotechnology 73, 152, 276 bit information 275 black box models 271 Blue ammonia 255 Blue hydrogen 197 bottleneck 369 Bottom-up approaches 124 Boudoir–Bell reaction 173 Brayton cycle 196 bridge fuel 254, 255 business 292 business as usual 214 business efficiency 57 business initiative 270 business value 266, 270 business-as-usual 251, 274 butanol 205

C capacitors 219 capacity factor 199 capacity lost 134 capacity utilization 288 capital cost 223, 238 capital expenditure 256 capital expenses 278 capital intensive industries 266 capital intensive operation 274 capital recovery factor 59 capstone design experience 370 carbon balance 285 carbon capture 157, 197, 286, 317, 337, 340 carbon capture system 175 carbon capture technology 340 carbon cycle 31 carbon emissions 331 carbon footprint 270, 279, 293, 319, 323 carbon neutrality 294 carbon recycling 76 Carbon tracking 33, 105 carbon-capture technology 141 carbon-free economy 74 carbon-free energy sources 253, 255 carbon-free fuel 74 career opportunities 365 Carnot cycle 195 Carnot heat engine 195 cascading effects 10 catalyst research 280 Catalytic and anti-catalytic 178 catalytic reactive distillation 308 cause-effect chains 349, 350 cellular approach 276

Index  385  cellulosic biofuel 202 cellulosic biomass 203 cement production 174 centralized security information and event management 303 certification agencies 377 chain-wide models 283 chemical absorption 162 chemical feedstock 156 chemical looping gasification 163 chemical looping systems 157, 158 chemical process developments 335 chemical processes 282 chemical reactor models 283 chemical recycling 155, 332 Chemical Safety Board 254, 359 Chemical-looping combustion 158 chemical-looping reactor 159 chemical-looping technology 158 chromatographic reactors 138 chromatographic technologies 127 circular economy 11, 61, 62, 64, 65, 70, 72, 73, 136, 148, 300, 331, 332, 334, 336 circulating fluid-bed reactors 142 Climate change 33, 35, 310, 350, 362 climate change restriction 330 climate targets 255 closed feedback loop 300 closed loop optimization 293 closed-loop Brayton cycle 220 closed-loop control 282 closed-loop decisions 285 closed-loop execution 331 closed-loop production optimization 284 cloud platform 286 cloud security 301 cloud-based electronic lab notebooks 279 cloud-based solutions 274 cloud-based storage options 279 cloud-based tools 280 cloud-ready infrastructure 275 CO2 emission factor data source 33 coal-based power and methanol production 235 coarse-grain models 278 cocurrent heat exchanger 132 coefficient of performance 217, 218, 235 cogeneration 361 cogeneration plant 196 cognitive guidance 330 cold composite curves 227 cold plasma 183 cold/hot utility 239 collaboration 5 collaboration competence 350 collaborative engineering 339 collaborative work 278 column grand composite curve 131, 230, 238, 241 column targeting tool 131, 240, 305 Combined heat and power 196 community involvement 352 competencies in sustainability 349

competitive workforce 366 complex biological system 277 complex manufacturing facilities 289 complex processes 287 complexity 276 composite curves 223, 224 Compressed air energy storage 220 computer-aided workflow 17 computerized maintenance 280 computer-supported retrosynthesis 335 concentrated solar energy 199 condenser and reboiler duties 238 condition-based maintenance 281 condition-based monitoring 297 connected workforce 336, 369, 374 conscious decision making 353 considerable recoverable heat 226 constellation of AI 266 continuously optimizing 271 contributor 156 conventional ethanol 203 conversion efficiency 339 cooperative learning 357 Co-optimization 364 corporate culture 324 corporate governance 352 corrosion monitoring 279 cost analysis 368 cost estimation 299 cost estimation insights 270 cost of maintenance 270 cost of operation 238 cost-benefit analysis 209 cost-competitive hydrogen technology 339 Cost-competitive manufacturing 288 cost-effective designs 120 cost-effective measures 213 countercurrent heat exchanger 132 course syllabus 356 cradle-to-grave assessment 362 cradle-to-grave scope 247 critical business operations 303, 368 critical design review 371 critical information 374 critical infrastructure 301 cross-training 364 Crude oil refinery operation 240 crude unit 243 cryogenic distillation 162 cryogenic distillation column 125 cryogenic liquid methane 213 cryogenic processes 224 cryogenic region 224 cryogenic temperature 220 crystallization 144 cultivating leadership 354 cultural competence 350 cultural development 357 cultural diversity 350 cultures 351 curriculum 373

386  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence curriculum development 350 curriculum for sustainable engineering 376 curriculum in engineering 370 customer expectations 274 customer experiences 269 cyber hygiene 303 cyber incident 368 cyber infrastructure 307 cyber threats 368 cyber-attacks 368 Cybercriminal 302 cybersecurity 269, 273, 292, 294, 300, 301, 318, 368 cybersecurity concerns 322 cybersecurity domains 301 cybersecurity knowledge 304, 368 cybersecurity management 303 cybersecurity practices 304, 368 cybersecurity professionals 302 cybersecurity risk 303, 319 cybersecurity technologies 302 cybersecurity training 304 cyclic distillation 138, 139 cyclic propylene carbonate 171

D Data 273, 325 data analysis 279, 320 data analysis workflows 280 data analytics 370, 373 data availability 325 data collection 369 data integrity 304 data processing 369 data quality 314, 315 data science 266, 296 data science and engineering, information 286 data security platform 302 data storage 280 data vulnerabilities 302 data-driven algorithms 324 data-driven decisions 306 data-driven models 306, 308 data-driven process modeling 307 data-driven utility operations 274 debottlenecking hydrogen distribution supply chains 340 decarbonization 59, 61, 165, 252, 253, 255, 269, 271, 274, 317, 334, 335, 368 decarbonization efforts 208 decarbonizing 157, 222 decision making 272, 304, 331, 354, 357, 372 decision-making tools 354 decision-making units 326 decision-support capabilities 331 decomposition-based solutions 179 decreasing the capital cost 223 deep learning 266 deep understanding 350 Deep-learning advanced process control 336 Deep-learning-optimized APC methods 298 defects per million opportunities 133

Definitive screening design 136 deforestation 354 degrees of freedom 124, 148 demand-supply balance 331 depolymerization 152 depreciation method 78 desalination 233 descriptive analytics 370, 373 design knowledge 288 desulfurization 141 detailed design phase 371 diabatic type 220 diagnostic analytics 370, 373 digestate 201 digital addiction 273 digital age 279 digital applications 296 digital approaches 336 digital copies of processes 287 digital divide 315 digital innovation 279 digital manufacturing sector 330 digital platform 373 digital profile 285 digital shadow 284 digital solutions 328, 334, 336, 338 digital technologies 312, 313, 315, 333, 334, 336, 338, 376 digital technology 64, 290, 317, 322, 331, 336, 338, 339, 368 digital tools 274, 27, 280, 315, 335 digital transformation 19, 267, 274, 278–280, 292, 301, 310, 312, 317, 331, 334 digital transformation initiatives 301 digital transformation strategy 269 digital twin 272, 284–287, 291, 306, 333–335, 376 digital twin approach 270 digital twin models 19, 254, 255, 272, 282, 287 digital twin platform networks 287 digital twin process models 287 digital twin solutions 339 digital twin technology 336 Digital twins and business model 287 digitalization 9, 19, 182, 251, 278, 282, 287, 290, 291, 297, 301, 312, 317, 331, 332, 335, 336 digitalization tools 373 digitization 268, 272, 274, 372 dimension of strategy 269 dimensions of sustainability 2 Dimethyl ether 206 Direct carboxylation 171 direct optimization 370 Disaster recovery 301 Disaster resiliency 310 discipline focusing 265 discounted cash flow diagrams 78 discrete software 279 dissipative structures 7 distance from global equilibrium 7 distillation 237 distillation column 130, 132, 226, 285 distillation network synthesis 148

Index  387  distillation operations 238 distillation sequence 141 distributed control system 313 distributed denial-of-service 302 distributed energy resources 268 distribution models 293 district energy 234 district energy utilities 221 diverse 85, 91 diverse backgrounds 352 diverse teams 352 diversity 4, 5, 85, 86, 91, 351–353, 367 diversity training 367 divided wall column 117 dividing-wall column 138 domain expertise 282, 331 domain knowledge 281–283 domain specific roles 286 domain-specific 265 Domestic biomass potential 211 downstream processing 278 downtime 271, 329, 336, 369 driving force 122, 132 dry fluid 201 Dry steam power plant 200 DT V-Model 284 Dual fuel injection 253 Dual-fluid systems 200 dynamic asset optimization 293 dynamic efficiency 57 dynamic integrated climate-economy 92 dynamic modeling 334 dynamic optimization 284 dynamic processes 122 dynamic simulation 334, 376

E Earth’s ecosystems 4 EcoCalculator 107 Eco-economic 2, 31, 57 eco-efficiency 6, 326 ecological balance 100 ecological cost 77 ecological efficiency 77 ecological impact 34, 362 ecological indicators 99 eco-municipality 14, 91 economic affordability 363 economic analysis 77, 230, 371 economic assessment 59 economic constraints 251 economic development 351, 357 economic dimensions 354 economic efficiency 11, 57, 326 economic evaluations 222 economic factors 354 economic feasibility 126 economic indicators 326 economic modeling 278 economic outcomes 320, 351

economic perspective 275 economic sustainability 15, 56, 57, 79 economic tradeoffs 335 ecosystem product 209 ecosystem services 15, 65 ecosystems 13, 14 ecosystems 360, 362 ecotoxicity 362 education 376 educational curricula 364 Effective energy management 176 effective teaching 356, 377 effective teaching tools 371 effective training 366 effective training program 365 efficiency 211 Electric heat pumps 235 Electric motors 217 electric power 216 electric resistance heaters 235 electric resistance heating 236 electric vehicle 250 electric work 217 electrical energy 219 electrical energy storage 219 electrical heating 235 electricity generation 234 electrification 250, 252 electrochemical capacitors 219 electrochemical reduction 182 electrochemical systems 183 electronic lab notebooks 280 electronic markets 272 elements of sustainability 349 emissions compliance 327 emissions intensity 217 empirical models 282 enabling technologies 331 endangered species 360 endurance 100 End-user education 301 energy analysis 22, 109, 174, 222, 230, 231, 236, 243, 250, 326, 354, 355 energy and exergy efficiencies 236 energy balance 222, 371 energy carriers 214, 361 Energy companies 272 energy conservation 211, 212, 214, 251, 335 Energy conservation and recovery 217 energy conservation measures 213, 251 energy consumption 251, 334 energy conversion 359 energy conversion efficiency 250 energy cost 176, 223 energy cost of utilities 223 energy crops 201 energy currencies 214 Energy Demand and Supply 293 energy density 219 energy dissipation 132 energy economics 223

388  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence Energy efficiencies of biofuels 217 energy efficiency 18, 69, 196, 208, 217, 236, 245, 289, 294, 320, 328, 355, 376 Energy efficiency ratio 218 energy efficiency standards 218, 219 energy efficient 369 Energy from solid waste 206 energy goals 250 energy industry 278 energy infrastructure 251 energy integration 11, 229 energy intensity 105 energy intensity improvements 367 energy intensive processes 130 energy intensive units 226 energy management 17, 315 energy models 293 energy policy 335 energy processes 359 energy production 194, 368 energy rate 100 energy recovery 212, 229 energy recovery practices 212 energy resource management 367 energy resources 209, 360 energy return on investment 58, 91, 209, 218, 294 energy savings 218 energy security 211 energy sources 214, 215 Energy star programs 219 energy storage 208, 221, 222, 234, 312, 362 Energy storage applications 221 Energy sustainability 249 energy target values 223 energy targets 223, 226 Energy targets for toluene hydrodealkylation process 225 Energy technology 174 Energy transfer processes 122 energy transition 65, 251, 255, 300, 311, 328 energy use sustainability 293 energy-conversion processes 207 energy-conversion technologies 215 energy-food systems 246 energy-intensive industries 146 energy-management system 367 energy-related emissions 310 energy-storage devices 222 energy-storage system 219 energy-water-food nexus 359 engaging phase 377 engineering activities 357 engineering analysis 355 engineering competencies 359, 360 engineering decisions 354 engineering design 354, 359 engineering discipline 355 engineering education 356, 357 engineering fundamentals 300 engineering processes 362 engineering sustainability 358, 360–363 engineering-based modeling 289

enhanced energy storage 209 enhanced mixing 304 enhanced resource management 361 enterprise data 270 enterprise reliability 290 enterprises 282, 287 entropic 178 entropy mapping experiments 276 entropy method 106 entropy production 18, 19, 132 environment and ecology 235 environment performance 103 environmental analyses 218 environmental bioremediation 277 environmental burden 36, 272 environmental capacity 99 environmental conditions 327 environmental governance 322 environmental impact 352, 368 environmental impact assessment 13, 107, 362 environmental impact categories 248 environmental impacts 12, 326, 362 environmental management 1, 6 environmental management and demand management 6 Environmental Management System 108 environmental problems 321 environmental protection 292, 323 environmental regulations 253, 317 environmental security 14, 90 environmental stewardship 4, 328 environmental sustainability 14, 30, 70, 141, 318, 320, 321 environmental, social and governance 352, 370 environmentally friendly 336 environmentally sustainable manufacturing 136, 295 enzymes 276 equation-based frameworks 370 equations of state 128 equipartition driving forces 119 equipartition principle 132 equipment failure 271 equitable society 62 equity 4, 6, 85, 91, 351, 353, 363 Equity and inclusion 352 Equity, diversity, and inclusion 350 Ethanol fermentation 169 Ethical AI introduces 323 ethical and social issues 273 ethical social impact 352 Ethics 352 Ethylene plants 253 event agent 299, 364, 365 event agent technology 299 event disturbance 299 event fingerprint 299 event signature 299, 365 exergoeconomic 78, 362 exergoenvironmental methods 362 exergy 19, 77, 130 exergy analyses 326 exergy analysis 234, 235, 362 exergy applications 234

Index  389  exergy balance 231 exergy cost 78 exergy cost balances 130 exergy cost theory 130 exergy criterion 101 exergy destruction number 77 exergy destructions 231 exergy efficiencies 236 exergy loss 239 exergy loss profiles 131, 230, 238, 239 exergy loss rate 231 exergy losses 232, 239 exergy methods 234, 235 Exergy methods and economics 234 exergy of a flow 231 exergy transfer 78 exothermic reaction 139, 162 expand the innovation toolkit 251 experiential learning 365 expert systems 321 extended exergy accounting 130 external ambient temperature 236 external fluctuations 10 extracting sugars 254

F failure analysis 326 failure risk 79 fatty acid methyl (or ethyl) esters 203 fatty acid methyl ester 230 fatty free 204 feasibility 121, 370 feasibility analyses of designs 350 feasibility analysis 14 feasible products 370 feedstock availability 288 feedstock flexibility 289 fermentation 151, 153, 155, 169, 203 fermentation process 205 fertilizer 201 financial risk 278 first principles 282 First principles-driven hybrid models 283 first principles-governed models 283 First-generation biofuels 201 First-generation technologies 109 first-principles models 19 Fischer-Tropsch diesel 202 Fischer-Tropsch process 202 Fischer-Tropsch synthesis 155 fit-for-purpose 275 five key technological improvements 250 Fixed bed reactors 159 flame speed 76 flame stability 256 flame-proof sinter 178 flaring 20, 254 flash steam power plant 200 flashing geothermal fluids 201 Flex-fuel engines 218

flexibility 278, 335 flow chemistry 164, 336 fluctuations 10 Fluidized bed reactors 175 Flywheel energy storage 220 Flywheels 221 Food-energy-water nexus 15, 16 fossil fuel-based power plant 195 fossil-based hydrogen 222 fossil-fuel baseline 202 fractional crystallization 172 frontline workers 274 fuel cells 216, 217, 234 fuel-fired power plants 199 full life cycle 292, 312 full-cycle 317 functional domain 120 fundamental knowledge 376 fundamentals of engineering 265, 268 fundamentals of science 265 fuzzy logic 321, 325 fuzzy logic models 320

G gasification process 157, 163 gene expression 276 genetic circuit 276 geothermal energy 200, 233 geothermal energy systems 361 geothermal heat pump 200 get ready 269 GHG emissions 208, 226 Gibbs free energy 128, 216, 231 global hydrogen economy 255 global intensification initiative 125 global participatory platform 317 global warming 34, 35, 354, 362 global warming potential 37, 105 globally integrated operations 308 Goal and scope definition 247 Gouy-Stodola theorem 18, 129 governance 5 grand composite curve 147, 227 green ammonia 74, 75, 253 green building 216 Green building certification 34 green building efforts 18 green chemistry 13, 164, 165, 355 green diesel 204, 205 green economy 15, 73 green electricity 157 green energy 109, 200 green energy engineer 369 green energy progress 371 green engineering 163, 165, 167, 369 green hydrogen 74, 75, 156, 157, 222, 312, 339, 340 Green hydrogenation processes 165 Green lean six sigma 295 Green steam crackers 168 green steel 156

390  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence greener energy sources 311 greener synthesis 337 greenhouse gas emissions 32 greenhouse gases 35 grid efficiency 322 gross domestic product 91, 354 gross national income 90, 106 group work 357

H Haber-Bosch process 156, 253 hands-on training 368 hands-on-learning 373 Hazard and operability 177 hazardous materials 177 hazardous operability 359 hazardous process 177 hazardous processes 179 Health and safety 363 Healthy ecosystems 6 heat capacity 224 heat exchanger network 229 heat exchanger network synthesis 148 heat exchanger network system 147, 148, 224, 229, 241, 243 heat exchanger train 335 heat exchanger units 223 heat exchangers 132, 225 heat integration 230, 334 heat integration by pinch analysis 147 heat integration in a biodiesel plant 229 heat losses 218 heat of combustion 157 heat pump 217, 220 heat pump assisted distillation 138 heat recovery 223 heat recovery steam generation 11 heat recovery steam generator 197, 217 heat transfer 176 heat-integrated distillation column 138 heavy-asset industries 330 Hedonic Pricing Methods 209 Henry’s law 128 heuristics 229 HiGee distillation 138 high-complexity worm 368 higher energy costs 229 higher heating value 76, 218 higher-level human input 293 high-performance computing 292, 312, 313, 339 Hopf bifurcation 7 hot and cold utilities 226 Hot geothermal brine 200 human behavior 273 human development index 9, 90, 106 human dimensions 363 human error 287 human needs 363 human resources 268, 351 human rights 4

Human-centered AI 323 human–environment systems 11 Humanity 351 human-like feelings 273 hybrid electric vehicles 221, 222 hybrid modeling 284 hybrid models 270, 281, 282, 284–286, 299, 308, 339, 340 hybrid nonreactive separation 138 hybrid processes 122 hybrid system 221 hydro deoxygenate 204 Hydro energy 199 hydro isomerization 204 hydrodealkylation process 225 Hydroelectric 198 hydrogen 255, 256 hydrogen carrier 75, 76, 156, 253 hydrogen challenges 339 hydrogen economy 75, 251, 253, 338–340 hydrogen energy 200, 234 hydrogen flames 178 hydrogen production 339 hydrogen safety 178 hydrogen storage 221 hydrogen technology 338 hydrogen value chain 338 hydrogenation reactions 161 Hydropower 198 Hydrothermal conversion 160 Hydrothermal processes 149 hydrothermal reaction 161 hypothetical molecules 280

I ideal gas law 128 Identity and access management 302 ill-advised decisions 274 impact assessment 247, 326 impact investing 5 impacts of biofuels 207 improving sustainability 362 incineration of waste 198 Incineration of waste materials 200 in-class group work 357 inclusion 4, 353, 367 Inclusive 85, 91 Inclusive communities 351 inclusive culture 4 Inclusive leaders 352 inclusive workplace 353 In-context guidance 299 in-depth understanding 277, 278 Indicator applicability 325 indicators 249 industrial applications 265 Industrial Artificial Intelligence 265 industrial control system 301, 367 industrial data strategy 269 industrial ecology 272 Industrial ecology methods 326

Index  391  industrial energy efficiency 251 industrial expertise 266 industrial incident analyses 360 industrial internet of things (IIoT) 284, 296, 365, 367, 368, 370 industrial revolutions 294 industrial sector 290, 297 industrial sustainability 17 industrial systems 247 Industry 4.0 132, 136, 290, 294, 306, 373 Industry 4.0 approach 371 Industry 4.0 approaches 374 Industry impact on education 359 industry-influenced education 359 Information 273, 276, 279, 306, 313, 315, 320, 328, 364, 368, 370, 372, 373 information and communication technology 314 information and communications technologies 319 information and thermodynamics 277 information extraction 307 information flow 275 Information processing 307 information quality 274 information receiver 275 Information security 301 information sharing 284 information technology 300, 301, 307, 364, 367, 368 information transparency 306 infrastructure 269, 369 infrastructure security 301 ingenuity 353 in-house expertise 300 innovative process modeling 281 inquiring mind 353 in‑silico exploration 306 instrumentation 279 integrated data processing 275 integrated energy sector 222 Integrated engineering tools 288 integrated gasification combined cycle gas turbines 195 integrated global design teams 288 integrated resource planning 313 integration of chemical product 370 integration of data 315 Integration of first-principles knowledge 308 integrity of digital twin 287 intellectual framework 370 intelligent systems 274 intensification 337 intensification factor 125, 226 intensification in energy analysis 222 intensification level 226 intensified biorefinery 180 intensified processes 369 intensive energy source 182 intensive properties 231 interconnected databases 320 interconnected systems 289 Intergovernmental Panel on Climate Change 14 International Organization for Standardization 108

Internet of things 20, 176 intra-generational equity 351 intuitive learners 356 inventory assessment 247 inventory management 303 investment risks 146 involve risk assessments 360 ionic liquids 171 IoT-based energy management 322 IoT-based energy management system 322 IoT-based products 284 IPAT formulation 5 irreversibility 120, 129, 131, 132, 225, 244 Issues with biofuel production 209

J job-position performance 365 just-in-time in nature 278

K key performance indicators 365 kinetics 369 kinetics of different peptide arrangements 276 knowledge creation 290 knowledge discovery 307 knowledge domain 121, 305 Knowledge sharing 5

L lactic acid fermentation 169 Land 363 large-scale data analytics 318 Lean manufacturing 108, 330 Lean six sigma 136, 137, 296 learning and teaching environment 356 learning and teaching strategies 356 learning and teaching styles 356 learning objectives 355 learning platform 373 learning styles 357 learning-based digital twin 285 levelized cost of electricity 59 levelized cost of energy 59 liabilities 353 life cycle 317, 362 life cycle analysis 6, 105, 247, 355 life cycle assessment 70, 74, 247, 355, 362 Life Cycle Costing 107 Life cycle energy efficiency 210 life cycle engineering 355 life cycle management 302 life cycle of a product 286 lifestyles 363 life-supporting systems 350 lignocellulosic biomass 201–203 lignocellulosic feedstocks 69 linear economy 61 linear programming technique 326

392  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence liquefied natural gas 233 liquid chromatography 117 Liquified natural gas 255 living systems 307 long-term remote working 317 lost work 224 lost work analysis 362 low-carbon cities 319 low-carbon economy 175 low-carbon energy 320 Low-carbon hydrogen 252 low-emission energy technology 158 lower heating value 69 low-resolution counterpart 276 low-risk 339

M machine learning 165, 266, 267, 270, 277, 280–283, 287, 291, 296, 297, 313, 324, 326–329, 372, 373 machine learning approaches 276 machine learning methods 266, 308 machine learning platforms 323 machine learning techniques 289 machine-learning prescriptive maintenance 293 Magnetic energy storage 220 maintenance 280, 297 maintenance activities 289 maintenance engineers 372 maintenance schedules 329 make-to-order manufacturing 294 Malware 302 management of sustainability 108 manufacturing environment 274 manufacturing execution system 313 manufacturing processes 370 manufacturing sector 213, 288 mass and energy balances 138, 283 mass energy densities 253 material efficiency 249 material intensity 105 materials fabrication-sustainability 324 mathematical modeling 148 mathematical principles 357 Maturity assessments 314 maximum information entropy 275 Maxwell daemon 277 Measurement 108 measurement of sustainability 105 mechanical energy 195 mechanical sensor signals 299 membrane chromatography 152 membrane network synthesis 148 Membrane separation 138 Methane dry reforming 173 methane reforming 173 methanogenic microorganisms 166 methanogens 169 Methanol economy 156 Methanol synthesis 205 Microreactors 142

Microwave processing 168 microwave units 117 microwaves 143 minimal information loss 276 minimization of entropy production 132 minimum approach temperature 147, 222, 227 minimum driving force 225 minimum entropy production 225 minimum hot and cold utilities 230 minimum number 225 minimum separation work 131 minimum total exergy flow rate 238 mixed flow reactor 143 model tuning 291 model-based approach 304 Model-based approaches 122 model-based simulation 310 model-based simulation 364 model-based systems engineering 370 Modeling 127 Modeling and simulation 127 modular approach 147, 288 modular approaches 122 modular designs 147, 288 modular manufacturing 145 modular process 127, 146 modular process intensification 146, 304 modular processes 119, 364 modular processing 146 modular teaching experience 358 modular teaching technique 355 modularly 290 modules 355 molecular ensembles 275 molecular interactions 277 molecular level simulations 276 molecular simulation 276, 277 Molten carbonate fuel cells 196 Monolith catalytic reactors 142 Monte-Carlo method 79 more-traditional designs 370 Motivation 353 moving bed reactors 138 Multi-case analysis 270, 299 multi-criteria decision matrix 67 multi-criteria decision matrixes 350 multi-criteria decision-making 105 multi-dimensional decision matrix 318 multifunctional materials 122 multifunctional units 182 multi-generation renewable energy systems 233 multi-level reactor design 124 multiple measurements 279 multi-scale design 123 multi-spectrum infrared 179 multivariate analysis-based DT approach 294 multivariate analytics 333 multi-variety coordination 287 municipal solid waste 66, 206 municipal wastewater treatment 149

Index  393  N natural capital 164 natural environment 351, 353 natural gas upgrading 255 natural language processing 321 natural resources 14, 358–360 Network security 301 Net-zero carbon 340 net-zero economy 57 Neural network model 327 neural networks 321 neutralization reaction 230 new workforce 375 Nitrogen cycle 31 non-discrimination policies 352 nonequilibrium state 7 nonequilibrium systems 275 Nonideality 128 Nonlinear programming 126 Nonrenewable energy 194 nonrenewable energy productions 197 non-renewable energy sources 360 nonrenewable natural resource 77 Normative competence 350 nuclear power plants 197 nucleation process 278

O Occupational Exposure Limits 87 Ocean health 310 off-peak electricity storage 221 off-peak hours 221 onboard energy-storage 222 on-demand power 326 on-the-ground teams 372 open-ended design problems 355 open-minded workforce 377 operating expenses 278 operating strategies 278 operation and maintenance 59 Operational digital twin models 293 operational efficiency 57, 137 operational excellence 292 operational integrity 287 operational performance 274 operational risk 272, 374 operational technology 367 optical detector 179 optimal efficiency 306 optimal heat exchanger network 224 optimal plant 271, 365 optimal system designs 297 optimization 11, 148, 235, 293, 295, 306, 336, 340, 355, 376 optimization of safety 376 optimize 292 optimize chemical process quality 287 optimize technology 253 optimized operations 298 optimized total cost 223

optimizing maintenance 290 optimizing performance 292 optimizing the key process units 278 optimum 147 optimum feed stage 230 organic Rankine cycle 196 organization’s resilience 10 organizations 336 organized structures 7 oscillatory baffle reactor 125 oscillatory flow reactors 142 Out-of-class group projects 357 out-of-spec material 271 oxidative dehydrogenation 172 oxygenated fuel 218 ozone depleting substances 38 ozone depletion 362 ozone layer 32

P parallel computing 126 Pareto efficiency 11 Pareto optimality 11 Paris Climate Agreement 14 pattern recognition 267, 307, 320 payback period 213, 218 performance 292 Performance engineering 299 performance indicators 102 Performance management 274 performance-based competency grid 365 personally identifiable information 300 Petlyuk column 140 petrochemical industries 157 phase change 224 phase change materials 220 phase equilibria 128 Phishing and social engineering 302 Photobioreactors 149 photosynthesis 218 photosynthesis process 174 Photovoltaic conversion 199 photovoltaic power 200 photovoltaics 198 photovoltaics and battery storage 221 physical quality 274 physio-chemical knowledge 283 pinch analysis 147, 223, 229, 362 pinch point 223, 225 Pinch technology 224 pinch temperature 227, 229 Plan of study 355 plant cost index 78 plant engineers 372 plant optimization 376 Plant-wide modeling and optimization 298 policy analysis of the greenhouse effect 92 policy makers 363 pollution control cost 256 Pollution rate 100

394  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence polygeneration 194, 234, 235 polylactic acid 144 polyvinyl acetate 279 population growth 6 portable training 373 potency factor 36 Potential chemical risk 105 Potential environmental impact 105 potential improvements 367 power storage 177 power transmission network 199 Power-to-X facilitates 256 practical near minimum thermodynamic condition 223 Practicing engineers 22 precise control strategy 306 predictive analysis 304, 329 predictive analytics 19, 268, 298, 310, 370, 372, 373 predictive and prescriptive analytics 289 predictive control 306 predictive maintenance 20, 281, 304, 311, 314, 317, 328, 329, 333 predictive maintenance agents 268 predictive models 165, 304, 305 Prejudice 367 preliminary design review 371 prescribing maintenance 334 prescriptive analytics 281, 292, 298, 370, 373, 374 prescriptive maintenance 254, 255, 266, 271, 287, 329 prescriptive maintenance plans 286 prescriptive maintenance solutions 336 prescriptive maintenance strategies 280 preventative action 289 preventive maintenance 280, 284, 292, 329 primary energy 214 privacy concerns 272 privacy of consumers 272 probability density function 133 problem-based courses 350 problem-based learning 377 process analytical technology 12, 323 process control 305 process design 132, 371 process development 370 process economics 124 Process flexibility analysis 124 process flow structures 297 Process heat integration 239 process industries 290 Process industry 282 process information 307 process integration 141 process integrity 274 Process intensification 13, 16, 17, 23, 119, 141, 127, 175, 180, 182, 250, 252, 276, 277, 304, 307, 308, 317, 354, 355, 364, 371, 372, 377 Process intensification principles 119 process intensification strategies 121 Process intensification techniques 122 process intensification technologies 304 Process intensification vision 118 Process operability analysis 124

Process optimization 311, 336 process safety 177, 182, 274, 371 Process simulation 326 Process synthesis 117, 122, 148 Process systems engineering 117 Processes and systems 359 processing the information 276 Produce carbon-free electricity 250 product development 127 product life cycle 295 Product safety 352 Product stewardship 104 production disruptions 274 productive efficiency 57 profitability criteria 78 project life cycle 370 project-based learning 359 Prosumers 64 proton exchange membrane 216 public health 354 Pumped energy storage 220 Pumped hydro energy storage 220 purpose-oriented machine learning 265 pyrolysis 151 pyrolysis reaction 170

Q Quality 274 quality and risk assessments 287 quality and safety 295 quality challenge 293 quality of life 6, 11, 15, 86 quality optimization 373 quantity exergy 232 quantum phenomena 276, 277 quantum realm 277

R Radiological effects 362 Rankine cycle 195 Ransomware 302 Raoult’s law 128 rapid advancement 364 rate-based model 140 Rate-based separation 128, 339 Reaction engineering 142 reaction network synthesis 148 reactive adsorption 138 reactive distillation 117, 124, 139 reactive distillation columns 138 reactive maintenance 280, 328, 329 reactive separation 139 real time 280, 313 real time adjustments 332 real time applications 297 real time capability principle 308 real time data 279, 292, 297 real time feedback 315 real time information 295, 306 Real time monitoring 322

Index  395  Real time optimization 308 Real time quality monitoring 289 real-world 333 real-world data 268 real-world effects 320 rebound effect 216, 321 reciprocating engines 217 recorded information 279 recyclable products 181 recycle 309 recycled carbon 73 recycling 294, 295 Recycling and composting programs 206 Redlich-Kwong-Soave equation of state 226 reduce carbon emissions 294 reduced energy use 340 reduced-order modeling 126 reducing energy consumption 272, 336 reference environment 231 reference-environment temperature 236 Reflux ratio 238 reforming processes 173, 179 regulatory policy 353 reinforcement learning 266 relative humidity 232 reliability 333 remote access 278 remote monitoring 328 remote operation center 322 remote work 374 remote workforce 369 renewable 369 renewable electricity 76 renewable energy 59, 60, 144, 174, 194, 198, 221, 244, 245, 308, 322, 335 renewable energy generation 221 renewable energy production 209 renewable energy sources 221, 326, 331 renewable energy systems 176, 234, 354 renewable energy technologies 109, 198 renewable feedstock 244, 355 renewable fuel standards 70 renewable hydrocarbons 175 renewable hydrogen 150, 155, 256 renewable material and energy 328 renewable power 254, 271, 322 renewable power assets 311 renewable power generation 79 renewable power plants 177 renewable resources 153, 155, 169, 174, 272 renewable transportation fuels 155 Residential energy efficiency 212 resilience 6, 9, 65, 105, 268, 272 resilience and stability 9 resilience challenge 312 resilience management 105 resilience methods 9 resilience objectives 312 resilience strategy 8, 312 resiliency 89 resilient 269, 313, 369, 374

resilient development 25 resilient systems 10 resource conservation 361 resource demands 363 resource depletion 56, 362 resource efficiency 295 resource intensity 6 resource use 359 resource utilization 361 resource wars 84 resource-efficient 216 retrofits 131, 196, 230, 238 retrofits of a plant 223 retrofitted process 230 retrofitting 177 retrofitting by column targeting tool 238 retrosynthesis 335 return-on-capital requirements 293 reverse osmosis 176, 233 reversible operation 213 Rigorous process simulation 287 risk 291 risk analysis 79 risk of operation 306 risk of process 306 risk priority 322 risk-based maintenance 281 risks for productivity 323 risk-taking ability 353 roadmap integration business 270 robotics 321 room for improvements 226, 230 root cause 271, 299, 370 rotating packed beds 122

S safe 270 safe operating window management 288 safe operations 278, 285, 335 safety 279, 288, 291, 292, 322, 325, 326, 328, 329, 331, 333, 336, 355 safety consequences 276 safety engineers 372 safety hazard 286 safety impact metric 350 safety instrumented 179 safety limits 274 safety of a plant 179 safety performance 104, 178 safety program’s 178 scale of digital twins 294 scale up 376 Seasonal Energy Efficiency Ratio 218 seasonal heat load 217 Second Law of Thermodynamics 230 Second-generation technologies 109 security 272 security controls 303 security information and event management 302 security monitoring 303

396  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence security policies 368 security solutions 302 selective data sharing 280 self-adapting 255 self-correct 276, 307 self-correcting system 275 self-driven laboratory 308 Self-driving laboratory 308 self-healing advanced process control 293 self-learning 300 self-learning AI 330 self-learning algorithms 286 self-optimizing 274 self-optimizing plant 254, 265, 268, 300 self-regulate 275 Self-reliant communities 14 Self-service analytics 370, 372 self-sustaining 255 self-tuning adaptive control 271, 278 semi-autonomous 330 semi-autonomous or autonomous systems 331 sensing learners 356 sensor-generated time-series data 373 sensors 328, 330 Sewage sludge 150 shape-selective 372 shifted operation 134 shutdown 334 side condensing or side reboiling 238 simple network management protocol 302 simulated annealing 140 simulated moving bed 138 simulation software 372 simulation techniques 369 simulator-based training 373 Six-sigma 133, 136 skills in communicating 350 Small businesses 58 smart grids 319 smart infrastructure 315 smart manufacturing technologies 364 smart sensors 279, 310 social and ethical considerations 272 social cohesion 86 social cost of carbon 92 social dimensions 351 social equity 15 social factors 359 social identity 351 social impacts 326 social inequalities 351 social investment 90 social issues 273 social resources 4 social responsibility 15, 88, 104 social responsibility standard 108 social sustainability 84, 86, 89 social welfare function 92 social well-being 353 social-ecological integrity 350

socially responsible 272 socially sustainable 85 societal impact metrics 350 societal involvement 363 societal outcomes 319 societal sustainability 15 Socio-ecological 2, 30, 85 Socio-economic 2, 57, 85 socioeconomic analysis 6 socio-economic challenges 208 sociological indicators 100 soft skills 376 solar energy 199 solar greenhouses 221 solar-based pumping 244 solar-thermal electric production 199 sophisticated data analytics 268 spatial domain 120 specific flow exergy 231 specific flow-exergy destruction 77 specific learning outcomes 350 spinning disc reactor 117 stability 10 stable operations 292 stage exergy losses 238 stage-enthalpy deficits 241 stage-gate 370 Stage-Gate approaches 370 stakeholder 13, 368, 370, 371, 373 state-of-the-art AI systems 271 static mixer 117 statistical analysis 280 steam methane reformer 252 steam methane reforming 156, 197 steam reforming reactor 159 steam tables 128 steam turbine 196 STEM education 355 Stepwise approach 270 stochastic methods 140 stochastic model 79 stochastic modeling 338 stochastic optimization 140 strategic agility 9 strategic commitment 103 strategic resilience 7, 9, 10 strategic thinking competence 350 strategically agile 10 strategy 269 structural analysis 6 structural integrity 10 structured devices 122 subject-matter experts 370 substrate 276 suitable land use 363 summative assessments 357 supercapacitors 219 superconducting 220 supercritical fluid extraction 168 supercritical state 168

Index  397  supervisory control and data acquisition 313 supply and demand 362 supply chain 8, 290, 334, 335, 340, 364 supply chain management 132 supply chain network 293 supply chain processes 332 supply chain reliability 336 support tool 273 surrogate models 271 sustainability 137, 222, 229, 279, 285, 290, 293, 297, 304, 306, 312, 318, 320, 322, 323, 328, 329, 332, 334, 336, 352, 353, 358, 369, 372, 376 sustainability analysis 319, 349, 371 sustainability aspects 287, 359 sustainability assessment 97, 98, 100, 107, 351 sustainability assessment tools 107 sustainability businesses 369 Sustainability challenges 20 sustainability commitments 4 sustainability consideration 18, 363 sustainability criteria 351 sustainability dimensions 17, 349 sustainability education 358 sustainability efforts 5, 278, 369 sustainability engineer 350 sustainability gap 290 Sustainability goals 298, 300, 331, 336 Sustainability impacts 97 sustainability index 99 sustainability indicators 97, 99 sustainability indices 102 Sustainability innovation 103 Sustainability measurement 14 sustainability metric 105 sustainability metrics 67, 104, 156, 328 sustainability milestones 294 sustainability needs 181 sustainability of designs 350 sustainability of electricity generation 195 sustainability practices 9 sustainability principles 2 sustainability problem-solving frameworks 350 sustainability research 350, 354 sustainability science 3, 290 sustainability strategies 311 sustainability strategy 4, 352 sustainability targets 282, 331, 334, 336 sustainability track record 352 sustainability transition 4, 11 sustainability values 350 sustainability-oriented corporate culture 324 sustainability-related problems 354 sustainability-related risk assessments 298 sustainable 279, 361 sustainable agriculture 156 sustainable artificial intelligence 323, 324 sustainable biofuel production 210 sustainable business 4 sustainable business model 287 sustainable business opportunities 338

sustainable catalysis 337 sustainable choices 352 sustainable cities 91 sustainable design 11, 157, 355 sustainable development 1, 24, 272, 290, 308, 309, 315, 317, 323, 351, 358, 359 Sustainable Development Goals 24, 86, 89, 105, 146, 181, 290, 309, 369 sustainable development policy 73 sustainable development scenario 249 sustainable development strategies 244 sustainable economies 58 sustainable energy 209, 256, 361 sustainable energy pathway 108 sustainable energy technology 158 sustainable engineering 11–13, 17, 89, 107, 146, 180, 349–353, 355, 358, 359, 361, 371, 372 sustainable engineering degree 354 sustainable engineering practice 369 Sustainable engineering principles 11–13 Sustainable engineering techniques 13 sustainable engineers 353 sustainable feedstocks 201 sustainable fermentation 332 Sustainable food systems 16 sustainable future 300 sustainable improvement 289 sustainable initiatives 181 sustainable investments 268 sustainable living 91 sustainable maintenance 329 sustainable manner 358, 364 sustainable manufacturing 137, 334, 359 sustainable market 370 sustainable means 355 sustainable objects 318 sustainable operation 308 sustainable pathway 337 sustainable practices 359 sustainable process implementation 355 sustainable processes 20, 361 sustainable processing 179, 308 sustainable society 350 sustainable strategies 352 sustainable systems 13 sustainable technology 146 sustainable transport 91 sustainable transportation 361 sustaining modeling 284 switchable solvents 172 synchronize 272 syngas 76 system functional review 370 system optimization 292 system robustness 7 system thinking competency 349 systematic 178 systematic approach 287 system-level transformations 306 systems-based thinking 359

398  Sustainable Engineering: Process Intensification, Energy Analysis, Artificial Intelligence T talent 369 target values 226 team projects 360 team-based learning 359, 377 technical decision making 287 technical knowledge 353 techno economic analysis 326 technoeconomic 74, 79 technoeconomic alternatives 339 techno-economic analysis 6, 78, 325, 376 technological indicators 101 temporal domain 120 thermal analysis 131 thermal efficiencies of residential furnaces 218 thermal efficiency 196 thermal energy analysis 238 thermal energy storage 219–221, 234 thermal resources 362 thermal storage 220 thermochemical 337 thermochemical conversion 157 thermochemical energy storage 220 thermochemical processes 157 thermochemical routes 183 thermodynamic analysis 129, 131, 230, 243, 305 thermodynamic cost 130 thermodynamic domain 120 thermodynamic driving forces 243 thermodynamic efficiency 238, 239, 243 thermodynamic losses 231 thermodynamic method 128, 230 thermodynamic models 127 thermodynamic optimum 19, 132 thermodynamic performances 234 thermodynamic processes 326 thermodynamic properties 277, 283 thermodynamic property database 276 thermodynamics 276, 369 thermoeconomics 77 third-generation technologies 109 throttle valve 213 throughput yield 134 toggle circuit 276 tools risk 359 top-down approaches 124 total annual cost of energy and capital costs 229 total exergy input rate 238 total information entropy 275 total intensification factor 226, 239 total intensifications 227 total quality management 133 traditional computational systems 276 traditional engineering 13 train students 369 trainee self-study 365 training 364, 367, 368 training data 297 training efficiency 365 training platforms 365

training students 369 transesterification 149, 203 transesterification reaction 230 transport phenomena 369 Traumatic and catastrophic 178 trickle bed reactors 142 Triglycerides 204 triple-bottom-line 31, 336 tropical deforestation 32 troubleshooting operations 274, 287 twin pillars of sustainable 108 two-minute breaks 357 two-stage compression process 212 types of energy 194

U ultrasonic 117 ultrasonic mixing 122 underrepresented groups 367 United Nations Development Programme 201 United Nations Environment Programme 248 United Nations Sustainable Development Goals 358, 377 Up-to-date revisions 284 user-defined computations 276 user-driven machine learning 372 utilities 223 utility load allocation method 223 utility rates 226 Utility supply optimization 294 utilization factor 196

V Value chain 338 value chain index 65 value-adjusted levelized cost of electricity 60 Value-chain management 104 value-creation chains 294 vapor efficiency 128 variance-bias tradeoffs 321 Virtual representation 306 virtual-reality training 365

W Waste 337 waste energy 360 waste heat 120 waste heat recovery 221 waste management 247 waste-management solutions 65 waste-to-energy plants 206 wastewater treatment 76, 213 water security 244 water, energy, and food systems 244 water-based coating 279 Water-energy nexus 245 Water-food systems 246 water-gas shift reaction 143 what if scenarios 376 Willingness to accept 71

Index  399  Willingness to pay 71 wind farms 199 Wind power 199 Wind power density 199 wind-based power generation 272 work environment 375 workbook 356, 357 workbook strategy 356, 358 workflow 315, 364, 291 workforce 268, 270, 272, 273, 283, 286, 288, 300, 312, 331, 333, 336, 354, 357, 364, 368, 369, 372, 373 workforce development programs 364 workforce management 374 workforce skills gap 373

workforce strategies 374 workplace 367 Workplace responsibility 88 World Energy Model 60 Worldwide policy 251

Z zero carbon operations 311 zero trust 368 Zero trust policy 303 Zero trust security strategy 303 zero-carbon fuels 251 zero-carbon pure hydrogen 157 zero-valent metal 161

About the Authors Yaşar Demirel earned a Ph.D. degree in chemical engineering from the University of Birmingham, UK in 1981. He carried out research and scholarly work at the University of Delaware and at Virginia Tech in Blacksburg as a visiting professor. He has been on the faculty of the University of Nebraska, Lincoln since 2006. He has accumulated extensive teaching and research experience over the years in diverse fields of engineering. He taught process design and optimization at VT and UNL for more than 20 years. He currently serves as a professor at the chemical and biomolecular engineering department and teaches process design and thermodynamics at UNL. He is the editorin-chief of the International Journal of Thermodynamics. He authored and co-authored two books, four book chapters, and more than 160 research papers. The fourth edition of “Nonequilibrium Thermodynamics” was published in 2019 by Elsevier. The third edition of the book titled “Energy: Production, Conversion, Storage, Conservation, and Coupling was published in 2021 by Springer. He has obtained several awards, scholarships, and presented numerous invited seminars. Marc A. Rosen, Ph.D. is a Professor at Ontario Tech University (formally University of Ontario Institute of Technology) in Oshawa, Canada, where he served as founding Dean of the Faculty of Engineering and Applied Science. Dr. Rosen has served as President of the Engineering Institute of Canada and of the Canadian Society for Mechanical Engineering. He has acted in many professional capacities, including Editor-in-Chief of various journals and a Director of Oshawa Power and Utilities Corporation. With over 70 research grants and contracts and 900 technical publications, Dr. Rosen is an active teacher and researcher in sustainable energy, sustainability, and environmental impact. Much of his research has been carried out for industry. Dr. Rosen has worked for such organizations as Imatra Power Company in Finland, Argonne National Laboratory near Chicago, the Institute for Hydrogen Systems near Toronto, and Toronto Metropolitan University (formerly Ryerson University) in Toronto, where he served as Chair of the Department of Mechanical, Aerospace and Industrial Engineering. Dr. Rosen has received numerous awards and honors, and he is a Fellow of the Royal Society of Canada, the Engineering Institute of Canada, the Canadian Academy of Engineering, the Canadian Society for Mechanical Engineering, the American Society of Mechanical Engineers, the International Energy Foundation and the Canadian Society for Senior Engineers.