Pandian Vasant, PhD, is Editor-in-Chief of the International Journal of Energy Optimization and Engineering and senior research associate at MERLIN Research Centre of Ton Duc Thang University, HCMC, Vietnam. He has 31 years of teaching experience and has co-authored over 300 publications, including research articles in journals, conference proceedings, presentations and book chapters. He has also been a guest editor for various scientific and technical journals. Gerhard-Wilhelm Weber, PhD, is a professor at Poznan University of Technology, Poznan, Poland. He received his PhD in mathematics, and economics / business administration, from RWTH Aachen. He held professorships by proxy at University of Cologne, and TU Chemnitz, Germany. J. Joshua Thomas, PhD, has been a senior lecturer at KDU Penang University College, Malaysia since 2008. He obtained his PhD in intelligent systems techniques in 2015 from University Sains Malaysia, Penang, and is an editorial board member for the International Journal of Energy Optimization and Engineering. He has also published more than 30 papers in leading international conference proceedings and peer reviewed journals. Jose A. Marmolejo Saucedo, PhD, is a professor at Pan-American University, Mexico. He received his PhD in operations research at the National Autonomous University of Mexico and has co-authored numerous research articles in scientific and scholarly journals, conference proceedings, presentations, books, and book chapters. Roman Rodriguez-Aguilar, PhD, is a professor in the School of Economic and Business Sciences of the “Universidad Panamericana” in Mexico. He received his PhD at the School of Economics at the National Polytechnic Institute, Mexico and has co-authored multiple research articles in scientific and scholarly journals, conference proceedings, presentations, and book chapters.

• Preface xvSection I: Renewable Energy 11 Artificial Intelligence for Sustainability: Opportunities and Challenges 3Amany Alshawi1.1 Introduction 31.2 History of AI for Sustainability and Smart Energy Practices 41.3 Energy and Resources Scenarios on the Global Scale 51.4 Statistical Basis of AI in Sustainability Practices 61.4.1 General Statistics 61.4.2 Environmental Stress–Based Statistics 81.4.2.1 Climate Change 91.4.2.2 Biodiversity 101.4.2.3 Deforestation 101.4.2.4 Changes in Chemistry of Oceans 101.4.2.5 Nitrogen Cycle 101.4.2.6 Water Crisis 111.4.2.7 Air Pollution 111.5 Major Challenges Faced by AI in Sustainability 111.5.1 Concentration of Wealth 111.5.2 Talent-Related and Business-Related Challenges of AI 121.5.3 Dependence on Machine Learning 141.5.4 Cybersecurity Risks 151.5.5 Carbon Footprint of AI 161.5.6 Issues in Performance Measurement 161.6 Major Opportunities of AI in Sustainability 171.6.1 AI and Water-Related Hazards Management 171.6.2 AI and Smart Cities 181.6.3 AI and Climate Change 211.6.4 AI and Environmental Sustainability 231.6.5 Impacts of AI in Transportation 241.6.6 Opportunities in Disaster Forecasting and Deforestation Forecasting 251.6.7 Opportunities in the Energy Sector 261.7 Conclusion and Future Direction 26References 272 Recent Applications of Machine Learning in Solar Energy Prediction 33N. Kapilan, R.P. Reddy and Vidhya P.2.1 Introduction 342.2 Solar Energy 342.3 AI, ML and DL 362.4 Data Preprocessing Techniques 382.5 Solar Radiation Estimation 382.6 Solar Power Prediction 432.7 Challenges and Opportunities 452.8 Future Research Directions 462.9 Conclusion 46Acknowledgement 47References 473 Mathematical Analysis on Power Generation – Part I 53G. Udhaya Sankar, C. Ganesa Moorthy and C.T. Ramasamy3.1 Introduction 543.2 Methodology for Derivations 553.3 Energy Discussions 593.4 Data Analysis 63Acknowledgement 67References 67Supplementary 694 Mathematical Analysis on Power Generation – Part II 87G. Udhaya Sankar, C. Ganesa Moorthy and C.T. Ramasamy4.1 Energy Analysis 884.2 Power Efficiency Method 894.3 Data Analysis 91Acknowledgement 96References 97Supplementary - II 1005 Sustainable Energy Materials 117G. Udhaya Sankar5.1 Introduction 1175.2 Different Methods 1195.2.1 Co-Precipitation Method 1195.2.2 Microwave-Assisted Solvothermal Method 1205.2.3 Sol-Gel Method 1205.3 X-R ay Diffraction Analysis 1205.4 FTIR Analysis 1225.5 Raman Analysis 1245.6 UV Analysis 1255.7 SEM Analysis 1275.8 Energy Dispersive X-Ray Analysis 1275.9 Thermoelectric Application 1295.9.1 Thermal Conductivity 1295.9.2 Electrical Conductivity 1315.9.3 Seebeck Coefficient 1315.9.4 Power Factor 1325.9.5 Figure of Merit 1335.10 Limitations and Future Direction 1335.11 Conclusion 133Acknowledgement 134References 1346 Soft Computing Techniques for Maximum Power Point Tracking in Wind Energy Harvesting System: A Survey 137TigiluMitikuDinku, Mukhdeep Singh Manshahia and Karanvir Singh Chahal6.1 Introduction 1376.1.1 Conventional MPPT Control Techniques 1386.2 Other MPPT Control Methods 1426.2.1 Proportional Integral Derivative Controllers 1426.2.2 Fuzzy Logic Controller 1446.2.2.1 Fuzzy Inference System 1506.2.2.2 Advantage and Disadvantages of Fuzzy Logic Controller 1516.2.3 Artificial Neural Network 1516.2.3.1 Biological Neural Networks 1526.2.3.2 Architectures of Artificial Neural Networks 1556.2.3.3 Training of Artificial Neural Networks 1576.2.3.4 Radial Basis Function 1586.2.4 Neuro-Fuzzy Inference Approach 1586.2.4.1 Adaptive Neuro-Fuzzy Approach 1616.2.4.2 Hybrid Training Algorithm 1616.3 Conclusion 167References 167Section II: Climate Change 1717 The Contribution of AI-Based Approaches in the Determination of CO2 Emission Gas Amounts of Vehicles, Determination of CO2 Emission Rates Yearly of Countries, Air Quality Measurement and Determination of Smart Electric Grids’ Stability 173Mesut Toğaçar7.1 Introduction 1747.2 Materials 1777.2.1 Classification of Air Quality Condition in Gas Concentration Measurement 1777.2.2 CO2 Emission of Vehicles 1787.2.3 Countries’ CO2 Emission Amount 1797.2.4 Stability Level in Electric Grids 1797.3 Artificial Intelligence Approaches 1817.3.1 Machine Learning Methods 1827.3.1.1 Support Vector Machine 1837.3.1.2 eXtreme Gradient Boosting (XG Boost) 1847.3.1.3 Gradient Boost 1857.3.1.4 Decision Tree 1867.3.1.5 Random Forest 1867.3.2 Deep Learning Methods 1887.3.2.1 Convolutional Neural Networks 1897.3.2.2 Long Short-Term Memory 1917.3.2.3 Bi-Directional LSTM and CNN 1927.3.2.4 Recurrent Neural Network 1937.3.3 Activation Functions 1957.3.3.1 Rectified Linear Unit 1957.3.3.2 Softmax Function 1967.4 Experimental Analysis 1967.5 Discussion 2107.6 Conclusion 211Funding 212Ethical Approval 212Conflicts of Interest 212References 2128 Performance Analysis and Effects of Dust & Temperature on Solar PV Module System by Using Multivariate Linear Regression Model 217Sumit Sharma, J. Joshua Thomas and Pandian Vasant8.1 Introduction 2188.1.1 Indian Scenario of Renewable Energy 2188.1.2 Solar Radiation at Earth 2208.1.3 Solar Photovoltaic Technologies 2208.1.3.1 Types of SPV Systems 2218.1.3.2 Types of Solar Photovoltaic Cells 2228.1.3.3 Effects of Temperature 2238.1.3.4 Conversion Efficiency 2238.1.4 Losses in PV Systems 2248.1.5 Performance of Solar Power Plants 2248.2 Literature Review 2258.3 Experimental Setup 2288.3.1 Selection of Site and Development of Experimental Facilities 2298.3.2 Methodology 2298.3.3 Experimental Instrumentation 2308.3.3.1 Solar Photovoltaic Modules 2308.3.3.2 PV Grid-Connected Inverter 2328.3.3.3 Pyranometer 2328.3.3.4 Digital Thermometer 2348.3.3.5 Lightning Arrester 2358.3.3.6 Data Acquisition System 2368.3.4 Formula Used and Sample Calculations 2368.3.5 Assumptions and Limitations 2378.4 Results Discussion 2388.4.1 Phases of Data Collection 2388.4.2 Variation in Responses Evaluated During Phase I (From 1 Jan. to 27 Feb.) of Study 2388.4.2.1 Effect of Dust and Ambient Temperature on Conversion Efficiency 2388.4.2.2 Capacity Utilization Factor and Performance Ratio 2418.4.2.3 Evaluation of MLR Model 2428.4.3 Variation in Responses Evaluated During Phase II (From 1 March to 5 April) 2468.4.3.1 Influence of Dust and Ambient Temperature on Conversion Efficiency 2468.4.3.2 Capacity Utilization Factor and Performance Ratio 2468.4.3.3 Evaluation of MLR Model 2468.4.4 Variation in Responses Evaluated During Phase III (18 May to 25 June) 2528.4.4.1 Effect of Dust and Ambient Temperature on Conversion Efficiency 2528.4.4.2 Capacity Utilization Factor and Performance Ratio 2558.4.4.3 Evaluation of MLR Model 2568.4.5 Regression Analysis for the Whole Period 2588.4.6 Best Subsets Regression: Conversion Efficiency v/s Exposure Day, Ambient Temperature 2678.4.7 Regression Outputs Summary 2688.4.8 Comparison Between Measured Efficiency and Predicted Efficiency 2688.4.9 Losses Due to Dust Accumulation 2708.4.10 Economic Analysis 2708.5 Future Research Directions 2718.6 Conclusion 271References 2729 Evaluation of In-House Compact Biogas Plant Thereby Testing Four-Stroke Single-Cylinder Diesel Engine 277Pradeep Kumar Meena, Sumit Sharma, Amit Pal and Samsher9.1 Introduction 2789.1.1 Benefits of the Use of Biogas as a Fuel in India 2789.1.2 Biogas Generators in India 2799.1.3 Biogas 2799.1.3.1 Process of Biogas Production 2809.2 Literature Review 2819.2.1 Wastes and Environment 2819.2.2 Economic and Environmental Considerations 2839.2.3 Factor Affecting Yield and Production of Biogas 2859.2.3.1 The Temperature 2859.2.3.2 PH and Buffering Systems 2879.2.3.3 C/N Ratio 2879.2.3.4 Substrate Type 2899.2.3.5 Retention Time 2899.2.3.6 Total Solids 2899.2.4 Advantages of Anaerobic Digestion to Society 2909.2.4.1 Electricity Generation 2909.2.4.2 Fertilizer Production 2909.2.4.3 Pathogen Reduction 2909.3 Methodology 2909.3.1 Set Up of Compact Biogas Plant and Equipments 2909.3.2 Assembling and Fabrication of Biogas Plant 2929.3.3 Design and Technology of Compact Biogas Plant 2949.3.4 Gas Quantity and Quality 2959.3.5 Calculation of Gas Quantity in Gas Holder 2959.4 Analysis of Compact Biogas Plant 2999.4.1 Experiment Result 2999.4.1.1 Testing on 50 Kg Animal Dung Along With 500 Ltrs Water 2999.4.1.2 Testing on Kitchen Waste 3009.4.1.3 Testing on Fruits Waste 3029.4.2 Comparison of Biogas by Different Substrate 3049.4.3 Production of Biogas Per Day at Different Waste 3049.4.4 Variation of PH Value 3079.4.5 Variation of Average pH Value 3079.4.6 Variation of Temperature 3089.4.7 Variation of Average Temperature With Respect to No. of Days for Animal Dung, Kitchen Waste, Fruits Waste and Sugar 3099.4.8 Variation of Biogas Production W.R.T. Quantity of Kitchen Waste and Fruits Waste 3119.5 Analysis of Single-Cylinder Diesel Engine on Dual Fuel 3139.5.1 Testing on 4-Stroke Single-Cylinder Diesel Engine 3139.5.2 Calculation 3169.5.3 Heat Balance Sheet 3229.5.4 Testing Result With Dual Fuel (Biogas and Diesel) on 4-Stroke Single-Cylinder Diesel Engine 3269.5.5 Calculation 3309.5.6 Heat Balance Sheet 3359.6 General Comments 3369.7 Conclusion 3399.8 Future Scope 340References 34010 Low-Temperature Combustion Technologies for Emission Reduction in Diesel Engines 345Amit Jhalani, Sumit Sharma, Pushpendra Kumar Sharma and Digambar SinghAbbreviations 34610.1 Introduction 34610.1.1 Global Scenario of Energy and Emissions 34710.1.2 Diesel Engine Emissions 34810.1.3 Mitigation of NOx and Particulate Matter 35010.1.4 Low-Temperature Combustion Engine Fuels 35010.2 Scope of the Current Article 35110.3 HCCI Technology 35210.3.1 Principle of HCCI 35310.3.2 Performance and Emissions with HCCI 35410.4 Partially Premixed Compression Ignition (PPCI) 35410.5 Exhaust Gas Recirculation (EGR) 35510.6 Reactivity Controlled Compression Ignition (RCCI) 35610.7 LTC Through Fuel Additives 35710.8 Emulsified Fuels (Water-in-Diesel Emulsion Fuel) 35810.8.1 Brake Thermal Efficiency (BTE) 35910.8.2 Nitrogen Oxide (NOx) 35910.8.3 Soot and Particulate Matter (PM) 36010.9 Conclusion and Future Scope 361Acknowledgement 361References 36111 Efficiency Optimization of Indoor Air Disinfection by Radiation Exposure for Poultry Breeding Rational for Microclimate Systems Modernization for Livestock Premises 371Dovlatov Igor Mamedjarevich and Yurochka Sergey Sergeevich11.1 Introduction 37211.2 Materials and Methods 37411.3 Results 37911.4 Discussion 38211.5 Conclusions 385References 38612 Improving the Efficiency of Photovoltaic Installations for Sustainable Development of the Urban Environment 389Pavel Kuznetsov, Leonid Yuferev and Dmitry Voronin12.1 Introduction 39012.2 Background 39212.3 Main Focus of the Chapter 40212.4 Solutions and Recommendations 417Acknowledgements 417References 41813 Monitoring System Based Micro-Controller for Biogas Digester 423Ahmed Abdelouareth and Mohamed Tamali13.1 Introduction 42313.2 Related Work 42413.3 Methods and Material 42513.3.1 Identification of Needs 42513.3.2 ADOLMS Software Setup 42513.3.3 ADOLMS Sensors 42613.3.4 ADOLMS Hardware Architecture 42813.4 Results 43013.5 Conclusion 432Acknowledgements 433References 43314 Greenhouse Gas Statistics and Methods of Combating Climate Change 435Tatyana G. KrotovaIntroduction 435Methodology 436Findings 436Conclusion 454References 455About the Editors 457Index 459

Pandian Vasant, PhD, is Editor-in-Chief of the International Journal of Energy Optimization and Engineering and senior research associate at MERLIN Research Centre of Ton Duc Thang University, HCMC, Vietnam. He has 31 years of teaching experience and has co-authored over 300 publications, including research articles in journals, conference proceedings, presentations and book chapters. He has also been a guest editor for various scientific and technical journals. Gerhard-Wilhelm Weber, PhD, is a professor at Poznan University of Technology, Poznan, Poland. He received his PhD in mathematics, and economics / business administration, from RWTH Aachen. He held professorships by proxy at University of Cologne, and TU Chemnitz, Germany. J. Joshua Thomas, PhD, has been a senior lecturer at KDU Penang University College, Malaysia since 2008. He obtained his PhD in intelligent systems techniques in 2015 from University Sains Malaysia, Penang, and is an editorial board member for the International Journal of Energy Optimization and Engineering. He has also published more than 30 papers in leading international conference proceedings and peer reviewed journals. Jose A. Marmolejo Saucedo, PhD, is a professor at Pan-American University, Mexico. He received his PhD in operations research at the National Autonomous University of Mexico and has co-authored numerous research articles in scientific and scholarly journals, conference proceedings, presentations, books, and book chapters. Roman Rodriguez-Aguilar, PhD, is a professor in the School of Economic and Business Sciences of the “Universidad Panamericana” in Mexico. He received his PhD at the School of Economics at the National Polytechnic Institute, Mexico and has co-authored multiple research articles in scientific and scholarly journals, conference proceedings, presentations, and book chapters.

• Preface xvSection I: Renewable Energy 11 Artificial Intelligence for Sustainability: Opportunities and Challenges 3Amany Alshawi1.1 Introduction 31.2 History of AI for Sustainability and Smart Energy Practices 41.3 Energy and Resources Scenarios on the Global Scale 51.4 Statistical Basis of AI in Sustainability Practices 61.4.1 General Statistics 61.4.2 Environmental Stress–Based Statistics 81.4.2.1 Climate Change 91.4.2.2 Biodiversity 101.4.2.3 Deforestation 101.4.2.4 Changes in Chemistry of Oceans 101.4.2.5 Nitrogen Cycle 101.4.2.6 Water Crisis 111.4.2.7 Air Pollution 111.5 Major Challenges Faced by AI in Sustainability 111.5.1 Concentration of Wealth 111.5.2 Talent-Related and Business-Related Challenges of AI 121.5.3 Dependence on Machine Learning 141.5.4 Cybersecurity Risks 151.5.5 Carbon Footprint of AI 161.5.6 Issues in Performance Measurement 161.6 Major Opportunities of AI in Sustainability 171.6.1 AI and Water-Related Hazards Management 171.6.2 AI and Smart Cities 181.6.3 AI and Climate Change 211.6.4 AI and Environmental Sustainability 231.6.5 Impacts of AI in Transportation 241.6.6 Opportunities in Disaster Forecasting and Deforestation Forecasting 251.6.7 Opportunities in the Energy Sector 261.7 Conclusion and Future Direction 26References 272 Recent Applications of Machine Learning in Solar Energy Prediction 33N. Kapilan, R.P. Reddy and Vidhya P.2.1 Introduction 342.2 Solar Energy 342.3 AI, ML and DL 362.4 Data Preprocessing Techniques 382.5 Solar Radiation Estimation 382.6 Solar Power Prediction 432.7 Challenges and Opportunities 452.8 Future Research Directions 462.9 Conclusion 46Acknowledgement 47References 473 Mathematical Analysis on Power Generation – Part I 53G. Udhaya Sankar, C. Ganesa Moorthy and C.T. Ramasamy3.1 Introduction 543.2 Methodology for Derivations 553.3 Energy Discussions 593.4 Data Analysis 63Acknowledgement 67References 67Supplementary 694 Mathematical Analysis on Power Generation – Part II 87G. Udhaya Sankar, C. Ganesa Moorthy and C.T. Ramasamy4.1 Energy Analysis 884.2 Power Efficiency Method 894.3 Data Analysis 91Acknowledgement 96References 97Supplementary - II 1005 Sustainable Energy Materials 117G. Udhaya Sankar5.1 Introduction 1175.2 Different Methods 1195.2.1 Co-Precipitation Method 1195.2.2 Microwave-Assisted Solvothermal Method 1205.2.3 Sol-Gel Method 1205.3 X-R ay Diffraction Analysis 1205.4 FTIR Analysis 1225.5 Raman Analysis 1245.6 UV Analysis 1255.7 SEM Analysis 1275.8 Energy Dispersive X-Ray Analysis 1275.9 Thermoelectric Application 1295.9.1 Thermal Conductivity 1295.9.2 Electrical Conductivity 1315.9.3 Seebeck Coefficient 1315.9.4 Power Factor 1325.9.5 Figure of Merit 1335.10 Limitations and Future Direction 1335.11 Conclusion 133Acknowledgement 134References 1346 Soft Computing Techniques for Maximum Power Point Tracking in Wind Energy Harvesting System: A Survey 137TigiluMitikuDinku, Mukhdeep Singh Manshahia and Karanvir Singh Chahal6.1 Introduction 1376.1.1 Conventional MPPT Control Techniques 1386.2 Other MPPT Control Methods 1426.2.1 Proportional Integral Derivative Controllers 1426.2.2 Fuzzy Logic Controller 1446.2.2.1 Fuzzy Inference System 1506.2.2.2 Advantage and Disadvantages of Fuzzy Logic Controller 1516.2.3 Artificial Neural Network 1516.2.3.1 Biological Neural Networks 1526.2.3.2 Architectures of Artificial Neural Networks 1556.2.3.3 Training of Artificial Neural Networks 1576.2.3.4 Radial Basis Function 1586.2.4 Neuro-Fuzzy Inference Approach 1586.2.4.1 Adaptive Neuro-Fuzzy Approach 1616.2.4.2 Hybrid Training Algorithm 1616.3 Conclusion 167References 167Section II: Climate Change 1717 The Contribution of AI-Based Approaches in the Determination of CO2 Emission Gas Amounts of Vehicles, Determination of CO2 Emission Rates Yearly of Countries, Air Quality Measurement and Determination of Smart Electric Grids’ Stability 173Mesut Toğaçar7.1 Introduction 1747.2 Materials 1777.2.1 Classification of Air Quality Condition in Gas Concentration Measurement 1777.2.2 CO2 Emission of Vehicles 1787.2.3 Countries’ CO2 Emission Amount 1797.2.4 Stability Level in Electric Grids 1797.3 Artificial Intelligence Approaches 1817.3.1 Machine Learning Methods 1827.3.1.1 Support Vector Machine 1837.3.1.2 eXtreme Gradient Boosting (XG Boost) 1847.3.1.3 Gradient Boost 1857.3.1.4 Decision Tree 1867.3.1.5 Random Forest 1867.3.2 Deep Learning Methods 1887.3.2.1 Convolutional Neural Networks 1897.3.2.2 Long Short-Term Memory 1917.3.2.3 Bi-Directional LSTM and CNN 1927.3.2.4 Recurrent Neural Network 1937.3.3 Activation Functions 1957.3.3.1 Rectified Linear Unit 1957.3.3.2 Softmax Function 1967.4 Experimental Analysis 1967.5 Discussion 2107.6 Conclusion 211Funding 212Ethical Approval 212Conflicts of Interest 212References 2128 Performance Analysis and Effects of Dust & Temperature on Solar PV Module System by Using Multivariate Linear Regression Model 217Sumit Sharma, J. Joshua Thomas and Pandian Vasant8.1 Introduction 2188.1.1 Indian Scenario of Renewable Energy 2188.1.2 Solar Radiation at Earth 2208.1.3 Solar Photovoltaic Technologies 2208.1.3.1 Types of SPV Systems 2218.1.3.2 Types of Solar Photovoltaic Cells 2228.1.3.3 Effects of Temperature 2238.1.3.4 Conversion Efficiency 2238.1.4 Losses in PV Systems 2248.1.5 Performance of Solar Power Plants 2248.2 Literature Review 2258.3 Experimental Setup 2288.3.1 Selection of Site and Development of Experimental Facilities 2298.3.2 Methodology 2298.3.3 Experimental Instrumentation 2308.3.3.1 Solar Photovoltaic Modules 2308.3.3.2 PV Grid-Connected Inverter 2328.3.3.3 Pyranometer 2328.3.3.4 Digital Thermometer 2348.3.3.5 Lightning Arrester 2358.3.3.6 Data Acquisition System 2368.3.4 Formula Used and Sample Calculations 2368.3.5 Assumptions and Limitations 2378.4 Results Discussion 2388.4.1 Phases of Data Collection 2388.4.2 Variation in Responses Evaluated During Phase I (From 1 Jan. to 27 Feb.) of Study 2388.4.2.1 Effect of Dust and Ambient Temperature on Conversion Efficiency 2388.4.2.2 Capacity Utilization Factor and Performance Ratio 2418.4.2.3 Evaluation of MLR Model 2428.4.3 Variation in Responses Evaluated During Phase II (From 1 March to 5 April) 2468.4.3.1 Influence of Dust and Ambient Temperature on Conversion Efficiency 2468.4.3.2 Capacity Utilization Factor and Performance Ratio 2468.4.3.3 Evaluation of MLR Model 2468.4.4 Variation in Responses Evaluated During Phase III (18 May to 25 June) 2528.4.4.1 Effect of Dust and Ambient Temperature on Conversion Efficiency 2528.4.4.2 Capacity Utilization Factor and Performance Ratio 2558.4.4.3 Evaluation of MLR Model 2568.4.5 Regression Analysis for the Whole Period 2588.4.6 Best Subsets Regression: Conversion Efficiency v/s Exposure Day, Ambient Temperature 2678.4.7 Regression Outputs Summary 2688.4.8 Comparison Between Measured Efficiency and Predicted Efficiency 2688.4.9 Losses Due to Dust Accumulation 2708.4.10 Economic Analysis 2708.5 Future Research Directions 2718.6 Conclusion 271References 2729 Evaluation of In-House Compact Biogas Plant Thereby Testing Four-Stroke Single-Cylinder Diesel Engine 277Pradeep Kumar Meena, Sumit Sharma, Amit Pal and Samsher9.1 Introduction 2789.1.1 Benefits of the Use of Biogas as a Fuel in India 2789.1.2 Biogas Generators in India 2799.1.3 Biogas 2799.1.3.1 Process of Biogas Production 2809.2 Literature Review 2819.2.1 Wastes and Environment 2819.2.2 Economic and Environmental Considerations 2839.2.3 Factor Affecting Yield and Production of Biogas 2859.2.3.1 The Temperature 2859.2.3.2 PH and Buffering Systems 2879.2.3.3 C/N Ratio 2879.2.3.4 Substrate Type 2899.2.3.5 Retention Time 2899.2.3.6 Total Solids 2899.2.4 Advantages of Anaerobic Digestion to Society 2909.2.4.1 Electricity Generation 2909.2.4.2 Fertilizer Production 2909.2.4.3 Pathogen Reduction 2909.3 Methodology 2909.3.1 Set Up of Compact Biogas Plant and Equipments 2909.3.2 Assembling and Fabrication of Biogas Plant 2929.3.3 Design and Technology of Compact Biogas Plant 2949.3.4 Gas Quantity and Quality 2959.3.5 Calculation of Gas Quantity in Gas Holder 2959.4 Analysis of Compact Biogas Plant 2999.4.1 Experiment Result 2999.4.1.1 Testing on 50 Kg Animal Dung Along With 500 Ltrs Water 2999.4.1.2 Testing on Kitchen Waste 3009.4.1.3 Testing on Fruits Waste 3029.4.2 Comparison of Biogas by Different Substrate 3049.4.3 Production of Biogas Per Day at Different Waste 3049.4.4 Variation of PH Value 3079.4.5 Variation of Average pH Value 3079.4.6 Variation of Temperature 3089.4.7 Variation of Average Temperature With Respect to No. of Days for Animal Dung, Kitchen Waste, Fruits Waste and Sugar 3099.4.8 Variation of Biogas Production W.R.T. Quantity of Kitchen Waste and Fruits Waste 3119.5 Analysis of Single-Cylinder Diesel Engine on Dual Fuel 3139.5.1 Testing on 4-Stroke Single-Cylinder Diesel Engine 3139.5.2 Calculation 3169.5.3 Heat Balance Sheet 3229.5.4 Testing Result With Dual Fuel (Biogas and Diesel) on 4-Stroke Single-Cylinder Diesel Engine 3269.5.5 Calculation 3309.5.6 Heat Balance Sheet 3359.6 General Comments 3369.7 Conclusion 3399.8 Future Scope 340References 34010 Low-Temperature Combustion Technologies for Emission Reduction in Diesel Engines 345Amit Jhalani, Sumit Sharma, Pushpendra Kumar Sharma and Digambar SinghAbbreviations 34610.1 Introduction 34610.1.1 Global Scenario of Energy and Emissions 34710.1.2 Diesel Engine Emissions 34810.1.3 Mitigation of NOx and Particulate Matter 35010.1.4 Low-Temperature Combustion Engine Fuels 35010.2 Scope of the Current Article 35110.3 HCCI Technology 35210.3.1 Principle of HCCI 35310.3.2 Performance and Emissions with HCCI 35410.4 Partially Premixed Compression Ignition (PPCI) 35410.5 Exhaust Gas Recirculation (EGR) 35510.6 Reactivity Controlled Compression Ignition (RCCI) 35610.7 LTC Through Fuel Additives 35710.8 Emulsified Fuels (Water-in-Diesel Emulsion Fuel) 35810.8.1 Brake Thermal Efficiency (BTE) 35910.8.2 Nitrogen Oxide (NOx) 35910.8.3 Soot and Particulate Matter (PM) 36010.9 Conclusion and Future Scope 361Acknowledgement 361References 36111 Efficiency Optimization of Indoor Air Disinfection by Radiation Exposure for Poultry Breeding Rational for Microclimate Systems Modernization for Livestock Premises 371Dovlatov Igor Mamedjarevich and Yurochka Sergey Sergeevich11.1 Introduction 37211.2 Materials and Methods 37411.3 Results 37911.4 Discussion 38211.5 Conclusions 385References 38612 Improving the Efficiency of Photovoltaic Installations for Sustainable Development of the Urban Environment 389Pavel Kuznetsov, Leonid Yuferev and Dmitry Voronin12.1 Introduction 39012.2 Background 39212.3 Main Focus of the Chapter 40212.4 Solutions and Recommendations 417Acknowledgements 417References 41813 Monitoring System Based Micro-Controller for Biogas Digester 423Ahmed Abdelouareth and Mohamed Tamali13.1 Introduction 42313.2 Related Work 42413.3 Methods and Material 42513.3.1 Identification of Needs 42513.3.2 ADOLMS Software Setup 42513.3.3 ADOLMS Sensors 42613.3.4 ADOLMS Hardware Architecture 42813.4 Results 43013.5 Conclusion 432Acknowledgements 433References 43314 Greenhouse Gas Statistics and Methods of Combating Climate Change 435Tatyana G. KrotovaIntroduction 435Methodology 436Findings 436Conclusion 454References 455About the Editors 457Index 459

Pandian Vasant, PhD, is Editor-in-Chief of the International Journal of Energy Optimization and Engineering and senior research associate at MERLIN Research Centre of Ton Duc Thang University, HCMC, Vietnam. He has 31 years of teaching experience and has co-authored over 300 publications, including research articles in journals, conference proceedings, presentations and book chapters. He has also been a guest editor for various scientific and technical journals. Gerhard-Wilhelm Weber, PhD, is a professor at Poznan University of Technology, Poznan, Poland. He received his PhD in mathematics, and economics / business administration, from RWTH Aachen. He held professorships by proxy at University of Cologne, and TU Chemnitz, Germany. J. Joshua Thomas, PhD, has been a senior lecturer at KDU Penang University College, Malaysia since 2008. He obtained his PhD in intelligent systems techniques in 2015 from University Sains Malaysia, Penang, and is an editorial board member for the International Journal of Energy Optimization and Engineering. He has also published more than 30 papers in leading international conference proceedings and peer reviewed journals. Jose A. Marmolejo Saucedo, PhD, is a professor at Pan-American University, Mexico. He received his PhD in operations research at the National Autonomous University of Mexico and has co-authored numerous research articles in scientific and scholarly journals, conference proceedings, presentations, books, and book chapters. Roman Rodriguez-Aguilar, PhD, is a professor in the School of Economic and Business Sciences of the “Universidad Panamericana” in Mexico. He received his PhD at the School of Economics at the National Polytechnic Institute, Mexico and has co-authored multiple research articles in scientific and scholarly journals, conference proceedings, presentations, and book chapters.

• Preface xvSection I: Renewable Energy 11 Artificial Intelligence for Sustainability: Opportunities and Challenges 3Amany Alshawi1.1 Introduction 31.2 History of AI for Sustainability and Smart Energy Practices 41.3 Energy and Resources Scenarios on the Global Scale 51.4 Statistical Basis of AI in Sustainability Practices 61.4.1 General Statistics 61.4.2 Environmental Stress–Based Statistics 81.4.2.1 Climate Change 91.4.2.2 Biodiversity 101.4.2.3 Deforestation 101.4.2.4 Changes in Chemistry of Oceans 101.4.2.5 Nitrogen Cycle 101.4.2.6 Water Crisis 111.4.2.7 Air Pollution 111.5 Major Challenges Faced by AI in Sustainability 111.5.1 Concentration of Wealth 111.5.2 Talent-Related and Business-Related Challenges of AI 121.5.3 Dependence on Machine Learning 141.5.4 Cybersecurity Risks 151.5.5 Carbon Footprint of AI 161.5.6 Issues in Performance Measurement 161.6 Major Opportunities of AI in Sustainability 171.6.1 AI and Water-Related Hazards Management 171.6.2 AI and Smart Cities 181.6.3 AI and Climate Change 211.6.4 AI and Environmental Sustainability 231.6.5 Impacts of AI in Transportation 241.6.6 Opportunities in Disaster Forecasting and Deforestation Forecasting 251.6.7 Opportunities in the Energy Sector 261.7 Conclusion and Future Direction 26References 272 Recent Applications of Machine Learning in Solar Energy Prediction 33N. Kapilan, R.P. Reddy and Vidhya P.2.1 Introduction 342.2 Solar Energy 342.3 AI, ML and DL 362.4 Data Preprocessing Techniques 382.5 Solar Radiation Estimation 382.6 Solar Power Prediction 432.7 Challenges and Opportunities 452.8 Future Research Directions 462.9 Conclusion 46Acknowledgement 47References 473 Mathematical Analysis on Power Generation – Part I 53G. Udhaya Sankar, C. Ganesa Moorthy and C.T. Ramasamy3.1 Introduction 543.2 Methodology for Derivations 553.3 Energy Discussions 593.4 Data Analysis 63Acknowledgement 67References 67Supplementary 694 Mathematical Analysis on Power Generation – Part II 87G. Udhaya Sankar, C. Ganesa Moorthy and C.T. Ramasamy4.1 Energy Analysis 884.2 Power Efficiency Method 894.3 Data Analysis 91Acknowledgement 96References 97Supplementary - II 1005 Sustainable Energy Materials 117G. Udhaya Sankar5.1 Introduction 1175.2 Different Methods 1195.2.1 Co-Precipitation Method 1195.2.2 Microwave-Assisted Solvothermal Method 1205.2.3 Sol-Gel Method 1205.3 X-R ay Diffraction Analysis 1205.4 FTIR Analysis 1225.5 Raman Analysis 1245.6 UV Analysis 1255.7 SEM Analysis 1275.8 Energy Dispersive X-Ray Analysis 1275.9 Thermoelectric Application 1295.9.1 Thermal Conductivity 1295.9.2 Electrical Conductivity 1315.9.3 Seebeck Coefficient 1315.9.4 Power Factor 1325.9.5 Figure of Merit 1335.10 Limitations and Future Direction 1335.11 Conclusion 133Acknowledgement 134References 1346 Soft Computing Techniques for Maximum Power Point Tracking in Wind Energy Harvesting System: A Survey 137TigiluMitikuDinku, Mukhdeep Singh Manshahia and Karanvir Singh Chahal6.1 Introduction 1376.1.1 Conventional MPPT Control Techniques 1386.2 Other MPPT Control Methods 1426.2.1 Proportional Integral Derivative Controllers 1426.2.2 Fuzzy Logic Controller 1446.2.2.1 Fuzzy Inference System 1506.2.2.2 Advantage and Disadvantages of Fuzzy Logic Controller 1516.2.3 Artificial Neural Network 1516.2.3.1 Biological Neural Networks 1526.2.3.2 Architectures of Artificial Neural Networks 1556.2.3.3 Training of Artificial Neural Networks 1576.2.3.4 Radial Basis Function 1586.2.4 Neuro-Fuzzy Inference Approach 1586.2.4.1 Adaptive Neuro-Fuzzy Approach 1616.2.4.2 Hybrid Training Algorithm 1616.3 Conclusion 167References 167Section II: Climate Change 1717 The Contribution of AI-Based Approaches in the Determination of CO2 Emission Gas Amounts of Vehicles, Determination of CO2 Emission Rates Yearly of Countries, Air Quality Measurement and Determination of Smart Electric Grids’ Stability 173Mesut Toğaçar7.1 Introduction 1747.2 Materials 1777.2.1 Classification of Air Quality Condition in Gas Concentration Measurement 1777.2.2 CO2 Emission of Vehicles 1787.2.3 Countries’ CO2 Emission Amount 1797.2.4 Stability Level in Electric Grids 1797.3 Artificial Intelligence Approaches 1817.3.1 Machine Learning Methods 1827.3.1.1 Support Vector Machine 1837.3.1.2 eXtreme Gradient Boosting (XG Boost) 1847.3.1.3 Gradient Boost 1857.3.1.4 Decision Tree 1867.3.1.5 Random Forest 1867.3.2 Deep Learning Methods 1887.3.2.1 Convolutional Neural Networks 1897.3.2.2 Long Short-Term Memory 1917.3.2.3 Bi-Directional LSTM and CNN 1927.3.2.4 Recurrent Neural Network 1937.3.3 Activation Functions 1957.3.3.1 Rectified Linear Unit 1957.3.3.2 Softmax Function 1967.4 Experimental Analysis 1967.5 Discussion 2107.6 Conclusion 211Funding 212Ethical Approval 212Conflicts of Interest 212References 2128 Performance Analysis and Effects of Dust & Temperature on Solar PV Module System by Using Multivariate Linear Regression Model 217Sumit Sharma, J. Joshua Thomas and Pandian Vasant8.1 Introduction 2188.1.1 Indian Scenario of Renewable Energy 2188.1.2 Solar Radiation at Earth 2208.1.3 Solar Photovoltaic Technologies 2208.1.3.1 Types of SPV Systems 2218.1.3.2 Types of Solar Photovoltaic Cells 2228.1.3.3 Effects of Temperature 2238.1.3.4 Conversion Efficiency 2238.1.4 Losses in PV Systems 2248.1.5 Performance of Solar Power Plants 2248.2 Literature Review 2258.3 Experimental Setup 2288.3.1 Selection of Site and Development of Experimental Facilities 2298.3.2 Methodology 2298.3.3 Experimental Instrumentation 2308.3.3.1 Solar Photovoltaic Modules 2308.3.3.2 PV Grid-Connected Inverter 2328.3.3.3 Pyranometer 2328.3.3.4 Digital Thermometer 2348.3.3.5 Lightning Arrester 2358.3.3.6 Data Acquisition System 2368.3.4 Formula Used and Sample Calculations 2368.3.5 Assumptions and Limitations 2378.4 Results Discussion 2388.4.1 Phases of Data Collection 2388.4.2 Variation in Responses Evaluated During Phase I (From 1 Jan. to 27 Feb.) of Study 2388.4.2.1 Effect of Dust and Ambient Temperature on Conversion Efficiency 2388.4.2.2 Capacity Utilization Factor and Performance Ratio 2418.4.2.3 Evaluation of MLR Model 2428.4.3 Variation in Responses Evaluated During Phase II (From 1 March to 5 April) 2468.4.3.1 Influence of Dust and Ambient Temperature on Conversion Efficiency 2468.4.3.2 Capacity Utilization Factor and Performance Ratio 2468.4.3.3 Evaluation of MLR Model 2468.4.4 Variation in Responses Evaluated During Phase III (18 May to 25 June) 2528.4.4.1 Effect of Dust and Ambient Temperature on Conversion Efficiency 2528.4.4.2 Capacity Utilization Factor and Performance Ratio 2558.4.4.3 Evaluation of MLR Model 2568.4.5 Regression Analysis for the Whole Period 2588.4.6 Best Subsets Regression: Conversion Efficiency v/s Exposure Day, Ambient Temperature 2678.4.7 Regression Outputs Summary 2688.4.8 Comparison Between Measured Efficiency and Predicted Efficiency 2688.4.9 Losses Due to Dust Accumulation 2708.4.10 Economic Analysis 2708.5 Future Research Directions 2718.6 Conclusion 271References 2729 Evaluation of In-House Compact Biogas Plant Thereby Testing Four-Stroke Single-Cylinder Diesel Engine 277Pradeep Kumar Meena, Sumit Sharma, Amit Pal and Samsher9.1 Introduction 2789.1.1 Benefits of the Use of Biogas as a Fuel in India 2789.1.2 Biogas Generators in India 2799.1.3 Biogas 2799.1.3.1 Process of Biogas Production 2809.2 Literature Review 2819.2.1 Wastes and Environment 2819.2.2 Economic and Environmental Considerations 2839.2.3 Factor Affecting Yield and Production of Biogas 2859.2.3.1 The Temperature 2859.2.3.2 PH and Buffering Systems 2879.2.3.3 C/N Ratio 2879.2.3.4 Substrate Type 2899.2.3.5 Retention Time 2899.2.3.6 Total Solids 2899.2.4 Advantages of Anaerobic Digestion to Society 2909.2.4.1 Electricity Generation 2909.2.4.2 Fertilizer Production 2909.2.4.3 Pathogen Reduction 2909.3 Methodology 2909.3.1 Set Up of Compact Biogas Plant and Equipments 2909.3.2 Assembling and Fabrication of Biogas Plant 2929.3.3 Design and Technology of Compact Biogas Plant 2949.3.4 Gas Quantity and Quality 2959.3.5 Calculation of Gas Quantity in Gas Holder 2959.4 Analysis of Compact Biogas Plant 2999.4.1 Experiment Result 2999.4.1.1 Testing on 50 Kg Animal Dung Along With 500 Ltrs Water 2999.4.1.2 Testing on Kitchen Waste 3009.4.1.3 Testing on Fruits Waste 3029.4.2 Comparison of Biogas by Different Substrate 3049.4.3 Production of Biogas Per Day at Different Waste 3049.4.4 Variation of PH Value 3079.4.5 Variation of Average pH Value 3079.4.6 Variation of Temperature 3089.4.7 Variation of Average Temperature With Respect to No. of Days for Animal Dung, Kitchen Waste, Fruits Waste and Sugar 3099.4.8 Variation of Biogas Production W.R.T. Quantity of Kitchen Waste and Fruits Waste 3119.5 Analysis of Single-Cylinder Diesel Engine on Dual Fuel 3139.5.1 Testing on 4-Stroke Single-Cylinder Diesel Engine 3139.5.2 Calculation 3169.5.3 Heat Balance Sheet 3229.5.4 Testing Result With Dual Fuel (Biogas and Diesel) on 4-Stroke Single-Cylinder Diesel Engine 3269.5.5 Calculation 3309.5.6 Heat Balance Sheet 3359.6 General Comments 3369.7 Conclusion 3399.8 Future Scope 340References 34010 Low-Temperature Combustion Technologies for Emission Reduction in Diesel Engines 345Amit Jhalani, Sumit Sharma, Pushpendra Kumar Sharma and Digambar SinghAbbreviations 34610.1 Introduction 34610.1.1 Global Scenario of Energy and Emissions 34710.1.2 Diesel Engine Emissions 34810.1.3 Mitigation of NOx and Particulate Matter 35010.1.4 Low-Temperature Combustion Engine Fuels 35010.2 Scope of the Current Article 35110.3 HCCI Technology 35210.3.1 Principle of HCCI 35310.3.2 Performance and Emissions with HCCI 35410.4 Partially Premixed Compression Ignition (PPCI) 35410.5 Exhaust Gas Recirculation (EGR) 35510.6 Reactivity Controlled Compression Ignition (RCCI) 35610.7 LTC Through Fuel Additives 35710.8 Emulsified Fuels (Water-in-Diesel Emulsion Fuel) 35810.8.1 Brake Thermal Efficiency (BTE) 35910.8.2 Nitrogen Oxide (NOx) 35910.8.3 Soot and Particulate Matter (PM) 36010.9 Conclusion and Future Scope 361Acknowledgement 361References 36111 Efficiency Optimization of Indoor Air Disinfection by Radiation Exposure for Poultry Breeding Rational for Microclimate Systems Modernization for Livestock Premises 371Dovlatov Igor Mamedjarevich and Yurochka Sergey Sergeevich11.1 Introduction 37211.2 Materials and Methods 37411.3 Results 37911.4 Discussion 38211.5 Conclusions 385References 38612 Improving the Efficiency of Photovoltaic Installations for Sustainable Development of the Urban Environment 389Pavel Kuznetsov, Leonid Yuferev and Dmitry Voronin12.1 Introduction 39012.2 Background 39212.3 Main Focus of the Chapter 40212.4 Solutions and Recommendations 417Acknowledgements 417References 41813 Monitoring System Based Micro-Controller for Biogas Digester 423Ahmed Abdelouareth and Mohamed Tamali13.1 Introduction 42313.2 Related Work 42413.3 Methods and Material 42513.3.1 Identification of Needs 42513.3.2 ADOLMS Software Setup 42513.3.3 ADOLMS Sensors 42613.3.4 ADOLMS Hardware Architecture 42813.4 Results 43013.5 Conclusion 432Acknowledgements 433References 43314 Greenhouse Gas Statistics and Methods of Combating Climate Change 435Tatyana G. KrotovaIntroduction 435Methodology 436Findings 436Conclusion 454References 455About the Editors 457Index 459