Artificial Intelligence for Renewable Energy and Climate Change
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portes grátis
Artificial Intelligence for Renewable Energy and Climate Change
Thomas, J. Joshua; Marmolejo-Saucedo, Jose Antonio; Weber, Gerhard-Wilhelm; Rodriguez-Aguilar, Roman; Vasant, Pandian
John Wiley & Sons Inc
08/2022
496
Dura
Inglês
9781119768999
15 a 20 dias
453
Descrição não disponível.
Preface xv
Section I: Renewable Energy 1
1 Artificial Intelligence for Sustainability: Opportunities and Challenges 3
Amany Alshawi
1.1 Introduction 3
1.2 History of AI for Sustainability and Smart Energy Practices 4
1.3 Energy and Resources Scenarios on the Global Scale 5
1.4 Statistical Basis of AI in Sustainability Practices 6
1.4.1 General Statistics 6
1.4.2 Environmental Stress-Based Statistics 8
1.4.2.1 Climate Change 9
1.4.2.2 Biodiversity 10
1.4.2.3 Deforestation 10
1.4.2.4 Changes in Chemistry of Oceans 10
1.4.2.5 Nitrogen Cycle 10
1.4.2.6 Water Crisis 11
1.4.2.7 Air Pollution 11
1.5 Major Challenges Faced by AI in Sustainability 11
1.5.1 Concentration of Wealth 11
1.5.2 Talent-Related and Business-Related Challenges of AI 12
1.5.3 Dependence on Machine Learning 14
1.5.4 Cybersecurity Risks 15
1.5.5 Carbon Footprint of AI 16
1.5.6 Issues in Performance Measurement 16
1.6 Major Opportunities of AI in Sustainability 17
1.6.1 AI and Water-Related Hazards Management 17
1.6.2 AI and Smart Cities 18
1.6.3 AI and Climate Change 21
1.6.4 AI and Environmental Sustainability 23
1.6.5 Impacts of AI in Transportation 24
1.6.6 Opportunities in Disaster Forecasting and Deforestation Forecasting 25
1.6.7 Opportunities in the Energy Sector 26
1.7 Conclusion and Future Direction 26
References 27
2 Recent Applications of Machine Learning in Solar Energy Prediction 33
N. Kapilan, R.P. Reddy and Vidhya P.
2.1 Introduction 34
2.2 Solar Energy 34
2.3 AI, ML and DL 36
2.4 Data Preprocessing Techniques 38
2.5 Solar Radiation Estimation 38
2.6 Solar Power Prediction 43
2.7 Challenges and Opportunities 45
2.8 Future Research Directions 46
2.9 Conclusion 46
Acknowledgement 47
References 47
3 Mathematical Analysis on Power Generation - Part I 53
G. Udhaya Sankar, C. Ganesa Moorthy and C.T. Ramasamy
3.1 Introduction 54
3.2 Methodology for Derivations 55
3.3 Energy Discussions 59
3.4 Data Analysis 63
Acknowledgement 67
References 67
Supplementary 69
4 Mathematical Analysis on Power Generation - Part II 87
G. Udhaya Sankar, C. Ganesa Moorthy and C.T. Ramasamy
4.1 Energy Analysis 88
4.2 Power Efficiency Method 89
4.3 Data Analysis 91
Acknowledgement 96
References 97
Supplementary - II 100
5 Sustainable Energy Materials 117
G. Udhaya Sankar
5.1 Introduction 117
5.2 Different Methods 119
5.2.1 Co-Precipitation Method 119
5.2.2 Microwave-Assisted Solvothermal Method 120
5.2.3 Sol-Gel Method 120
5.3 X-R ay Diffraction Analysis 120
5.4 FTIR Analysis 122
5.5 Raman Analysis 124
5.6 UV Analysis 125
5.7 SEM Analysis 127
5.8 Energy Dispersive X-Ray Analysis 127
5.9 Thermoelectric Application 129
5.9.1 Thermal Conductivity 129
5.9.2 Electrical Conductivity 131
5.9.3 Seebeck Coefficient 131
5.9.4 Power Factor 132
5.9.5 Figure of Merit 133
5.10 Limitations and Future Direction 133
5.11 Conclusion 133
Acknowledgement 134
References 134
6 Soft Computing Techniques for Maximum Power Point Tracking in Wind Energy Harvesting System: A Survey 137
TigiluMitikuDinku, Mukhdeep Singh Manshahia and Karanvir Singh Chahal
6.1 Introduction 137
6.1.1 Conventional MPPT Control Techniques 138
6.2 Other MPPT Control Methods 142
6.2.1 Proportional Integral Derivative Controllers 142
6.2.2 Fuzzy Logic Controller 144
6.2.2.1 Fuzzy Inference System 150
6.2.2.2 Advantage and Disadvantages of Fuzzy Logic Controller 151
6.2.3 Artificial Neural Network 151
6.2.3.1 Biological Neural Networks 152
6.2.3.2 Architectures of Artificial Neural Networks 155
6.2.3.3 Training of Artificial Neural Networks 157
6.2.3.4 Radial Basis Function 158
6.2.4 Neuro-Fuzzy Inference Approach 158
6.2.4.1 Adaptive Neuro-Fuzzy Approach 161
6.2.4.2 Hybrid Training Algorithm 161
6.3 Conclusion 167
References 167
Section II: Climate Change 171
7 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 173
Mesut Togacar
7.1 Introduction 174
7.2 Materials 177
7.2.1 Classification of Air Quality Condition in Gas Concentration Measurement 177
7.2.2 CO2 Emission of Vehicles 178
7.2.3 Countries' CO2 Emission Amount 179
7.2.4 Stability Level in Electric Grids 179
7.3 Artificial Intelligence Approaches 181
7.3.1 Machine Learning Methods 182
7.3.1.1 Support Vector Machine 183
7.3.1.2 eXtreme Gradient Boosting (XG Boost) 184
7.3.1.3 Gradient Boost 185
7.3.1.4 Decision Tree 186
7.3.1.5 Random Forest 186
7.3.2 Deep Learning Methods 188
7.3.2.1 Convolutional Neural Networks 189
7.3.2.2 Long Short-Term Memory 191
7.3.2.3 Bi-Directional LSTM and CNN 192
7.3.2.4 Recurrent Neural Network 193
7.3.3 Activation Functions 195
7.3.3.1 Rectified Linear Unit 195
7.3.3.2 Softmax Function 196
7.4 Experimental Analysis 196
7.5 Discussion 210
7.6 Conclusion 211
Funding 212
Ethical Approval 212
Conflicts of Interest 212
References 212
8 Performance Analysis and Effects of Dust & Temperature on Solar PV Module System by Using Multivariate Linear Regression Model 217
Sumit Sharma, J. Joshua Thomas and Pandian Vasant
8.1 Introduction 218
8.1.1 Indian Scenario of Renewable Energy 218
8.1.2 Solar Radiation at Earth 220
8.1.3 Solar Photovoltaic Technologies 220
8.1.3.1 Types of SPV Systems 221
8.1.3.2 Types of Solar Photovoltaic Cells 222
8.1.3.3 Effects of Temperature 223
8.1.3.4 Conversion Efficiency 223
8.1.4 Losses in PV Systems 224
8.1.5 Performance of Solar Power Plants 224
8.2 Literature Review 225
8.3 Experimental Setup 228
8.3.1 Selection of Site and Development of Experimental Facilities 229
8.3.2 Methodology 229
8.3.3 Experimental Instrumentation 230
8.3.3.1 Solar Photovoltaic Modules 230
8.3.3.2 PV Grid-Connected Inverter 232
8.3.3.3 Pyranometer 232
8.3.3.4 Digital Thermometer 234
8.3.3.5 Lightning Arrester 235
8.3.3.6 Data Acquisition System 236
8.3.4 Formula Used and Sample Calculations 236
8.3.5 Assumptions and Limitations 237
8.4 Results Discussion 238
8.4.1 Phases of Data Collection 238
8.4.2 Variation in Responses Evaluated During Phase I (From 1 Jan. to 27 Feb.) of Study 238
8.4.2.1 Effect of Dust and Ambient Temperature on Conversion Efficiency 238
8.4.2.2 Capacity Utilization Factor and Performance Ratio 241
8.4.2.3 Evaluation of MLR Model 242
8.4.3 Variation in Responses Evaluated During Phase II (From 1 March to 5 April) 246
8.4.3.1 Influence of Dust and Ambient Temperature on Conversion Efficiency 246
8.4.3.2 Capacity Utilization Factor and Performance Ratio 246
8.4.3.3 Evaluation of MLR Model 246
8.4.4 Variation in Responses Evaluated During Phase III (18 May to 25 June) 252
8.4.4.1 Effect of Dust and Ambient Temperature on Conversion Efficiency 252
8.4.4.2 Capacity Utilization Factor and Performance Ratio 255
8.4.4.3 Evaluation of MLR Model 256
8.4.5 Regression Analysis for the Whole Period 258
8.4.6 Best Subsets Regression: Conversion Efficiency v/s Exposure Day, Ambient Temperature 267
8.4.7 Regression Outputs Summary 268
8.4.8 Comparison Between Measured Efficiency and Predicted Efficiency 268
8.4.9 Losses Due to Dust Accumulation 270
8.4.10 Economic Analysis 270
8.5 Future Research Directions 271
8.6 Conclusion 271
References 272
9 Evaluation of In-House Compact Biogas Plant Thereby Testing Four-Stroke Single-Cylinder Diesel Engine 277
Pradeep Kumar Meena, Sumit Sharma, Amit Pal and Samsher
9.1 Introduction 278
9.1.1 Benefits of the Use of Biogas as a Fuel in India 278
9.1.2 Biogas Generators in India 279
9.1.3 Biogas 279
9.1.3.1 Process of Biogas Production 280
9.2 Literature Review 281
9.2.1 Wastes and Environment 281
9.2.2 Economic and Environmental Considerations 283
9.2.3 Factor Affecting Yield and Production of Biogas 285
9.2.3.1 The Temperature 285
9.2.3.2 PH and Buffering Systems 287
9.2.3.3 C/N Ratio 287
9.2.3.4 Substrate Type 289
9.2.3.5 Retention Time 289
9.2.3.6 Total Solids 289
9.2.4 Advantages of Anaerobic Digestion to Society 290
9.2.4.1 Electricity Generation 290
9.2.4.2 Fertilizer Production 290
9.2.4.3 Pathogen Reduction 290
9.3 Methodology 290
9.3.1 Set Up of Compact Biogas Plant and Equipments 290
9.3.2 Assembling and Fabrication of Biogas Plant 292
9.3.3 Design and Technology of Compact Biogas Plant 294
9.3.4 Gas Quantity and Quality 295
9.3.5 Calculation of Gas Quantity in Gas Holder 295
9.4 Analysis of Compact Biogas Plant 299
9.4.1 Experiment Result 299
9.4.1.1 Testing on 50 Kg Animal Dung Along With 500 Ltrs Water 299
9.4.1.2 Testing on Kitchen Waste 300
9.4.1.3 Testing on Fruits Waste 302
9.4.2 Comparison of Biogas by Different Substrate 304
9.4.3 Production of Biogas Per Day at Different Waste 304
9.4.4 Variation of PH Value 307
9.4.5 Variation of Average pH Value 307
9.4.6 Variation of Temperature 308
9.4.7 Variation of Average Temperature With Respect to No. of Days for Animal Dung, Kitchen Waste, Fruits Waste and Sugar 309
9.4.8 Variation of Biogas Production W.R.T. Quantity of Kitchen Waste and Fruits Waste 311
9.5 Analysis of Single-Cylinder Diesel Engine on Dual Fuel 313
9.5.1 Testing on 4-Stroke Single-Cylinder Diesel Engine 313
9.5.2 Calculation 316
9.5.3 Heat Balance Sheet 322
9.5.4 Testing Result With Dual Fuel (Biogas and Diesel) on 4-Stroke Single-Cylinder Diesel Engine 326
9.5.5 Calculation 330
9.5.6 Heat Balance Sheet 335
9.6 General Comments 336
9.7 Conclusion 339
9.8 Future Scope 340
References 340
10 Low-Temperature Combustion Technologies for Emission Reduction in Diesel Engines 345
Amit Jhalani, Sumit Sharma, Pushpendra Kumar Sharma and Digambar Singh
Abbreviations 346
10.1 Introduction 346
10.1.1 Global Scenario of Energy and Emissions 347
10.1.2 Diesel Engine Emissions 348
10.1.3 Mitigation of NOx and Particulate Matter 350
10.1.4 Low-Temperature Combustion Engine Fuels 350
10.2 Scope of the Current Article 351
10.3 HCCI Technology 352
10.3.1 Principle of HCCI 353
10.3.2 Performance and Emissions with HCCI 354
10.4 Partially Premixed Compression Ignition (PPCI) 354
10.5 Exhaust Gas Recirculation (EGR) 355
10.6 Reactivity Controlled Compression Ignition (RCCI) 356
10.7 LTC Through Fuel Additives 357
10.8 Emulsified Fuels (Water-in-Diesel Emulsion Fuel) 358
10.8.1 Brake Thermal Efficiency (BTE) 359
10.8.2 Nitrogen Oxide (NOx) 359
10.8.3 Soot and Particulate Matter (PM) 360
10.9 Conclusion and Future Scope 361
Acknowledgement 361
References 361
11 Efficiency Optimization of Indoor Air Disinfection by Radiation Exposure for Poultry Breeding Rational for Microclimate Systems Modernization for Livestock Premises 371
Dovlatov Igor Mamedjarevich and Yurochka Sergey Sergeevich
11.1 Introduction 372
11.2 Materials and Methods 374
11.3 Results 379
11.4 Discussion 382
11.5 Conclusions 385
References 386
12 Improving the Efficiency of Photovoltaic Installations for Sustainable Development of the Urban Environment 389
Pavel Kuznetsov, Leonid Yuferev and Dmitry Voronin
12.1 Introduction 390
12.2 Background 392
12.3 Main Focus of the Chapter 402
12.4 Solutions and Recommendations 417
Acknowledgements 417
References 418
13 Monitoring System Based Micro-Controller for Biogas Digester 423
Ahmed Abdelouareth and Mohamed Tamali
13.1 Introduction 423
13.2 Related Work 424
13.3 Methods and Material 425
13.3.1 Identification of Needs 425
13.3.2 ADOLMS Software Setup 425
13.3.3 ADOLMS Sensors 426
13.3.4 ADOLMS Hardware Architecture 428
13.4 Results 430
13.5 Conclusion 432
Acknowledgements 433
References 433
14 Greenhouse Gas Statistics and Methods of Combating Climate Change 435
Tatyana G. Krotova
Introduction 435
Methodology 436
Findings 436
Conclusion 454
References 455
About the Editors 457
Index 459
Section I: Renewable Energy 1
1 Artificial Intelligence for Sustainability: Opportunities and Challenges 3
Amany Alshawi
1.1 Introduction 3
1.2 History of AI for Sustainability and Smart Energy Practices 4
1.3 Energy and Resources Scenarios on the Global Scale 5
1.4 Statistical Basis of AI in Sustainability Practices 6
1.4.1 General Statistics 6
1.4.2 Environmental Stress-Based Statistics 8
1.4.2.1 Climate Change 9
1.4.2.2 Biodiversity 10
1.4.2.3 Deforestation 10
1.4.2.4 Changes in Chemistry of Oceans 10
1.4.2.5 Nitrogen Cycle 10
1.4.2.6 Water Crisis 11
1.4.2.7 Air Pollution 11
1.5 Major Challenges Faced by AI in Sustainability 11
1.5.1 Concentration of Wealth 11
1.5.2 Talent-Related and Business-Related Challenges of AI 12
1.5.3 Dependence on Machine Learning 14
1.5.4 Cybersecurity Risks 15
1.5.5 Carbon Footprint of AI 16
1.5.6 Issues in Performance Measurement 16
1.6 Major Opportunities of AI in Sustainability 17
1.6.1 AI and Water-Related Hazards Management 17
1.6.2 AI and Smart Cities 18
1.6.3 AI and Climate Change 21
1.6.4 AI and Environmental Sustainability 23
1.6.5 Impacts of AI in Transportation 24
1.6.6 Opportunities in Disaster Forecasting and Deforestation Forecasting 25
1.6.7 Opportunities in the Energy Sector 26
1.7 Conclusion and Future Direction 26
References 27
2 Recent Applications of Machine Learning in Solar Energy Prediction 33
N. Kapilan, R.P. Reddy and Vidhya P.
2.1 Introduction 34
2.2 Solar Energy 34
2.3 AI, ML and DL 36
2.4 Data Preprocessing Techniques 38
2.5 Solar Radiation Estimation 38
2.6 Solar Power Prediction 43
2.7 Challenges and Opportunities 45
2.8 Future Research Directions 46
2.9 Conclusion 46
Acknowledgement 47
References 47
3 Mathematical Analysis on Power Generation - Part I 53
G. Udhaya Sankar, C. Ganesa Moorthy and C.T. Ramasamy
3.1 Introduction 54
3.2 Methodology for Derivations 55
3.3 Energy Discussions 59
3.4 Data Analysis 63
Acknowledgement 67
References 67
Supplementary 69
4 Mathematical Analysis on Power Generation - Part II 87
G. Udhaya Sankar, C. Ganesa Moorthy and C.T. Ramasamy
4.1 Energy Analysis 88
4.2 Power Efficiency Method 89
4.3 Data Analysis 91
Acknowledgement 96
References 97
Supplementary - II 100
5 Sustainable Energy Materials 117
G. Udhaya Sankar
5.1 Introduction 117
5.2 Different Methods 119
5.2.1 Co-Precipitation Method 119
5.2.2 Microwave-Assisted Solvothermal Method 120
5.2.3 Sol-Gel Method 120
5.3 X-R ay Diffraction Analysis 120
5.4 FTIR Analysis 122
5.5 Raman Analysis 124
5.6 UV Analysis 125
5.7 SEM Analysis 127
5.8 Energy Dispersive X-Ray Analysis 127
5.9 Thermoelectric Application 129
5.9.1 Thermal Conductivity 129
5.9.2 Electrical Conductivity 131
5.9.3 Seebeck Coefficient 131
5.9.4 Power Factor 132
5.9.5 Figure of Merit 133
5.10 Limitations and Future Direction 133
5.11 Conclusion 133
Acknowledgement 134
References 134
6 Soft Computing Techniques for Maximum Power Point Tracking in Wind Energy Harvesting System: A Survey 137
TigiluMitikuDinku, Mukhdeep Singh Manshahia and Karanvir Singh Chahal
6.1 Introduction 137
6.1.1 Conventional MPPT Control Techniques 138
6.2 Other MPPT Control Methods 142
6.2.1 Proportional Integral Derivative Controllers 142
6.2.2 Fuzzy Logic Controller 144
6.2.2.1 Fuzzy Inference System 150
6.2.2.2 Advantage and Disadvantages of Fuzzy Logic Controller 151
6.2.3 Artificial Neural Network 151
6.2.3.1 Biological Neural Networks 152
6.2.3.2 Architectures of Artificial Neural Networks 155
6.2.3.3 Training of Artificial Neural Networks 157
6.2.3.4 Radial Basis Function 158
6.2.4 Neuro-Fuzzy Inference Approach 158
6.2.4.1 Adaptive Neuro-Fuzzy Approach 161
6.2.4.2 Hybrid Training Algorithm 161
6.3 Conclusion 167
References 167
Section II: Climate Change 171
7 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 173
Mesut Togacar
7.1 Introduction 174
7.2 Materials 177
7.2.1 Classification of Air Quality Condition in Gas Concentration Measurement 177
7.2.2 CO2 Emission of Vehicles 178
7.2.3 Countries' CO2 Emission Amount 179
7.2.4 Stability Level in Electric Grids 179
7.3 Artificial Intelligence Approaches 181
7.3.1 Machine Learning Methods 182
7.3.1.1 Support Vector Machine 183
7.3.1.2 eXtreme Gradient Boosting (XG Boost) 184
7.3.1.3 Gradient Boost 185
7.3.1.4 Decision Tree 186
7.3.1.5 Random Forest 186
7.3.2 Deep Learning Methods 188
7.3.2.1 Convolutional Neural Networks 189
7.3.2.2 Long Short-Term Memory 191
7.3.2.3 Bi-Directional LSTM and CNN 192
7.3.2.4 Recurrent Neural Network 193
7.3.3 Activation Functions 195
7.3.3.1 Rectified Linear Unit 195
7.3.3.2 Softmax Function 196
7.4 Experimental Analysis 196
7.5 Discussion 210
7.6 Conclusion 211
Funding 212
Ethical Approval 212
Conflicts of Interest 212
References 212
8 Performance Analysis and Effects of Dust & Temperature on Solar PV Module System by Using Multivariate Linear Regression Model 217
Sumit Sharma, J. Joshua Thomas and Pandian Vasant
8.1 Introduction 218
8.1.1 Indian Scenario of Renewable Energy 218
8.1.2 Solar Radiation at Earth 220
8.1.3 Solar Photovoltaic Technologies 220
8.1.3.1 Types of SPV Systems 221
8.1.3.2 Types of Solar Photovoltaic Cells 222
8.1.3.3 Effects of Temperature 223
8.1.3.4 Conversion Efficiency 223
8.1.4 Losses in PV Systems 224
8.1.5 Performance of Solar Power Plants 224
8.2 Literature Review 225
8.3 Experimental Setup 228
8.3.1 Selection of Site and Development of Experimental Facilities 229
8.3.2 Methodology 229
8.3.3 Experimental Instrumentation 230
8.3.3.1 Solar Photovoltaic Modules 230
8.3.3.2 PV Grid-Connected Inverter 232
8.3.3.3 Pyranometer 232
8.3.3.4 Digital Thermometer 234
8.3.3.5 Lightning Arrester 235
8.3.3.6 Data Acquisition System 236
8.3.4 Formula Used and Sample Calculations 236
8.3.5 Assumptions and Limitations 237
8.4 Results Discussion 238
8.4.1 Phases of Data Collection 238
8.4.2 Variation in Responses Evaluated During Phase I (From 1 Jan. to 27 Feb.) of Study 238
8.4.2.1 Effect of Dust and Ambient Temperature on Conversion Efficiency 238
8.4.2.2 Capacity Utilization Factor and Performance Ratio 241
8.4.2.3 Evaluation of MLR Model 242
8.4.3 Variation in Responses Evaluated During Phase II (From 1 March to 5 April) 246
8.4.3.1 Influence of Dust and Ambient Temperature on Conversion Efficiency 246
8.4.3.2 Capacity Utilization Factor and Performance Ratio 246
8.4.3.3 Evaluation of MLR Model 246
8.4.4 Variation in Responses Evaluated During Phase III (18 May to 25 June) 252
8.4.4.1 Effect of Dust and Ambient Temperature on Conversion Efficiency 252
8.4.4.2 Capacity Utilization Factor and Performance Ratio 255
8.4.4.3 Evaluation of MLR Model 256
8.4.5 Regression Analysis for the Whole Period 258
8.4.6 Best Subsets Regression: Conversion Efficiency v/s Exposure Day, Ambient Temperature 267
8.4.7 Regression Outputs Summary 268
8.4.8 Comparison Between Measured Efficiency and Predicted Efficiency 268
8.4.9 Losses Due to Dust Accumulation 270
8.4.10 Economic Analysis 270
8.5 Future Research Directions 271
8.6 Conclusion 271
References 272
9 Evaluation of In-House Compact Biogas Plant Thereby Testing Four-Stroke Single-Cylinder Diesel Engine 277
Pradeep Kumar Meena, Sumit Sharma, Amit Pal and Samsher
9.1 Introduction 278
9.1.1 Benefits of the Use of Biogas as a Fuel in India 278
9.1.2 Biogas Generators in India 279
9.1.3 Biogas 279
9.1.3.1 Process of Biogas Production 280
9.2 Literature Review 281
9.2.1 Wastes and Environment 281
9.2.2 Economic and Environmental Considerations 283
9.2.3 Factor Affecting Yield and Production of Biogas 285
9.2.3.1 The Temperature 285
9.2.3.2 PH and Buffering Systems 287
9.2.3.3 C/N Ratio 287
9.2.3.4 Substrate Type 289
9.2.3.5 Retention Time 289
9.2.3.6 Total Solids 289
9.2.4 Advantages of Anaerobic Digestion to Society 290
9.2.4.1 Electricity Generation 290
9.2.4.2 Fertilizer Production 290
9.2.4.3 Pathogen Reduction 290
9.3 Methodology 290
9.3.1 Set Up of Compact Biogas Plant and Equipments 290
9.3.2 Assembling and Fabrication of Biogas Plant 292
9.3.3 Design and Technology of Compact Biogas Plant 294
9.3.4 Gas Quantity and Quality 295
9.3.5 Calculation of Gas Quantity in Gas Holder 295
9.4 Analysis of Compact Biogas Plant 299
9.4.1 Experiment Result 299
9.4.1.1 Testing on 50 Kg Animal Dung Along With 500 Ltrs Water 299
9.4.1.2 Testing on Kitchen Waste 300
9.4.1.3 Testing on Fruits Waste 302
9.4.2 Comparison of Biogas by Different Substrate 304
9.4.3 Production of Biogas Per Day at Different Waste 304
9.4.4 Variation of PH Value 307
9.4.5 Variation of Average pH Value 307
9.4.6 Variation of Temperature 308
9.4.7 Variation of Average Temperature With Respect to No. of Days for Animal Dung, Kitchen Waste, Fruits Waste and Sugar 309
9.4.8 Variation of Biogas Production W.R.T. Quantity of Kitchen Waste and Fruits Waste 311
9.5 Analysis of Single-Cylinder Diesel Engine on Dual Fuel 313
9.5.1 Testing on 4-Stroke Single-Cylinder Diesel Engine 313
9.5.2 Calculation 316
9.5.3 Heat Balance Sheet 322
9.5.4 Testing Result With Dual Fuel (Biogas and Diesel) on 4-Stroke Single-Cylinder Diesel Engine 326
9.5.5 Calculation 330
9.5.6 Heat Balance Sheet 335
9.6 General Comments 336
9.7 Conclusion 339
9.8 Future Scope 340
References 340
10 Low-Temperature Combustion Technologies for Emission Reduction in Diesel Engines 345
Amit Jhalani, Sumit Sharma, Pushpendra Kumar Sharma and Digambar Singh
Abbreviations 346
10.1 Introduction 346
10.1.1 Global Scenario of Energy and Emissions 347
10.1.2 Diesel Engine Emissions 348
10.1.3 Mitigation of NOx and Particulate Matter 350
10.1.4 Low-Temperature Combustion Engine Fuels 350
10.2 Scope of the Current Article 351
10.3 HCCI Technology 352
10.3.1 Principle of HCCI 353
10.3.2 Performance and Emissions with HCCI 354
10.4 Partially Premixed Compression Ignition (PPCI) 354
10.5 Exhaust Gas Recirculation (EGR) 355
10.6 Reactivity Controlled Compression Ignition (RCCI) 356
10.7 LTC Through Fuel Additives 357
10.8 Emulsified Fuels (Water-in-Diesel Emulsion Fuel) 358
10.8.1 Brake Thermal Efficiency (BTE) 359
10.8.2 Nitrogen Oxide (NOx) 359
10.8.3 Soot and Particulate Matter (PM) 360
10.9 Conclusion and Future Scope 361
Acknowledgement 361
References 361
11 Efficiency Optimization of Indoor Air Disinfection by Radiation Exposure for Poultry Breeding Rational for Microclimate Systems Modernization for Livestock Premises 371
Dovlatov Igor Mamedjarevich and Yurochka Sergey Sergeevich
11.1 Introduction 372
11.2 Materials and Methods 374
11.3 Results 379
11.4 Discussion 382
11.5 Conclusions 385
References 386
12 Improving the Efficiency of Photovoltaic Installations for Sustainable Development of the Urban Environment 389
Pavel Kuznetsov, Leonid Yuferev and Dmitry Voronin
12.1 Introduction 390
12.2 Background 392
12.3 Main Focus of the Chapter 402
12.4 Solutions and Recommendations 417
Acknowledgements 417
References 418
13 Monitoring System Based Micro-Controller for Biogas Digester 423
Ahmed Abdelouareth and Mohamed Tamali
13.1 Introduction 423
13.2 Related Work 424
13.3 Methods and Material 425
13.3.1 Identification of Needs 425
13.3.2 ADOLMS Software Setup 425
13.3.3 ADOLMS Sensors 426
13.3.4 ADOLMS Hardware Architecture 428
13.4 Results 430
13.5 Conclusion 432
Acknowledgements 433
References 433
14 Greenhouse Gas Statistics and Methods of Combating Climate Change 435
Tatyana G. Krotova
Introduction 435
Methodology 436
Findings 436
Conclusion 454
References 455
About the Editors 457
Index 459
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Artificial intelligence; Machine learning; Deep learning; Metaheuristic algorithms; Hydropower; Renewable electricity; Solar PV; Bio power; Geothermal power; Ocean power; Wind power; Biogas; Hydrogen; Global warming; Climate change; Renewable energy; Hybrid technology; CO2 minimization; Evolutionary algorithms; Swarm intelligence; Computational intelligence; Soft computing; Operational research; Data mining; Hybrid optimization; Bioenergy recycling; Biofuel supply chains; Energy management policy; Energy efficiency; Energy-saving technology; Small hydropower plants; Thermal treatments; Remote sensing; Optimization theory; Optimal control theory; Stochastic optimal control theory; Big Data Analytics; IoT; Cloud Computing; Block Chain; 5G Technology; Data Technology; Industry 4.0; Smart Technology; Deep Neural Network; Fuzzy Logic; Evolutionary Algorithm; Ant Optimization; Financial Technology; Drone Technology; Robotics; Smart Energy; Smart farming
Preface xv
Section I: Renewable Energy 1
1 Artificial Intelligence for Sustainability: Opportunities and Challenges 3
Amany Alshawi
1.1 Introduction 3
1.2 History of AI for Sustainability and Smart Energy Practices 4
1.3 Energy and Resources Scenarios on the Global Scale 5
1.4 Statistical Basis of AI in Sustainability Practices 6
1.4.1 General Statistics 6
1.4.2 Environmental Stress-Based Statistics 8
1.4.2.1 Climate Change 9
1.4.2.2 Biodiversity 10
1.4.2.3 Deforestation 10
1.4.2.4 Changes in Chemistry of Oceans 10
1.4.2.5 Nitrogen Cycle 10
1.4.2.6 Water Crisis 11
1.4.2.7 Air Pollution 11
1.5 Major Challenges Faced by AI in Sustainability 11
1.5.1 Concentration of Wealth 11
1.5.2 Talent-Related and Business-Related Challenges of AI 12
1.5.3 Dependence on Machine Learning 14
1.5.4 Cybersecurity Risks 15
1.5.5 Carbon Footprint of AI 16
1.5.6 Issues in Performance Measurement 16
1.6 Major Opportunities of AI in Sustainability 17
1.6.1 AI and Water-Related Hazards Management 17
1.6.2 AI and Smart Cities 18
1.6.3 AI and Climate Change 21
1.6.4 AI and Environmental Sustainability 23
1.6.5 Impacts of AI in Transportation 24
1.6.6 Opportunities in Disaster Forecasting and Deforestation Forecasting 25
1.6.7 Opportunities in the Energy Sector 26
1.7 Conclusion and Future Direction 26
References 27
2 Recent Applications of Machine Learning in Solar Energy Prediction 33
N. Kapilan, R.P. Reddy and Vidhya P.
2.1 Introduction 34
2.2 Solar Energy 34
2.3 AI, ML and DL 36
2.4 Data Preprocessing Techniques 38
2.5 Solar Radiation Estimation 38
2.6 Solar Power Prediction 43
2.7 Challenges and Opportunities 45
2.8 Future Research Directions 46
2.9 Conclusion 46
Acknowledgement 47
References 47
3 Mathematical Analysis on Power Generation - Part I 53
G. Udhaya Sankar, C. Ganesa Moorthy and C.T. Ramasamy
3.1 Introduction 54
3.2 Methodology for Derivations 55
3.3 Energy Discussions 59
3.4 Data Analysis 63
Acknowledgement 67
References 67
Supplementary 69
4 Mathematical Analysis on Power Generation - Part II 87
G. Udhaya Sankar, C. Ganesa Moorthy and C.T. Ramasamy
4.1 Energy Analysis 88
4.2 Power Efficiency Method 89
4.3 Data Analysis 91
Acknowledgement 96
References 97
Supplementary - II 100
5 Sustainable Energy Materials 117
G. Udhaya Sankar
5.1 Introduction 117
5.2 Different Methods 119
5.2.1 Co-Precipitation Method 119
5.2.2 Microwave-Assisted Solvothermal Method 120
5.2.3 Sol-Gel Method 120
5.3 X-R ay Diffraction Analysis 120
5.4 FTIR Analysis 122
5.5 Raman Analysis 124
5.6 UV Analysis 125
5.7 SEM Analysis 127
5.8 Energy Dispersive X-Ray Analysis 127
5.9 Thermoelectric Application 129
5.9.1 Thermal Conductivity 129
5.9.2 Electrical Conductivity 131
5.9.3 Seebeck Coefficient 131
5.9.4 Power Factor 132
5.9.5 Figure of Merit 133
5.10 Limitations and Future Direction 133
5.11 Conclusion 133
Acknowledgement 134
References 134
6 Soft Computing Techniques for Maximum Power Point Tracking in Wind Energy Harvesting System: A Survey 137
TigiluMitikuDinku, Mukhdeep Singh Manshahia and Karanvir Singh Chahal
6.1 Introduction 137
6.1.1 Conventional MPPT Control Techniques 138
6.2 Other MPPT Control Methods 142
6.2.1 Proportional Integral Derivative Controllers 142
6.2.2 Fuzzy Logic Controller 144
6.2.2.1 Fuzzy Inference System 150
6.2.2.2 Advantage and Disadvantages of Fuzzy Logic Controller 151
6.2.3 Artificial Neural Network 151
6.2.3.1 Biological Neural Networks 152
6.2.3.2 Architectures of Artificial Neural Networks 155
6.2.3.3 Training of Artificial Neural Networks 157
6.2.3.4 Radial Basis Function 158
6.2.4 Neuro-Fuzzy Inference Approach 158
6.2.4.1 Adaptive Neuro-Fuzzy Approach 161
6.2.4.2 Hybrid Training Algorithm 161
6.3 Conclusion 167
References 167
Section II: Climate Change 171
7 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 173
Mesut Togacar
7.1 Introduction 174
7.2 Materials 177
7.2.1 Classification of Air Quality Condition in Gas Concentration Measurement 177
7.2.2 CO2 Emission of Vehicles 178
7.2.3 Countries' CO2 Emission Amount 179
7.2.4 Stability Level in Electric Grids 179
7.3 Artificial Intelligence Approaches 181
7.3.1 Machine Learning Methods 182
7.3.1.1 Support Vector Machine 183
7.3.1.2 eXtreme Gradient Boosting (XG Boost) 184
7.3.1.3 Gradient Boost 185
7.3.1.4 Decision Tree 186
7.3.1.5 Random Forest 186
7.3.2 Deep Learning Methods 188
7.3.2.1 Convolutional Neural Networks 189
7.3.2.2 Long Short-Term Memory 191
7.3.2.3 Bi-Directional LSTM and CNN 192
7.3.2.4 Recurrent Neural Network 193
7.3.3 Activation Functions 195
7.3.3.1 Rectified Linear Unit 195
7.3.3.2 Softmax Function 196
7.4 Experimental Analysis 196
7.5 Discussion 210
7.6 Conclusion 211
Funding 212
Ethical Approval 212
Conflicts of Interest 212
References 212
8 Performance Analysis and Effects of Dust & Temperature on Solar PV Module System by Using Multivariate Linear Regression Model 217
Sumit Sharma, J. Joshua Thomas and Pandian Vasant
8.1 Introduction 218
8.1.1 Indian Scenario of Renewable Energy 218
8.1.2 Solar Radiation at Earth 220
8.1.3 Solar Photovoltaic Technologies 220
8.1.3.1 Types of SPV Systems 221
8.1.3.2 Types of Solar Photovoltaic Cells 222
8.1.3.3 Effects of Temperature 223
8.1.3.4 Conversion Efficiency 223
8.1.4 Losses in PV Systems 224
8.1.5 Performance of Solar Power Plants 224
8.2 Literature Review 225
8.3 Experimental Setup 228
8.3.1 Selection of Site and Development of Experimental Facilities 229
8.3.2 Methodology 229
8.3.3 Experimental Instrumentation 230
8.3.3.1 Solar Photovoltaic Modules 230
8.3.3.2 PV Grid-Connected Inverter 232
8.3.3.3 Pyranometer 232
8.3.3.4 Digital Thermometer 234
8.3.3.5 Lightning Arrester 235
8.3.3.6 Data Acquisition System 236
8.3.4 Formula Used and Sample Calculations 236
8.3.5 Assumptions and Limitations 237
8.4 Results Discussion 238
8.4.1 Phases of Data Collection 238
8.4.2 Variation in Responses Evaluated During Phase I (From 1 Jan. to 27 Feb.) of Study 238
8.4.2.1 Effect of Dust and Ambient Temperature on Conversion Efficiency 238
8.4.2.2 Capacity Utilization Factor and Performance Ratio 241
8.4.2.3 Evaluation of MLR Model 242
8.4.3 Variation in Responses Evaluated During Phase II (From 1 March to 5 April) 246
8.4.3.1 Influence of Dust and Ambient Temperature on Conversion Efficiency 246
8.4.3.2 Capacity Utilization Factor and Performance Ratio 246
8.4.3.3 Evaluation of MLR Model 246
8.4.4 Variation in Responses Evaluated During Phase III (18 May to 25 June) 252
8.4.4.1 Effect of Dust and Ambient Temperature on Conversion Efficiency 252
8.4.4.2 Capacity Utilization Factor and Performance Ratio 255
8.4.4.3 Evaluation of MLR Model 256
8.4.5 Regression Analysis for the Whole Period 258
8.4.6 Best Subsets Regression: Conversion Efficiency v/s Exposure Day, Ambient Temperature 267
8.4.7 Regression Outputs Summary 268
8.4.8 Comparison Between Measured Efficiency and Predicted Efficiency 268
8.4.9 Losses Due to Dust Accumulation 270
8.4.10 Economic Analysis 270
8.5 Future Research Directions 271
8.6 Conclusion 271
References 272
9 Evaluation of In-House Compact Biogas Plant Thereby Testing Four-Stroke Single-Cylinder Diesel Engine 277
Pradeep Kumar Meena, Sumit Sharma, Amit Pal and Samsher
9.1 Introduction 278
9.1.1 Benefits of the Use of Biogas as a Fuel in India 278
9.1.2 Biogas Generators in India 279
9.1.3 Biogas 279
9.1.3.1 Process of Biogas Production 280
9.2 Literature Review 281
9.2.1 Wastes and Environment 281
9.2.2 Economic and Environmental Considerations 283
9.2.3 Factor Affecting Yield and Production of Biogas 285
9.2.3.1 The Temperature 285
9.2.3.2 PH and Buffering Systems 287
9.2.3.3 C/N Ratio 287
9.2.3.4 Substrate Type 289
9.2.3.5 Retention Time 289
9.2.3.6 Total Solids 289
9.2.4 Advantages of Anaerobic Digestion to Society 290
9.2.4.1 Electricity Generation 290
9.2.4.2 Fertilizer Production 290
9.2.4.3 Pathogen Reduction 290
9.3 Methodology 290
9.3.1 Set Up of Compact Biogas Plant and Equipments 290
9.3.2 Assembling and Fabrication of Biogas Plant 292
9.3.3 Design and Technology of Compact Biogas Plant 294
9.3.4 Gas Quantity and Quality 295
9.3.5 Calculation of Gas Quantity in Gas Holder 295
9.4 Analysis of Compact Biogas Plant 299
9.4.1 Experiment Result 299
9.4.1.1 Testing on 50 Kg Animal Dung Along With 500 Ltrs Water 299
9.4.1.2 Testing on Kitchen Waste 300
9.4.1.3 Testing on Fruits Waste 302
9.4.2 Comparison of Biogas by Different Substrate 304
9.4.3 Production of Biogas Per Day at Different Waste 304
9.4.4 Variation of PH Value 307
9.4.5 Variation of Average pH Value 307
9.4.6 Variation of Temperature 308
9.4.7 Variation of Average Temperature With Respect to No. of Days for Animal Dung, Kitchen Waste, Fruits Waste and Sugar 309
9.4.8 Variation of Biogas Production W.R.T. Quantity of Kitchen Waste and Fruits Waste 311
9.5 Analysis of Single-Cylinder Diesel Engine on Dual Fuel 313
9.5.1 Testing on 4-Stroke Single-Cylinder Diesel Engine 313
9.5.2 Calculation 316
9.5.3 Heat Balance Sheet 322
9.5.4 Testing Result With Dual Fuel (Biogas and Diesel) on 4-Stroke Single-Cylinder Diesel Engine 326
9.5.5 Calculation 330
9.5.6 Heat Balance Sheet 335
9.6 General Comments 336
9.7 Conclusion 339
9.8 Future Scope 340
References 340
10 Low-Temperature Combustion Technologies for Emission Reduction in Diesel Engines 345
Amit Jhalani, Sumit Sharma, Pushpendra Kumar Sharma and Digambar Singh
Abbreviations 346
10.1 Introduction 346
10.1.1 Global Scenario of Energy and Emissions 347
10.1.2 Diesel Engine Emissions 348
10.1.3 Mitigation of NOx and Particulate Matter 350
10.1.4 Low-Temperature Combustion Engine Fuels 350
10.2 Scope of the Current Article 351
10.3 HCCI Technology 352
10.3.1 Principle of HCCI 353
10.3.2 Performance and Emissions with HCCI 354
10.4 Partially Premixed Compression Ignition (PPCI) 354
10.5 Exhaust Gas Recirculation (EGR) 355
10.6 Reactivity Controlled Compression Ignition (RCCI) 356
10.7 LTC Through Fuel Additives 357
10.8 Emulsified Fuels (Water-in-Diesel Emulsion Fuel) 358
10.8.1 Brake Thermal Efficiency (BTE) 359
10.8.2 Nitrogen Oxide (NOx) 359
10.8.3 Soot and Particulate Matter (PM) 360
10.9 Conclusion and Future Scope 361
Acknowledgement 361
References 361
11 Efficiency Optimization of Indoor Air Disinfection by Radiation Exposure for Poultry Breeding Rational for Microclimate Systems Modernization for Livestock Premises 371
Dovlatov Igor Mamedjarevich and Yurochka Sergey Sergeevich
11.1 Introduction 372
11.2 Materials and Methods 374
11.3 Results 379
11.4 Discussion 382
11.5 Conclusions 385
References 386
12 Improving the Efficiency of Photovoltaic Installations for Sustainable Development of the Urban Environment 389
Pavel Kuznetsov, Leonid Yuferev and Dmitry Voronin
12.1 Introduction 390
12.2 Background 392
12.3 Main Focus of the Chapter 402
12.4 Solutions and Recommendations 417
Acknowledgements 417
References 418
13 Monitoring System Based Micro-Controller for Biogas Digester 423
Ahmed Abdelouareth and Mohamed Tamali
13.1 Introduction 423
13.2 Related Work 424
13.3 Methods and Material 425
13.3.1 Identification of Needs 425
13.3.2 ADOLMS Software Setup 425
13.3.3 ADOLMS Sensors 426
13.3.4 ADOLMS Hardware Architecture 428
13.4 Results 430
13.5 Conclusion 432
Acknowledgements 433
References 433
14 Greenhouse Gas Statistics and Methods of Combating Climate Change 435
Tatyana G. Krotova
Introduction 435
Methodology 436
Findings 436
Conclusion 454
References 455
About the Editors 457
Index 459
Section I: Renewable Energy 1
1 Artificial Intelligence for Sustainability: Opportunities and Challenges 3
Amany Alshawi
1.1 Introduction 3
1.2 History of AI for Sustainability and Smart Energy Practices 4
1.3 Energy and Resources Scenarios on the Global Scale 5
1.4 Statistical Basis of AI in Sustainability Practices 6
1.4.1 General Statistics 6
1.4.2 Environmental Stress-Based Statistics 8
1.4.2.1 Climate Change 9
1.4.2.2 Biodiversity 10
1.4.2.3 Deforestation 10
1.4.2.4 Changes in Chemistry of Oceans 10
1.4.2.5 Nitrogen Cycle 10
1.4.2.6 Water Crisis 11
1.4.2.7 Air Pollution 11
1.5 Major Challenges Faced by AI in Sustainability 11
1.5.1 Concentration of Wealth 11
1.5.2 Talent-Related and Business-Related Challenges of AI 12
1.5.3 Dependence on Machine Learning 14
1.5.4 Cybersecurity Risks 15
1.5.5 Carbon Footprint of AI 16
1.5.6 Issues in Performance Measurement 16
1.6 Major Opportunities of AI in Sustainability 17
1.6.1 AI and Water-Related Hazards Management 17
1.6.2 AI and Smart Cities 18
1.6.3 AI and Climate Change 21
1.6.4 AI and Environmental Sustainability 23
1.6.5 Impacts of AI in Transportation 24
1.6.6 Opportunities in Disaster Forecasting and Deforestation Forecasting 25
1.6.7 Opportunities in the Energy Sector 26
1.7 Conclusion and Future Direction 26
References 27
2 Recent Applications of Machine Learning in Solar Energy Prediction 33
N. Kapilan, R.P. Reddy and Vidhya P.
2.1 Introduction 34
2.2 Solar Energy 34
2.3 AI, ML and DL 36
2.4 Data Preprocessing Techniques 38
2.5 Solar Radiation Estimation 38
2.6 Solar Power Prediction 43
2.7 Challenges and Opportunities 45
2.8 Future Research Directions 46
2.9 Conclusion 46
Acknowledgement 47
References 47
3 Mathematical Analysis on Power Generation - Part I 53
G. Udhaya Sankar, C. Ganesa Moorthy and C.T. Ramasamy
3.1 Introduction 54
3.2 Methodology for Derivations 55
3.3 Energy Discussions 59
3.4 Data Analysis 63
Acknowledgement 67
References 67
Supplementary 69
4 Mathematical Analysis on Power Generation - Part II 87
G. Udhaya Sankar, C. Ganesa Moorthy and C.T. Ramasamy
4.1 Energy Analysis 88
4.2 Power Efficiency Method 89
4.3 Data Analysis 91
Acknowledgement 96
References 97
Supplementary - II 100
5 Sustainable Energy Materials 117
G. Udhaya Sankar
5.1 Introduction 117
5.2 Different Methods 119
5.2.1 Co-Precipitation Method 119
5.2.2 Microwave-Assisted Solvothermal Method 120
5.2.3 Sol-Gel Method 120
5.3 X-R ay Diffraction Analysis 120
5.4 FTIR Analysis 122
5.5 Raman Analysis 124
5.6 UV Analysis 125
5.7 SEM Analysis 127
5.8 Energy Dispersive X-Ray Analysis 127
5.9 Thermoelectric Application 129
5.9.1 Thermal Conductivity 129
5.9.2 Electrical Conductivity 131
5.9.3 Seebeck Coefficient 131
5.9.4 Power Factor 132
5.9.5 Figure of Merit 133
5.10 Limitations and Future Direction 133
5.11 Conclusion 133
Acknowledgement 134
References 134
6 Soft Computing Techniques for Maximum Power Point Tracking in Wind Energy Harvesting System: A Survey 137
TigiluMitikuDinku, Mukhdeep Singh Manshahia and Karanvir Singh Chahal
6.1 Introduction 137
6.1.1 Conventional MPPT Control Techniques 138
6.2 Other MPPT Control Methods 142
6.2.1 Proportional Integral Derivative Controllers 142
6.2.2 Fuzzy Logic Controller 144
6.2.2.1 Fuzzy Inference System 150
6.2.2.2 Advantage and Disadvantages of Fuzzy Logic Controller 151
6.2.3 Artificial Neural Network 151
6.2.3.1 Biological Neural Networks 152
6.2.3.2 Architectures of Artificial Neural Networks 155
6.2.3.3 Training of Artificial Neural Networks 157
6.2.3.4 Radial Basis Function 158
6.2.4 Neuro-Fuzzy Inference Approach 158
6.2.4.1 Adaptive Neuro-Fuzzy Approach 161
6.2.4.2 Hybrid Training Algorithm 161
6.3 Conclusion 167
References 167
Section II: Climate Change 171
7 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 173
Mesut Togacar
7.1 Introduction 174
7.2 Materials 177
7.2.1 Classification of Air Quality Condition in Gas Concentration Measurement 177
7.2.2 CO2 Emission of Vehicles 178
7.2.3 Countries' CO2 Emission Amount 179
7.2.4 Stability Level in Electric Grids 179
7.3 Artificial Intelligence Approaches 181
7.3.1 Machine Learning Methods 182
7.3.1.1 Support Vector Machine 183
7.3.1.2 eXtreme Gradient Boosting (XG Boost) 184
7.3.1.3 Gradient Boost 185
7.3.1.4 Decision Tree 186
7.3.1.5 Random Forest 186
7.3.2 Deep Learning Methods 188
7.3.2.1 Convolutional Neural Networks 189
7.3.2.2 Long Short-Term Memory 191
7.3.2.3 Bi-Directional LSTM and CNN 192
7.3.2.4 Recurrent Neural Network 193
7.3.3 Activation Functions 195
7.3.3.1 Rectified Linear Unit 195
7.3.3.2 Softmax Function 196
7.4 Experimental Analysis 196
7.5 Discussion 210
7.6 Conclusion 211
Funding 212
Ethical Approval 212
Conflicts of Interest 212
References 212
8 Performance Analysis and Effects of Dust & Temperature on Solar PV Module System by Using Multivariate Linear Regression Model 217
Sumit Sharma, J. Joshua Thomas and Pandian Vasant
8.1 Introduction 218
8.1.1 Indian Scenario of Renewable Energy 218
8.1.2 Solar Radiation at Earth 220
8.1.3 Solar Photovoltaic Technologies 220
8.1.3.1 Types of SPV Systems 221
8.1.3.2 Types of Solar Photovoltaic Cells 222
8.1.3.3 Effects of Temperature 223
8.1.3.4 Conversion Efficiency 223
8.1.4 Losses in PV Systems 224
8.1.5 Performance of Solar Power Plants 224
8.2 Literature Review 225
8.3 Experimental Setup 228
8.3.1 Selection of Site and Development of Experimental Facilities 229
8.3.2 Methodology 229
8.3.3 Experimental Instrumentation 230
8.3.3.1 Solar Photovoltaic Modules 230
8.3.3.2 PV Grid-Connected Inverter 232
8.3.3.3 Pyranometer 232
8.3.3.4 Digital Thermometer 234
8.3.3.5 Lightning Arrester 235
8.3.3.6 Data Acquisition System 236
8.3.4 Formula Used and Sample Calculations 236
8.3.5 Assumptions and Limitations 237
8.4 Results Discussion 238
8.4.1 Phases of Data Collection 238
8.4.2 Variation in Responses Evaluated During Phase I (From 1 Jan. to 27 Feb.) of Study 238
8.4.2.1 Effect of Dust and Ambient Temperature on Conversion Efficiency 238
8.4.2.2 Capacity Utilization Factor and Performance Ratio 241
8.4.2.3 Evaluation of MLR Model 242
8.4.3 Variation in Responses Evaluated During Phase II (From 1 March to 5 April) 246
8.4.3.1 Influence of Dust and Ambient Temperature on Conversion Efficiency 246
8.4.3.2 Capacity Utilization Factor and Performance Ratio 246
8.4.3.3 Evaluation of MLR Model 246
8.4.4 Variation in Responses Evaluated During Phase III (18 May to 25 June) 252
8.4.4.1 Effect of Dust and Ambient Temperature on Conversion Efficiency 252
8.4.4.2 Capacity Utilization Factor and Performance Ratio 255
8.4.4.3 Evaluation of MLR Model 256
8.4.5 Regression Analysis for the Whole Period 258
8.4.6 Best Subsets Regression: Conversion Efficiency v/s Exposure Day, Ambient Temperature 267
8.4.7 Regression Outputs Summary 268
8.4.8 Comparison Between Measured Efficiency and Predicted Efficiency 268
8.4.9 Losses Due to Dust Accumulation 270
8.4.10 Economic Analysis 270
8.5 Future Research Directions 271
8.6 Conclusion 271
References 272
9 Evaluation of In-House Compact Biogas Plant Thereby Testing Four-Stroke Single-Cylinder Diesel Engine 277
Pradeep Kumar Meena, Sumit Sharma, Amit Pal and Samsher
9.1 Introduction 278
9.1.1 Benefits of the Use of Biogas as a Fuel in India 278
9.1.2 Biogas Generators in India 279
9.1.3 Biogas 279
9.1.3.1 Process of Biogas Production 280
9.2 Literature Review 281
9.2.1 Wastes and Environment 281
9.2.2 Economic and Environmental Considerations 283
9.2.3 Factor Affecting Yield and Production of Biogas 285
9.2.3.1 The Temperature 285
9.2.3.2 PH and Buffering Systems 287
9.2.3.3 C/N Ratio 287
9.2.3.4 Substrate Type 289
9.2.3.5 Retention Time 289
9.2.3.6 Total Solids 289
9.2.4 Advantages of Anaerobic Digestion to Society 290
9.2.4.1 Electricity Generation 290
9.2.4.2 Fertilizer Production 290
9.2.4.3 Pathogen Reduction 290
9.3 Methodology 290
9.3.1 Set Up of Compact Biogas Plant and Equipments 290
9.3.2 Assembling and Fabrication of Biogas Plant 292
9.3.3 Design and Technology of Compact Biogas Plant 294
9.3.4 Gas Quantity and Quality 295
9.3.5 Calculation of Gas Quantity in Gas Holder 295
9.4 Analysis of Compact Biogas Plant 299
9.4.1 Experiment Result 299
9.4.1.1 Testing on 50 Kg Animal Dung Along With 500 Ltrs Water 299
9.4.1.2 Testing on Kitchen Waste 300
9.4.1.3 Testing on Fruits Waste 302
9.4.2 Comparison of Biogas by Different Substrate 304
9.4.3 Production of Biogas Per Day at Different Waste 304
9.4.4 Variation of PH Value 307
9.4.5 Variation of Average pH Value 307
9.4.6 Variation of Temperature 308
9.4.7 Variation of Average Temperature With Respect to No. of Days for Animal Dung, Kitchen Waste, Fruits Waste and Sugar 309
9.4.8 Variation of Biogas Production W.R.T. Quantity of Kitchen Waste and Fruits Waste 311
9.5 Analysis of Single-Cylinder Diesel Engine on Dual Fuel 313
9.5.1 Testing on 4-Stroke Single-Cylinder Diesel Engine 313
9.5.2 Calculation 316
9.5.3 Heat Balance Sheet 322
9.5.4 Testing Result With Dual Fuel (Biogas and Diesel) on 4-Stroke Single-Cylinder Diesel Engine 326
9.5.5 Calculation 330
9.5.6 Heat Balance Sheet 335
9.6 General Comments 336
9.7 Conclusion 339
9.8 Future Scope 340
References 340
10 Low-Temperature Combustion Technologies for Emission Reduction in Diesel Engines 345
Amit Jhalani, Sumit Sharma, Pushpendra Kumar Sharma and Digambar Singh
Abbreviations 346
10.1 Introduction 346
10.1.1 Global Scenario of Energy and Emissions 347
10.1.2 Diesel Engine Emissions 348
10.1.3 Mitigation of NOx and Particulate Matter 350
10.1.4 Low-Temperature Combustion Engine Fuels 350
10.2 Scope of the Current Article 351
10.3 HCCI Technology 352
10.3.1 Principle of HCCI 353
10.3.2 Performance and Emissions with HCCI 354
10.4 Partially Premixed Compression Ignition (PPCI) 354
10.5 Exhaust Gas Recirculation (EGR) 355
10.6 Reactivity Controlled Compression Ignition (RCCI) 356
10.7 LTC Through Fuel Additives 357
10.8 Emulsified Fuels (Water-in-Diesel Emulsion Fuel) 358
10.8.1 Brake Thermal Efficiency (BTE) 359
10.8.2 Nitrogen Oxide (NOx) 359
10.8.3 Soot and Particulate Matter (PM) 360
10.9 Conclusion and Future Scope 361
Acknowledgement 361
References 361
11 Efficiency Optimization of Indoor Air Disinfection by Radiation Exposure for Poultry Breeding Rational for Microclimate Systems Modernization for Livestock Premises 371
Dovlatov Igor Mamedjarevich and Yurochka Sergey Sergeevich
11.1 Introduction 372
11.2 Materials and Methods 374
11.3 Results 379
11.4 Discussion 382
11.5 Conclusions 385
References 386
12 Improving the Efficiency of Photovoltaic Installations for Sustainable Development of the Urban Environment 389
Pavel Kuznetsov, Leonid Yuferev and Dmitry Voronin
12.1 Introduction 390
12.2 Background 392
12.3 Main Focus of the Chapter 402
12.4 Solutions and Recommendations 417
Acknowledgements 417
References 418
13 Monitoring System Based Micro-Controller for Biogas Digester 423
Ahmed Abdelouareth and Mohamed Tamali
13.1 Introduction 423
13.2 Related Work 424
13.3 Methods and Material 425
13.3.1 Identification of Needs 425
13.3.2 ADOLMS Software Setup 425
13.3.3 ADOLMS Sensors 426
13.3.4 ADOLMS Hardware Architecture 428
13.4 Results 430
13.5 Conclusion 432
Acknowledgements 433
References 433
14 Greenhouse Gas Statistics and Methods of Combating Climate Change 435
Tatyana G. Krotova
Introduction 435
Methodology 436
Findings 436
Conclusion 454
References 455
About the Editors 457
Index 459
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Artificial intelligence; Machine learning; Deep learning; Metaheuristic algorithms; Hydropower; Renewable electricity; Solar PV; Bio power; Geothermal power; Ocean power; Wind power; Biogas; Hydrogen; Global warming; Climate change; Renewable energy; Hybrid technology; CO2 minimization; Evolutionary algorithms; Swarm intelligence; Computational intelligence; Soft computing; Operational research; Data mining; Hybrid optimization; Bioenergy recycling; Biofuel supply chains; Energy management policy; Energy efficiency; Energy-saving technology; Small hydropower plants; Thermal treatments; Remote sensing; Optimization theory; Optimal control theory; Stochastic optimal control theory; Big Data Analytics; IoT; Cloud Computing; Block Chain; 5G Technology; Data Technology; Industry 4.0; Smart Technology; Deep Neural Network; Fuzzy Logic; Evolutionary Algorithm; Ant Optimization; Financial Technology; Drone Technology; Robotics; Smart Energy; Smart farming
