{"product_id":"artificial-intelligence-for-renewable-energy-and-climate-change-hardback-9781119768999","title":"Artificial Intelligence for Renewable Energy and Climate Change (Hardback) 9781119768999","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eArtificial Intelligence for Renewable Energy and Climate Change\u003c\/font\u003e\u003cbr\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003c\/p\u003e\n\u003cp\u003e\u003cfont size=\"4\"\u003ePandian Vasant (Edited by), Vasant (Author), Gerhard-Wilhelm Weber (Edited by), J. Joshua Thomas (Edited by), José Antonio Marmolejo-Saucedo (Edited by), Roman Rodriguez-Aguilar (Edited by)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9781119768999, Wiley\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eHardback, published 8 August 2022\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e496 pages\u003cbr\u003e1 x 1 x 1 cm, 0.454 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003e\u003cp\u003e\u003cb\u003eARTIFICIAL INTELLIGENCE FOR RENEWABLE ENERGY AND CLIMATE CHANGE\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eWritten and edited by a global team of experts in the field, this groundbreaking new volume presents the concepts and fundamentals of using artificial intelligence in renewable energy and climate change, while also covering the practical applications that can be utilized across multiple disciplines and industries, for the engineer, the student, and other professionals and scientists.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eRenewable energy and climate change are two of the most important and difficult issues facing the world today. The state of the art in these areas is changing rapidly, with new techniques and theories coming online seemingly every day. It is important for scientists, engineers, and other professionals working in these areas to stay abreast of developments, advances, and practical applications, and this volume is an outstanding reference and tool for this purpose.\u003c\/p\u003e \u003cp\u003eThe paradigm in renewable energy and climate change shifts constantly. In today’s international and competitive environment, lean and green practices are important determinants to increase performance. Corresponding production philosophies and techniques help companies diminish lead times and costs of manufacturing, improve delivery on time and quality, and at the same time become more ecological by reducing material use and waste, and by recycling and reusing. Those lean and green activities enhance productivity, lower carbon footprint and improve consumer satisfaction, which in reverse makes firms competitive and sustainable.\u003c\/p\u003e \u003cp\u003eThis practical, new groundbreaking volume:\u003c\/p\u003e \u003cul\u003e \u003cli\u003eFeatures coverage on a wide range of topics such as classical and nature-inspired optimization and optimal control, hybrid and stochastic systems\u003c\/li\u003e \u003cli\u003eIs ideally designed for engineers, scientists, industrialist, academicians, researchers, computer and information technologists, sustainable developers, managers, environmentalists, government leaders, research officers, policy makers, business leaders and students\u003c\/li\u003e \u003cli\u003eIs useful as a practical tool for practitioners in the fields of sustainable and renewable energy sustainability\u003c\/li\u003e \u003cli\u003eIncludes wide coverage of how artificial intelligence can be used to impact the struggle against global warming and climate change\u003c\/li\u003e \u003c\/ul\u003e\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e\u003cp\u003ePreface xv\u003c\/p\u003e \u003cp\u003e\u003cb\u003eSection I: Renewable Energy 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Artificial Intelligence for Sustainability: Opportunities and Challenges 3\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eAmany Alshawi\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1.1 Introduction 3\u003c\/p\u003e \u003cp\u003e1.2 History of AI for Sustainability and Smart Energy Practices 4\u003c\/p\u003e \u003cp\u003e1.3 Energy and Resources Scenarios on the Global Scale 5\u003c\/p\u003e \u003cp\u003e1.4 Statistical Basis of AI in Sustainability Practices 6\u003c\/p\u003e \u003cp\u003e1.4.1 General Statistics 6\u003c\/p\u003e \u003cp\u003e1.4.2 Environmental Stress–Based Statistics 8\u003c\/p\u003e \u003cp\u003e1.4.2.1 Climate Change 9\u003c\/p\u003e \u003cp\u003e1.4.2.2 Biodiversity 10\u003c\/p\u003e \u003cp\u003e1.4.2.3 Deforestation 10\u003c\/p\u003e \u003cp\u003e1.4.2.4 Changes in Chemistry of Oceans 10\u003c\/p\u003e \u003cp\u003e1.4.2.5 Nitrogen Cycle 10\u003c\/p\u003e \u003cp\u003e1.4.2.6 Water Crisis 11\u003c\/p\u003e \u003cp\u003e1.4.2.7 Air Pollution 11\u003c\/p\u003e \u003cp\u003e1.5 Major Challenges Faced by AI in Sustainability 11\u003c\/p\u003e \u003cp\u003e1.5.1 Concentration of Wealth 11\u003c\/p\u003e \u003cp\u003e1.5.2 Talent-Related and Business-Related Challenges of AI 12\u003c\/p\u003e \u003cp\u003e1.5.3 Dependence on Machine Learning 14\u003c\/p\u003e \u003cp\u003e1.5.4 Cybersecurity Risks 15\u003c\/p\u003e \u003cp\u003e1.5.5 Carbon Footprint of AI 16\u003c\/p\u003e \u003cp\u003e1.5.6 Issues in Performance Measurement 16\u003c\/p\u003e \u003cp\u003e1.6 Major Opportunities of AI in Sustainability 17\u003c\/p\u003e \u003cp\u003e1.6.1 AI and Water-Related Hazards Management 17\u003c\/p\u003e \u003cp\u003e1.6.2 AI and Smart Cities 18\u003c\/p\u003e \u003cp\u003e1.6.3 AI and Climate Change 21\u003c\/p\u003e \u003cp\u003e1.6.4 AI and Environmental Sustainability 23\u003c\/p\u003e \u003cp\u003e1.6.5 Impacts of AI in Transportation 24\u003c\/p\u003e \u003cp\u003e1.6.6 Opportunities in Disaster Forecasting and Deforestation Forecasting 25\u003c\/p\u003e \u003cp\u003e1.6.7 Opportunities in the Energy Sector 26\u003c\/p\u003e \u003cp\u003e1.7 Conclusion and Future Direction 26\u003c\/p\u003e \u003cp\u003eReferences 27\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Recent Applications of Machine Learning in Solar Energy Prediction 33\u003c\/b\u003e\u003ci\u003e\u003cbr\u003eN. Kapilan, R.P. Reddy and Vidhya P.\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction 34\u003c\/p\u003e \u003cp\u003e2.2 Solar Energy 34\u003c\/p\u003e \u003cp\u003e2.3 AI, ML and DL 36\u003c\/p\u003e \u003cp\u003e2.4 Data Preprocessing Techniques 38\u003c\/p\u003e \u003cp\u003e2.5 Solar Radiation Estimation 38\u003c\/p\u003e \u003cp\u003e2.6 Solar Power Prediction 43\u003c\/p\u003e \u003cp\u003e2.7 Challenges and Opportunities 45\u003c\/p\u003e \u003cp\u003e2.8 Future Research Directions 46\u003c\/p\u003e \u003cp\u003e2.9 Conclusion 46\u003c\/p\u003e \u003cp\u003eAcknowledgement 47\u003c\/p\u003e \u003cp\u003eReferences 47\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Mathematical Analysis on Power Generation – Part I 53\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eG. Udhaya Sankar, C. Ganesa Moorthy and C.T. Ramasamy\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction 54\u003c\/p\u003e \u003cp\u003e3.2 Methodology for Derivations 55\u003c\/p\u003e \u003cp\u003e3.3 Energy Discussions 59\u003c\/p\u003e \u003cp\u003e3.4 Data Analysis 63\u003c\/p\u003e \u003cp\u003eAcknowledgement 67\u003c\/p\u003e \u003cp\u003eReferences 67\u003c\/p\u003e \u003cp\u003eSupplementary 69\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Mathematical Analysis on Power Generation – Part II 87\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eG. Udhaya Sankar, C. Ganesa Moorthy and C.T. Ramasamy\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e4.1 Energy Analysis 88\u003c\/p\u003e \u003cp\u003e4.2 Power Efficiency Method 89\u003c\/p\u003e \u003cp\u003e4.3 Data Analysis 91\u003c\/p\u003e \u003cp\u003eAcknowledgement 96\u003c\/p\u003e \u003cp\u003eReferences 97\u003c\/p\u003e \u003cp\u003eSupplementary - II 100\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Sustainable Energy Materials 117\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eG. Udhaya Sankar\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction 117\u003c\/p\u003e \u003cp\u003e5.2 Different Methods 119\u003c\/p\u003e \u003cp\u003e5.2.1 Co-Precipitation Method 119\u003c\/p\u003e \u003cp\u003e5.2.2 Microwave-Assisted Solvothermal Method 120\u003c\/p\u003e \u003cp\u003e5.2.3 Sol-Gel Method 120\u003c\/p\u003e \u003cp\u003e5.3 X-R ay Diffraction Analysis 120\u003c\/p\u003e \u003cp\u003e5.4 FTIR Analysis 122\u003c\/p\u003e \u003cp\u003e5.5 Raman Analysis 124\u003c\/p\u003e \u003cp\u003e5.6 UV Analysis 125\u003c\/p\u003e \u003cp\u003e5.7 SEM Analysis 127\u003c\/p\u003e \u003cp\u003e5.8 Energy Dispersive X-Ray Analysis 127\u003c\/p\u003e \u003cp\u003e5.9 Thermoelectric Application 129\u003c\/p\u003e \u003cp\u003e5.9.1 Thermal Conductivity 129\u003c\/p\u003e \u003cp\u003e5.9.2 Electrical Conductivity 131\u003c\/p\u003e \u003cp\u003e5.9.3 Seebeck Coefficient 131\u003c\/p\u003e \u003cp\u003e5.9.4 Power Factor 132\u003c\/p\u003e \u003cp\u003e5.9.5 Figure of Merit 133\u003c\/p\u003e \u003cp\u003e5.10 Limitations and Future Direction 133\u003c\/p\u003e \u003cp\u003e5.11 Conclusion 133\u003c\/p\u003e \u003cp\u003eAcknowledgement 134\u003c\/p\u003e \u003cp\u003eReferences 134\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Soft Computing Techniques for Maximum Power Point Tracking in Wind Energy Harvesting System: A Survey 137\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eTigiluMitikuDinku, Mukhdeep Singh Manshahia and Karanvir Singh Chahal\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 137\u003c\/p\u003e \u003cp\u003e6.1.1 Conventional MPPT Control Techniques 138\u003c\/p\u003e \u003cp\u003e6.2 Other MPPT Control Methods 142\u003c\/p\u003e \u003cp\u003e6.2.1 Proportional Integral Derivative Controllers 142\u003c\/p\u003e \u003cp\u003e6.2.2 Fuzzy Logic Controller 144\u003c\/p\u003e \u003cp\u003e6.2.2.1 Fuzzy Inference System 150\u003c\/p\u003e \u003cp\u003e6.2.2.2 Advantage and Disadvantages of Fuzzy Logic Controller 151\u003c\/p\u003e \u003cp\u003e6.2.3 Artificial Neural Network 151\u003c\/p\u003e \u003cp\u003e6.2.3.1 Biological Neural Networks 152\u003c\/p\u003e \u003cp\u003e6.2.3.2 Architectures of Artificial Neural Networks 155\u003c\/p\u003e \u003cp\u003e6.2.3.3 Training of Artificial Neural Networks 157\u003c\/p\u003e \u003cp\u003e6.2.3.4 Radial Basis Function 158\u003c\/p\u003e \u003cp\u003e6.2.4 Neuro-Fuzzy Inference Approach 158\u003c\/p\u003e \u003cp\u003e6.2.4.1 Adaptive Neuro-Fuzzy Approach 161\u003c\/p\u003e \u003cp\u003e6.2.4.2 Hybrid Training Algorithm 161\u003c\/p\u003e \u003cp\u003e6.3 Conclusion 167\u003c\/p\u003e \u003cp\u003eReferences 167\u003c\/p\u003e \u003cp\u003e\u003cb\u003eSection II: Climate Change 171\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 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\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eMesut Toğaçar\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction 174\u003c\/p\u003e \u003cp\u003e7.2 Materials 177\u003c\/p\u003e \u003cp\u003e7.2.1 Classification of Air Quality Condition in Gas Concentration Measurement 177\u003c\/p\u003e \u003cp\u003e7.2.2 CO2 Emission of Vehicles 178\u003c\/p\u003e \u003cp\u003e7.2.3 Countries’ CO2 Emission Amount 179\u003c\/p\u003e \u003cp\u003e7.2.4 Stability Level in Electric Grids 179\u003c\/p\u003e \u003cp\u003e7.3 Artificial Intelligence Approaches 181\u003c\/p\u003e \u003cp\u003e7.3.1 Machine Learning Methods 182\u003c\/p\u003e \u003cp\u003e7.3.1.1 Support Vector Machine 183\u003c\/p\u003e \u003cp\u003e7.3.1.2 eXtreme Gradient Boosting (XG Boost) 184\u003c\/p\u003e \u003cp\u003e7.3.1.3 Gradient Boost 185\u003c\/p\u003e \u003cp\u003e7.3.1.4 Decision Tree 186\u003c\/p\u003e \u003cp\u003e7.3.1.5 Random Forest 186\u003c\/p\u003e \u003cp\u003e7.3.2 Deep Learning Methods 188\u003c\/p\u003e \u003cp\u003e7.3.2.1 Convolutional Neural Networks 189\u003c\/p\u003e \u003cp\u003e7.3.2.2 Long Short-Term Memory 191\u003c\/p\u003e \u003cp\u003e7.3.2.3 Bi-Directional LSTM and CNN 192\u003c\/p\u003e \u003cp\u003e7.3.2.4 Recurrent Neural Network 193\u003c\/p\u003e \u003cp\u003e7.3.3 Activation Functions 195\u003c\/p\u003e \u003cp\u003e7.3.3.1 Rectified Linear Unit 195\u003c\/p\u003e \u003cp\u003e7.3.3.2 Softmax Function 196\u003c\/p\u003e \u003cp\u003e7.4 Experimental Analysis 196\u003c\/p\u003e \u003cp\u003e7.5 Discussion 210\u003c\/p\u003e \u003cp\u003e7.6 Conclusion 211\u003c\/p\u003e \u003cp\u003eFunding 212\u003c\/p\u003e \u003cp\u003eEthical Approval 212\u003c\/p\u003e \u003cp\u003eConflicts of Interest 212\u003c\/p\u003e \u003cp\u003eReferences 212\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Performance Analysis and Effects of Dust \u0026amp; Temperature on Solar PV Module System by Using Multivariate Linear Regression Model 217\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eSumit Sharma, J. Joshua Thomas and Pandian Vasant\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction 218\u003c\/p\u003e \u003cp\u003e8.1.1 Indian Scenario of Renewable Energy 218\u003c\/p\u003e \u003cp\u003e8.1.2 Solar Radiation at Earth 220\u003c\/p\u003e \u003cp\u003e8.1.3 Solar Photovoltaic Technologies 220\u003c\/p\u003e \u003cp\u003e8.1.3.1 Types of SPV Systems 221\u003c\/p\u003e \u003cp\u003e8.1.3.2 Types of Solar Photovoltaic Cells 222\u003c\/p\u003e \u003cp\u003e8.1.3.3 Effects of Temperature 223\u003c\/p\u003e \u003cp\u003e8.1.3.4 Conversion Efficiency 223\u003c\/p\u003e \u003cp\u003e8.1.4 Losses in PV Systems 224\u003c\/p\u003e \u003cp\u003e8.1.5 Performance of Solar Power Plants 224\u003c\/p\u003e \u003cp\u003e8.2 Literature Review 225\u003c\/p\u003e \u003cp\u003e8.3 Experimental Setup 228\u003c\/p\u003e \u003cp\u003e8.3.1 Selection of Site and Development of Experimental Facilities 229\u003c\/p\u003e \u003cp\u003e8.3.2 Methodology 229\u003c\/p\u003e \u003cp\u003e8.3.3 Experimental Instrumentation 230\u003c\/p\u003e \u003cp\u003e8.3.3.1 Solar Photovoltaic Modules 230\u003c\/p\u003e \u003cp\u003e8.3.3.2 PV Grid-Connected Inverter 232\u003c\/p\u003e \u003cp\u003e8.3.3.3 Pyranometer 232\u003c\/p\u003e \u003cp\u003e8.3.3.4 Digital Thermometer 234\u003c\/p\u003e \u003cp\u003e8.3.3.5 Lightning Arrester 235\u003c\/p\u003e \u003cp\u003e8.3.3.6 Data Acquisition System 236\u003c\/p\u003e \u003cp\u003e8.3.4 Formula Used and Sample Calculations 236\u003c\/p\u003e \u003cp\u003e8.3.5 Assumptions and Limitations 237\u003c\/p\u003e \u003cp\u003e8.4 Results Discussion 238\u003c\/p\u003e \u003cp\u003e8.4.1 Phases of Data Collection 238\u003c\/p\u003e \u003cp\u003e8.4.2 Variation in Responses Evaluated During Phase I (From 1 Jan. to 27 Feb.) of Study 238\u003c\/p\u003e \u003cp\u003e8.4.2.1 Effect of Dust and Ambient Temperature on Conversion Efficiency 238\u003c\/p\u003e \u003cp\u003e8.4.2.2 Capacity Utilization Factor and Performance Ratio 241\u003c\/p\u003e \u003cp\u003e8.4.2.3 Evaluation of MLR Model 242\u003c\/p\u003e \u003cp\u003e8.4.3 Variation in Responses Evaluated During Phase II (From 1 March to 5 April) 246\u003c\/p\u003e \u003cp\u003e8.4.3.1 Influence of Dust and Ambient Temperature on Conversion Efficiency 246\u003c\/p\u003e \u003cp\u003e8.4.3.2 Capacity Utilization Factor and Performance Ratio 246\u003c\/p\u003e \u003cp\u003e8.4.3.3 Evaluation of MLR Model 246\u003c\/p\u003e \u003cp\u003e8.4.4 Variation in Responses Evaluated During Phase III (18 May to 25 June) 252\u003c\/p\u003e \u003cp\u003e8.4.4.1 Effect of Dust and Ambient Temperature on Conversion Efficiency 252\u003c\/p\u003e \u003cp\u003e8.4.4.2 Capacity Utilization Factor and Performance Ratio 255\u003c\/p\u003e \u003cp\u003e8.4.4.3 Evaluation of MLR Model 256\u003c\/p\u003e \u003cp\u003e8.4.5 Regression Analysis for the Whole Period 258\u003c\/p\u003e \u003cp\u003e8.4.6 Best Subsets Regression: Conversion Efficiency v\/s Exposure Day, Ambient Temperature 267\u003c\/p\u003e \u003cp\u003e8.4.7 Regression Outputs Summary 268\u003c\/p\u003e \u003cp\u003e8.4.8 Comparison Between Measured Efficiency and Predicted Efficiency 268\u003c\/p\u003e \u003cp\u003e8.4.9 Losses Due to Dust Accumulation 270\u003c\/p\u003e \u003cp\u003e8.4.10 Economic Analysis 270\u003c\/p\u003e \u003cp\u003e8.5 Future Research Directions 271\u003c\/p\u003e \u003cp\u003e8.6 Conclusion 271\u003c\/p\u003e \u003cp\u003eReferences 272\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Evaluation of In-House Compact Biogas Plant Thereby Testing Four-Stroke Single-Cylinder Diesel Engine 277\u003cbr\u003e\u003c\/b\u003e\u003ci\u003ePradeep Kumar Meena, Sumit Sharma, Amit Pal and Samsher\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction 278\u003c\/p\u003e \u003cp\u003e9.1.1 Benefits of the Use of Biogas as a Fuel in India 278\u003c\/p\u003e \u003cp\u003e9.1.2 Biogas Generators in India 279\u003c\/p\u003e \u003cp\u003e9.1.3 Biogas 279\u003c\/p\u003e \u003cp\u003e9.1.3.1 Process of Biogas Production 280\u003c\/p\u003e \u003cp\u003e9.2 Literature Review 281\u003c\/p\u003e \u003cp\u003e9.2.1 Wastes and Environment 281\u003c\/p\u003e \u003cp\u003e9.2.2 Economic and Environmental Considerations 283\u003c\/p\u003e \u003cp\u003e9.2.3 Factor Affecting Yield and Production of Biogas 285\u003c\/p\u003e \u003cp\u003e9.2.3.1 The Temperature 285\u003c\/p\u003e \u003cp\u003e9.2.3.2 PH and Buffering Systems 287\u003c\/p\u003e \u003cp\u003e9.2.3.3 C\/N Ratio 287\u003c\/p\u003e \u003cp\u003e9.2.3.4 Substrate Type 289\u003c\/p\u003e \u003cp\u003e9.2.3.5 Retention Time 289\u003c\/p\u003e \u003cp\u003e9.2.3.6 Total Solids 289\u003c\/p\u003e \u003cp\u003e9.2.4 Advantages of Anaerobic Digestion to Society 290\u003c\/p\u003e \u003cp\u003e9.2.4.1 Electricity Generation 290\u003c\/p\u003e \u003cp\u003e9.2.4.2 Fertilizer Production 290\u003c\/p\u003e \u003cp\u003e9.2.4.3 Pathogen Reduction 290\u003c\/p\u003e \u003cp\u003e9.3 Methodology 290\u003c\/p\u003e \u003cp\u003e9.3.1 Set Up of Compact Biogas Plant and Equipments 290\u003c\/p\u003e \u003cp\u003e9.3.2 Assembling and Fabrication of Biogas Plant 292\u003c\/p\u003e \u003cp\u003e9.3.3 Design and Technology of Compact Biogas Plant 294\u003c\/p\u003e \u003cp\u003e9.3.4 Gas Quantity and Quality 295\u003c\/p\u003e \u003cp\u003e9.3.5 Calculation of Gas Quantity in Gas Holder 295\u003c\/p\u003e \u003cp\u003e9.4 Analysis of Compact Biogas Plant 299\u003c\/p\u003e \u003cp\u003e9.4.1 Experiment Result 299\u003c\/p\u003e \u003cp\u003e9.4.1.1 Testing on 50 Kg Animal Dung Along With 500 Ltrs Water 299\u003c\/p\u003e \u003cp\u003e9.4.1.2 Testing on Kitchen Waste 300\u003c\/p\u003e \u003cp\u003e9.4.1.3 Testing on Fruits Waste 302\u003c\/p\u003e \u003cp\u003e9.4.2 Comparison of Biogas by Different Substrate 304\u003c\/p\u003e \u003cp\u003e9.4.3 Production of Biogas Per Day at Different Waste 304\u003c\/p\u003e \u003cp\u003e9.4.4 Variation of PH Value 307\u003c\/p\u003e \u003cp\u003e9.4.5 Variation of Average pH Value 307\u003c\/p\u003e \u003cp\u003e9.4.6 Variation of Temperature 308\u003c\/p\u003e \u003cp\u003e9.4.7 Variation of Average Temperature With Respect to No. of Days for Animal Dung, Kitchen Waste, Fruits Waste and Sugar 309\u003c\/p\u003e \u003cp\u003e9.4.8 Variation of Biogas Production W.R.T. Quantity of Kitchen Waste and Fruits Waste 311\u003c\/p\u003e \u003cp\u003e9.5 Analysis of Single-Cylinder Diesel Engine on Dual Fuel 313\u003c\/p\u003e \u003cp\u003e9.5.1 Testing on 4-Stroke Single-Cylinder Diesel Engine 313\u003c\/p\u003e \u003cp\u003e9.5.2 Calculation 316\u003c\/p\u003e \u003cp\u003e9.5.3 Heat Balance Sheet 322\u003c\/p\u003e \u003cp\u003e9.5.4 Testing Result With Dual Fuel (Biogas and Diesel) on 4-Stroke Single-Cylinder Diesel Engine 326\u003c\/p\u003e \u003cp\u003e9.5.5 Calculation 330\u003c\/p\u003e \u003cp\u003e9.5.6 Heat Balance Sheet 335\u003c\/p\u003e \u003cp\u003e9.6 General Comments 336\u003c\/p\u003e \u003cp\u003e9.7 Conclusion 339\u003c\/p\u003e \u003cp\u003e9.8 Future Scope 340\u003c\/p\u003e \u003cp\u003eReferences 340\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Low-Temperature Combustion Technologies for Emission Reduction in Diesel Engines 345\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eAmit Jhalani, Sumit Sharma, Pushpendra Kumar Sharma and Digambar Singh\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eAbbreviations 346\u003c\/p\u003e \u003cp\u003e10.1 Introduction 346\u003c\/p\u003e \u003cp\u003e10.1.1 Global Scenario of Energy and Emissions 347\u003c\/p\u003e \u003cp\u003e10.1.2 Diesel Engine Emissions 348\u003c\/p\u003e \u003cp\u003e10.1.3 Mitigation of NOx and Particulate Matter 350\u003c\/p\u003e \u003cp\u003e10.1.4 Low-Temperature Combustion Engine Fuels 350\u003c\/p\u003e \u003cp\u003e10.2 Scope of the Current Article 351\u003c\/p\u003e \u003cp\u003e10.3 HCCI Technology 352\u003c\/p\u003e \u003cp\u003e10.3.1 Principle of HCCI 353\u003c\/p\u003e \u003cp\u003e10.3.2 Performance and Emissions with HCCI 354\u003c\/p\u003e \u003cp\u003e10.4 Partially Premixed Compression Ignition (PPCI) 354\u003c\/p\u003e \u003cp\u003e10.5 Exhaust Gas Recirculation (EGR) 355\u003c\/p\u003e \u003cp\u003e10.6 Reactivity Controlled Compression Ignition (RCCI) 356\u003c\/p\u003e \u003cp\u003e10.7 LTC Through Fuel Additives 357\u003c\/p\u003e \u003cp\u003e10.8 Emulsified Fuels (Water-in-Diesel Emulsion Fuel) 358\u003c\/p\u003e \u003cp\u003e10.8.1 Brake Thermal Efficiency (BTE) 359\u003c\/p\u003e \u003cp\u003e10.8.2 Nitrogen Oxide (NOx) 359\u003c\/p\u003e \u003cp\u003e10.8.3 Soot and Particulate Matter (PM) 360\u003c\/p\u003e \u003cp\u003e10.9 Conclusion and Future Scope 361\u003c\/p\u003e \u003cp\u003eAcknowledgement 361\u003c\/p\u003e \u003cp\u003eReferences 361\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Efficiency Optimization of Indoor Air Disinfection by Radiation Exposure for Poultry Breeding Rational for Microclimate Systems Modernization for Livestock Premises 371\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eDovlatov Igor Mamedjarevich and Yurochka Sergey Sergeevich\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e11.1 Introduction 372\u003c\/p\u003e \u003cp\u003e11.2 Materials and Methods 374\u003c\/p\u003e \u003cp\u003e11.3 Results 379\u003c\/p\u003e \u003cp\u003e11.4 Discussion 382\u003c\/p\u003e \u003cp\u003e11.5 Conclusions 385\u003c\/p\u003e \u003cp\u003eReferences 386\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Improving the Efficiency of Photovoltaic Installations for Sustainable Development of the Urban Environment 389\u003cbr\u003e\u003c\/b\u003e\u003ci\u003ePavel Kuznetsov, Leonid Yuferev and Dmitry Voronin\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e12.1 Introduction 390\u003c\/p\u003e \u003cp\u003e12.2 Background 392\u003c\/p\u003e \u003cp\u003e12.3 Main Focus of the Chapter 402\u003c\/p\u003e \u003cp\u003e12.4 Solutions and Recommendations 417\u003c\/p\u003e \u003cp\u003eAcknowledgements 417\u003c\/p\u003e \u003cp\u003eReferences 418\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Monitoring System Based Micro-Controller for Biogas Digester 423\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eAhmed Abdelouareth and Mohamed Tamali\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e13.1 Introduction 423\u003c\/p\u003e \u003cp\u003e13.2 Related Work 424\u003c\/p\u003e \u003cp\u003e13.3 Methods and Material 425\u003c\/p\u003e \u003cp\u003e13.3.1 Identification of Needs 425\u003c\/p\u003e \u003cp\u003e13.3.2 ADOLMS Software Setup 425\u003c\/p\u003e \u003cp\u003e13.3.3 ADOLMS Sensors 426\u003c\/p\u003e \u003cp\u003e13.3.4 ADOLMS Hardware Architecture 428\u003c\/p\u003e \u003cp\u003e13.4 Results 430\u003c\/p\u003e \u003cp\u003e13.5 Conclusion 432\u003c\/p\u003e \u003cp\u003eAcknowledgements 433\u003c\/p\u003e \u003cp\u003eReferences 433\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Greenhouse Gas Statistics and Methods of Combating Climate Change 435\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eTatyana G. Krotova\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eIntroduction 435\u003c\/p\u003e \u003cp\u003eMethodology 436\u003c\/p\u003e \u003cp\u003eFindings 436\u003c\/p\u003e \u003cp\u003eConclusion 454\u003c\/p\u003e \u003cp\u003eReferences 455\u003c\/p\u003e \u003cp\u003eAbout the Editors 457\u003c\/p\u003e \u003cp\u003eIndex 459 \u003c\/p\u003e\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Computer science [\u003ca title=\"See our other books on Computer science\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Computer%20science%20%5BUY%5D%22\"\u003eUY\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Wiley-Scrivener","offers":[{"title":"Brand New","offer_id":52430937587992,"sku":"9781119768999","price":128.58,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/files\/9781119768999.jpg?v=1784765785","url":"https:\/\/freshlyprintedbooks.co.uk\/products\/artificial-intelligence-for-renewable-energy-and-climate-change-hardback-9781119768999","provider":"Freshly Printed Books","version":"1.0","type":"link"}