Machine Learning for Sustainable Energy Solutions

Comprehensive insights into integrating modern engineering techniques with machine learning and renewable energy to create a more sustainable world Through an interdisciplinary approach, Machine Learning for Sustainable Energy Solutions provides comprehensive insights into integrating modern engineering techniques such as machine learning (ML), artificial intelligence (AI), nanotechnology, digital twins, and the Internet of Things (IoT) with renewable energy. Each chapter is based on modern research and enhanced by experimental or simulated data. The book offers a thorough review of several energy storage techniques, helping readers fully grasp the larger background in which chemical, thermal, electrical, mechanical, and machine learning technologies may be used to evaluate, categorize, and maximize different storage systems. The book also reviews the confluence of the Internet of Things (IoT) and machine learning for real-time digestive parameter control and monitoring, along with the cooperative importance of mathematical modeling and artificial intelligence in maximizing reactor performance, gas output, and operational stability. Machine Learning for Sustainable Energy Solutions includes information on: Bio-based energy generation from biomass gasification and biohydrogen Usage of hybrid approaches, support vector machines, and neural networks to anticipate and maximize bioenergy production from challenging organic feedstocks Hydrogen-powered dual-fuel engines, covering response surface methodology (RSM) for multi-attribute optimization Scalable, experimentally confirmed ML-based solutions for long-standing problems like sedimentation, pumping losses, and stability of nanofluids The growing and important use of nanotechnology in energy systems, particularly in engine emissions management, energy storage, and heat transfer improvements Machine Learning for Sustainable Energy Solutions is an essential reference for professionals, researchers, educators, and students working in the fields of energy, environmental science, and machine learning. The book also helps decision-makers in various fields by providing them the required knowledge to make informed choices on sustainable practices and policies.

List of Contributors xii Preface xvi 1 Green Energy-Led Sustainable Development: Barriers and Opportunities 1 Arni Gesselle M. Pornea, and Hussein Safwat Hasan Hasan 1.1 Introduction 1 1.2 The Current Landscape of Green Energy 3 1.2.1 Green Energy Types and Technologies 3 1.2.2 Global Green Energy Usage Statistics 3 1.3 Barriers to Green Energy Implementation 4 1.3.1 Economic and Financial Challenges 4 1.3.1.1 High Initial Costs 5 1.3.1.2 Investment Risks 5 1.3.2 Regulatory and Policy Frameworks 5 1.3.3 Social Acceptance and Cultural Factors 6 1.3.4 Technological Barriers 6 1.4 ml and AI in Green Energy 7 1.4.1 Technological Assessment and Optimization 7 1.4.2 Predictive Net-Zero Initiative 8 1.4.3 Enhancing Energy Storage Systems 9 1.4.4 Energy Demand and Supply Forecasting 9 1.4.5 Setting Ambitious Goal 10 1.4.6 Activate Support and Financial Investment 10 1.5 Challenges in the Integration of ML and AI in Renewable Energy 10 1.6 Directive in ML and AI Improvement Toward Its Application 11 1.6.1 Workforce Capacity Increase 12 1.6.2 Large-Scale Project Implementation 12 1.6.3 Public Awareness 12 1.6.4 Continuous Progress Monitoring and Strategies Adjustment 12 1.7 Conclusion 13 References 14 2 Machine Learning-Driven Valorization of Organic Waste for Sustainable Bio-Hydrogen Production 17 Munusamy Arun, Debabrata Barik, and Sreejesh S. R. Chandran 2.1 Introduction 17 2.1.1 Objectives 18 2.2 Literature Review 18 2.3 Proposed Method 19 2.4 Results and Discussion 25 2.4.1 Bio-Hydrogen Production Efficiency Analysis 25 2.4.2 Performance Analysis 25 2.4.3 Adaptability Analysis 27 2.5 Conclusion 28 Author Contributions 29 Acknowledgment 29 Data Availability Statement 29 Funding Statement 29 Conflict of Interest 29 References 30 3 Application of Neural Networks for Model Prediction of Combustion and Emissions in Diesel Engines 33 Parampreet Singh Jassal, Sridhar Sahoo, and Neeraj Kumbhakarna 3.1 Introduction 34 3.2 Artificial Neural Networks 35 3.2.1 Types of Artificial Neural Networks 37 3.3 AI and ANN in Internal Combustion Engines 39 3.4 ANN in Diesel Engines 40 3.4.1 ANN for Different Fuel Properties 41 3.4.2 ANN for Diesel Engine Performance 41 3.4.3 ANN for Diesel Engines Using Biodiesel Blends 43 3.4.4 ANN for Gaseous Fuels 47 3.4.4.1 MISO Model Studies 48 3.4.4.2 MIMO Model Studies 48 3.4.4.3 Comparative Studies 48 3.4.5 ANN for HCCI Engines 50 3.5 Conclusions 52 References 53 4 Enhanced Energy Storage with Hybrid Nanoparticles and Machine Learning for Energy Sustainability 59 Arun Munusamy, Debabrata Barik, and Sreejesh S.R. Chandran 4.1 Introduction 59 4.2 Materials and Methods 61 4.3 Result and Discussion 67 4.4 Conclusion 69 Author Contributions 70 Acknowledgment 70 Data Availability Statement 70 Funding 70 Conflict of Interest 70 References 71 5 Model Prediction of Biomass Gasification Using Support Vector Machines 73 Arun Munusamy and Debabrata Barik 5.1 Introduction and Literature Survey 73 5.2 Materials and Methods 76 5.3 Results and Discussion 83 5.4 Conclusion 89 References 90 6 Role of Machine Learning Techniques in Modeling and Optimization of Biomass Gasification Parameters in a Downdraft Gasifier 93 Vikas Attri and Avdhesh Kr. Sharma 6.1 Introduction 93 6.2 Biomass Gasification 94 6.2.1 Gasification Process 95 6.2.2 Gasification Parameters 97 6.2.2.1 Biomass Characterization 98 6.2.2.2 Equivalence Ratio 98 6.2.2.3 Gasification Temperature 99 6.2.2.4 Biomass Consumption Rate 99 6.2.2.5 Cold Gas Efficiency (CGE) 99 6.2.2.6 Importance of Various Gasifying Agents in the Gasification Process 99 6.2.2.7 Effect of the Gasification Parameters on the Producer Gas 100 6.3 Machine Learning Techniques in Biomass Gasification 100 6.3.1 Gaussian Process Regression 101 6.3.2 Support Vector Machines 101 6.3.3 Artificial Neural Network 103 6.3.4 Decision Trees 105 6.4 Model Performance Metrics 105 6.5 Application of the ML Model in Biomass Gasification 106 6.6 Challenges and Prospects 107 6.7 Conclusion 108 References 109 7 Response Surface Methodology-Based Multiattribute Optimization of a Hydrogen-Powered Dual-Fuel Engines 115 Sanjeev Kumar, Prabhu Paramasivam, and Abdul Razak 7.1 Introduction 115 7.2 Materials and Methods 118 7.2.1 Test Engine Setup and Fuel 118 7.2.2 Analysis of Variance 119 7.2.3 Response Surface Methodology 120 7.3 Results and Discussion 121 7.3.1 Correlation Analysis 121 7.3.2 Analysis of Variance 121 7.3.3 Surface Diagrams and Predictions 126 7.3.4 Parametric Optimization 134 7.4 Conclusion 135 References 135 8 Addition of Nanoparticles to Biodiesel–Diesel Blends to Improve Engine Efficiency and Reduce Tailpipe Emission 139 Mudasar Zafar, Abida Hussain, Tauseef Ahmed, Ahmed Daabo, and Farman Ullah 8.1 Introduction 139 8.2 Background and Performance of Biodiesel Blends in Engine Efficiency 141 8.2.1 Properties of the Biodiesel 142 8.2.2 Performance 144 8.2.3 Performance of Biodiesel Blends in Emission Characteristics 145 8.3 Mechanisms of Nanoparticles in Combustion Improvement 147 8.4 Biodiesel–Diesel Blends Nanoparticle Method 148 8.4.1 Limitations 150 8.4.2 Future Work 151 8.5 Conclusion 151 Author Contributions 152 Statement of Interest 152 Acknowledgment 152 References 152 9 Hybrid Nanoparticles to Improve Solar-Based Energy Storage 161 Pethurajan Vigneshwaran, Abin Roy, B.S. Bibin, and Saboor Shaik 9.1 Introduction 161 9.2 Thermal Energy Storage Systems 162 9.2.1 Sensible Heat Storage 163 9.2.2 Latent Heat Storage (LHS) 164 9.2.2.1 Phase Change Material (PCM) 165 9.2.3 Thermochemical Energy Storage 166 9.3 Solar Energy Storage Systems 166 9.3.1 TES for Solar Energy Storage Systems 166 9.3.2 Latent Heat TES in Solar Energy Storage Systems 168 9.4 Role of Nanotechnology in Solar Energy Storage 169 9.4.1 Types of Nanoparticles 169 9.4.2 Nanoparticles in Thermal Energy Storage 170 9.4.2.1 Inorganic-Based Nanomaterials 172 9.4.2.2 Carbon-Based Nanomaterials 172 9.4.2.3 Hybrid Nanomaterials 172 9.5 Applications of Nanoparticles in Solar Energy Storage 173 9.5.1 Solar Collectors 173 9.5.2 Solar Thermal Energy Conversion 174 9.5.3 Solar Photovoltaic System 175 9.5.4 Solar Heater 175 9.5.5 Solar Desalination 176 9.5.6 Other Applications 176 9.6 Conclusions and Future Recommendations 176 References 178 10 Application of Artificial Intelligence to Model-Predict the Thermo-physical Property of Hybrid Nanofluids 185 Prabhakar Sharma, Sanjeev Kumar, and Zafar Said 10.1 Introduction 185 10.2 Materials and Methods 188 10.2.1 Synthesis 188 10.2.2 Machine Learning 188 10.2.2.1 Linear Regression 189 10.2.2.2 Tweedie Regression 189 10.2.2.3 Huber Regression 189 10.2.2.4 Extreme Gradient Boosting 190 10.3 Results and Discussion 191 10.3.1 Data Analysis and Correlation 191 10.3.2 Linear Regression Model 193 10.3.3 Huber Regression Model 195 10.3.4 Tweedie Regression Model 195 10.3.5 XGBoost Model 198 10.3.6 Model Comparison 200 10.4 Conclusion 202 References 202 11 Optimization of Nanofluids for Heat Exchangers: Dealing with Sedimentation and Pump Losses 209 Nikhil S. Mane, Sayantan Mukherjee, Redhwan Almuzaiqer, and Niteen Bhirud 11.1 Introduction 209 11.2 Sedimentation 211 11.3 Pump Losses 216 11.4 Thermo-Economic Aspect of the Nanofluids 217 11.5 Conclusion 219 References 221 12 Clean Combustion with Biogas and Nano-Biodiesel in CI Engines 225 S. Lalhriatpuia, Md. Gulam Mustafa, and Lalhmingsanga Hauchhum 12.1 Introduction 225 12.2 Materials and Methods 228 12.2.1 Engine Specifications 228 12.2.2 Experimental Design 228 12.2.3 Fuel Properties 230 12.3 Modeling and Optimization 231 12.3.1 RSM Modeling 231 12.3.2 ANN Modeling 232 12.3.3 Optimization of RSM and ANN model 233 12.4 Results and Discussion 236 12.4.1 RSM Model Analysis 236 12.4.2 ANN Model Analysis 240 12.4.3 Optimization of RSM and ANN Model 241 12.5 Conclusions 243 References 244 13 A Differentiation of Energy Storage Methods 247 H. Bahuruteen Ali Ahamadu, K Arun, S Arivazhagan, N Sendhil Kumar, S. Kaliappan, and M. D. Rajkamal 13.1 Introduction 247 13.1.1 Conventional Energy Storage 248 13.1.2 Mechanical Energy Storage 248 13.1.3 Electrical Energy Storage 249 13.1.4 Electrochemical Energy Storage 249 13.1.5 Thermal Energy Storage 250 13.1.6 Characteristics of Thermal Energy Storage 250 13.1.7 Sensible Heat Storage 251 13.1.8 Aquifer Thermal Energy Storage 252 13.1.9 Hot Water Energy Storage 253 13.1.10 Cavern Energy Storage 254 13.1.11 Gravel Energy Storage 254 13.1.12 Molten Salt Energy Storage 255 13.1.13 Borehole Energy Storage 255 13.1.14 Packed-Bed Energy Storage 255 13.1.15 Latent Heat Storage 256 13.1.15.1 Latent Heat Energy Storage by Phase Change Material 256 13.1.15.2 Encapsulation of PCM 257 13.1.15.3 Latent Heat Energy Storage by Salt Hydrates 257 13.1.16 Thermochemical Energy Storage 258 13.2 Artificial Intelligence (AI) 258 13.2.1 AI in Energy Sector 258 13.2.1.1 Artificial Neural Network (ANN) 259 13.2.1.2 Fuzzy Logic (FL) 259 13.2.1.3 Adaptive Neuro Fuzzy Inference System (ANFIS) 259 13.2.1.4 Particle Swarm Optimization (PSO) 260 13.2.1.5 Support Vector Machine (SVM) 260 13.2.1.6 Implementation of AI in Energy Storage 260 13.3 Conclusion 260 References 262 14 Application of IoT and Machine Learning to Improve Biogas Production Through Anaerobic Digestion 267 Akshay Jain and Bhaskor Jyoti Bora 14.1 Introduction 268 14.2 Biogas Production 268 14.3 Techniques for Biogas Production Enhancement 269 14.4 Literature Review 270 14.5 Implementation of Mathematical Techniques for Biogas Production Enhancement 273 14.6 Conclusion 275 References 276 Index 281