Machine Learning for Sustainable Energy Solutions

Zafar Said, PhD, is a Mechanical and Aerospace Engineering Associate Professor at UAE University. With over AED six million in research funding, he has led industry-focused projects with SEWA, Tabreed, and Masdar, advancing innovations in nanofluids, solar energy, AI, and low-carbon fuels. Prabhakar Sharma, PhD, is an assistant professor at Delhi Skill and Entrepreneurship University, Delhi, India. He has 30 years of combined experience in academia and industry.

• List of Contributors xiiPreface xvi1 Green Energy-Led Sustainable Development: Barriers and Opportunities 1Arni Gesselle M. Pornea, and Hussein Safwat Hasan Hasan1.1 Introduction 11.2 The Current Landscape of Green Energy 31.2.1 Green Energy Types and Technologies 31.2.2 Global Green Energy Usage Statistics 31.3 Barriers to Green Energy Implementation 41.3.1 Economic and Financial Challenges 41.3.1.1 High Initial Costs 51.3.1.2 Investment Risks 51.3.2 Regulatory and Policy Frameworks 51.3.3 Social Acceptance and Cultural Factors 61.3.4 Technological Barriers 61.4 ml and AI in Green Energy 71.4.1 Technological Assessment and Optimization 71.4.2 Predictive Net-Zero Initiative 81.4.3 Enhancing Energy Storage Systems 91.4.4 Energy Demand and Supply Forecasting 91.4.5 Setting Ambitious Goal 101.4.6 Activate Support and Financial Investment 101.5 Challenges in the Integration of ML and AI in Renewable Energy 101.6 Directive in ML and AI Improvement Toward Its Application 111.6.1 Workforce Capacity Increase 121.6.2 Large-Scale Project Implementation 121.6.3 Public Awareness 121.6.4 Continuous Progress Monitoring and Strategies Adjustment 121.7 Conclusion 13References 142 Machine Learning-Driven Valorization of Organic Waste for Sustainable Bio-Hydrogen Production 17Munusamy Arun, Debabrata Barik, and Sreejesh S. R. Chandran2.1 Introduction 172.1.1 Objectives 182.2 Literature Review 182.3 Proposed Method 192.4 Results and Discussion 252.4.1 Bio-Hydrogen Production Efficiency Analysis 252.4.2 Performance Analysis 252.4.3 Adaptability Analysis 272.5 Conclusion 28Author Contributions 29Acknowledgment 29Data Availability Statement 29Funding Statement 29Conflict of Interest 29References 303 Application of Neural Networks for Model Prediction of Combustion and Emissions in Diesel Engines 33Parampreet Singh Jassal, Sridhar Sahoo, and Neeraj Kumbhakarna3.1 Introduction 343.2 Artificial Neural Networks 353.2.1 Types of Artificial Neural Networks 373.3 AI and ANN in Internal Combustion Engines 393.4 ANN in Diesel Engines 403.4.1 ANN for Different Fuel Properties 413.4.2 ANN for Diesel Engine Performance 413.4.3 ANN for Diesel Engines Using Biodiesel Blends 433.4.4 ANN for Gaseous Fuels 473.4.4.1 MISO Model Studies 483.4.4.2 MIMO Model Studies 483.4.4.3 Comparative Studies 483.4.5 ANN for HCCI Engines 503.5 Conclusions 52References 534 Enhanced Energy Storage with Hybrid Nanoparticles and Machine Learning for Energy Sustainability 59Arun Munusamy, Debabrata Barik, and Sreejesh S.R. Chandran4.1 Introduction 594.2 Materials and Methods 614.3 Result and Discussion 674.4 Conclusion 69Author Contributions 70Acknowledgment 70Data Availability Statement 70Funding 70Conflict of Interest 70References 715 Model Prediction of Biomass Gasification Using Support Vector Machines 73Arun Munusamy and Debabrata Barik5.1 Introduction and Literature Survey 735.2 Materials and Methods 765.3 Results and Discussion 835.4 Conclusion 89References 906 Role of Machine Learning Techniques in Modeling and Optimization of Biomass Gasification Parameters in a Downdraft Gasifier 93Vikas Attri and Avdhesh Kr. Sharma6.1 Introduction 936.2 Biomass Gasification 946.2.1 Gasification Process 956.2.2 Gasification Parameters 976.2.2.1 Biomass Characterization 986.2.2.2 Equivalence Ratio 986.2.2.3 Gasification Temperature 996.2.2.4 Biomass Consumption Rate 996.2.2.5 Cold Gas Efficiency (CGE) 996.2.2.6 Importance of Various Gasifying Agents in the Gasification Process 996.2.2.7 Effect of the Gasification Parameters on the Producer Gas 1006.3 Machine Learning Techniques in Biomass Gasification 1006.3.1 Gaussian Process Regression 1016.3.2 Support Vector Machines 1016.3.3 Artificial Neural Network 1036.3.4 Decision Trees 1056.4 Model Performance Metrics 1056.5 Application of the ML Model in Biomass Gasification 1066.6 Challenges and Prospects 1076.7 Conclusion 108References 1097 Response Surface Methodology-Based Multiattribute Optimization of a Hydrogen-Powered Dual-Fuel Engines 115Sanjeev Kumar, Prabhu Paramasivam, and Abdul Razak7.1 Introduction 1157.2 Materials and Methods 1187.2.1 Test Engine Setup and Fuel 1187.2.2 Analysis of Variance 1197.2.3 Response Surface Methodology 1207.3 Results and Discussion 1217.3.1 Correlation Analysis 1217.3.2 Analysis of Variance 1217.3.3 Surface Diagrams and Predictions 1267.3.4 Parametric Optimization 1347.4 Conclusion 135References 1358 Addition of Nanoparticles to Biodiesel–Diesel Blends to Improve Engine Efficiency and Reduce Tailpipe Emission 139Mudasar Zafar, Abida Hussain, Tauseef Ahmed, Ahmed Daabo, and Farman Ullah8.1 Introduction 1398.2 Background and Performance of Biodiesel Blends in Engine Efficiency 1418.2.1 Properties of the Biodiesel 1428.2.2 Performance 1448.2.3 Performance of Biodiesel Blends in Emission Characteristics 1458.3 Mechanisms of Nanoparticles in Combustion Improvement 1478.4 Biodiesel–Diesel Blends Nanoparticle Method 1488.4.1 Limitations 1508.4.2 Future Work 1518.5 Conclusion 151Author Contributions 152Statement of Interest 152Acknowledgment 152References 1529 Hybrid Nanoparticles to Improve Solar-Based Energy Storage 161Pethurajan Vigneshwaran, Abin Roy, B.S. Bibin, and Saboor Shaik9.1 Introduction 1619.2 Thermal Energy Storage Systems 1629.2.1 Sensible Heat Storage 1639.2.2 Latent Heat Storage (LHS) 1649.2.2.1 Phase Change Material (PCM) 1659.2.3 Thermochemical Energy Storage 1669.3 Solar Energy Storage Systems 1669.3.1 TES for Solar Energy Storage Systems 1669.3.2 Latent Heat TES in Solar Energy Storage Systems 1689.4 Role of Nanotechnology in Solar Energy Storage 1699.4.1 Types of Nanoparticles 1699.4.2 Nanoparticles in Thermal Energy Storage 1709.4.2.1 Inorganic-Based Nanomaterials 1729.4.2.2 Carbon-Based Nanomaterials 1729.4.2.3 Hybrid Nanomaterials 1729.5 Applications of Nanoparticles in Solar Energy Storage 1739.5.1 Solar Collectors 1739.5.2 Solar Thermal Energy Conversion 1749.5.3 Solar Photovoltaic System 1759.5.4 Solar Heater 1759.5.5 Solar Desalination 1769.5.6 Other Applications 1769.6 Conclusions and Future Recommendations 176References 17810 Application of Artificial Intelligence to Model-Predict the Thermo-physical Property of Hybrid Nanofluids 185Prabhakar Sharma, Sanjeev Kumar, and Zafar Said10.1 Introduction 18510.2 Materials and Methods 18810.2.1 Synthesis 18810.2.2 Machine Learning 18810.2.2.1 Linear Regression 18910.2.2.2 Tweedie Regression 18910.2.2.3 Huber Regression 18910.2.2.4 Extreme Gradient Boosting 19010.3 Results and Discussion 19110.3.1 Data Analysis and Correlation 19110.3.2 Linear Regression Model 19310.3.3 Huber Regression Model 19510.3.4 Tweedie Regression Model 19510.3.5 XGBoost Model 19810.3.6 Model Comparison 20010.4 Conclusion 202References 20211 Optimization of Nanofluids for Heat Exchangers: Dealing with Sedimentation and Pump Losses 209Nikhil S. Mane, Sayantan Mukherjee, Redhwan Almuzaiqer, and Niteen Bhirud11.1 Introduction 20911.2 Sedimentation 21111.3 Pump Losses 21611.4 Thermo-Economic Aspect of the Nanofluids 21711.5 Conclusion 219References 22112 Clean Combustion with Biogas and Nano-Biodiesel in CI Engines 225S. Lalhriatpuia, Md. Gulam Mustafa, and Lalhmingsanga Hauchhum12.1 Introduction 22512.2 Materials and Methods 22812.2.1 Engine Specifications 22812.2.2 Experimental Design 22812.2.3 Fuel Properties 23012.3 Modeling and Optimization 23112.3.1 RSM Modeling 23112.3.2 ANN Modeling 23212.3.3 Optimization of RSM and ANN model 23312.4 Results and Discussion 23612.4.1 RSM Model Analysis 23612.4.2 ANN Model Analysis 24012.4.3 Optimization of RSM and ANN Model 24112.5 Conclusions 243References 24413 A Differentiation of Energy Storage Methods 247H. Bahuruteen Ali Ahamadu, K Arun, S Arivazhagan, N Sendhil Kumar, S. Kaliappan, and M. D. Rajkamal13.1 Introduction 24713.1.1 Conventional Energy Storage 24813.1.2 Mechanical Energy Storage 24813.1.3 Electrical Energy Storage 24913.1.4 Electrochemical Energy Storage 24913.1.5 Thermal Energy Storage 25013.1.6 Characteristics of Thermal Energy Storage 25013.1.7 Sensible Heat Storage 25113.1.8 Aquifer Thermal Energy Storage 25213.1.9 Hot Water Energy Storage 25313.1.10 Cavern Energy Storage 25413.1.11 Gravel Energy Storage 25413.1.12 Molten Salt Energy Storage 25513.1.13 Borehole Energy Storage 25513.1.14 Packed-Bed Energy Storage 25513.1.15 Latent Heat Storage 25613.1.15.1 Latent Heat Energy Storage by Phase Change Material 25613.1.15.2 Encapsulation of PCM 25713.1.15.3 Latent Heat Energy Storage by Salt Hydrates 25713.1.16 Thermochemical Energy Storage 25813.2 Artificial Intelligence (AI) 25813.2.1 AI in Energy Sector 25813.2.1.1 Artificial Neural Network (ANN) 25913.2.1.2 Fuzzy Logic (FL) 25913.2.1.3 Adaptive Neuro Fuzzy Inference System (ANFIS) 25913.2.1.4 Particle Swarm Optimization (PSO) 26013.2.1.5 Support Vector Machine (SVM) 26013.2.1.6 Implementation of AI in Energy Storage 26013.3 Conclusion 260References 26214 Application of IoT and Machine Learning to Improve Biogas Production Through Anaerobic Digestion 267Akshay Jain and Bhaskor Jyoti Bora14.1 Introduction 26814.2 Biogas Production 26814.3 Techniques for Biogas Production Enhancement 26914.4 Literature Review 27014.5 Implementation of Mathematical Techniques for Biogas Production Enhancement 27314.6 Conclusion 275References 276Index 281