11026nam 22006133 450 991104892010332120251218080335.01-394-26743-61-394-26742-8(MiAaPQ)EBC32456551(Au-PeEL)EBL32456551(CKB)44267373200041(OCoLC)1569120250(EXLCZ)994426737320004120251218d2026 uy 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierMachine Learning for Sustainable Energy Solutions1st ed.Newark :John Wiley & Sons, Incorporated,2026.©2026.1 online resource (307 pages)1-394-26740-1 Cover -- Title Page -- Copyright Page -- Contents -- List of Contributors -- Preface -- Chapter 1 Green Energy-Led Sustainable Development: Barriers and Opportunities -- 1.1 Introduction -- 1.2 The Current Landscape of Green Energy -- 1.2.1 Green Energy Types and Technologies -- 1.2.2 Global Green Energy Usage Statistics -- 1.3 Barriers to Green Energy Implementation -- 1.3.1 Economic and Financial Challenges -- 1.3.1.1 High Initial Costs -- 1.3.1.2 Investment Risks -- 1.3.2 Regulatory and Policy Frameworks -- 1.3.3 Social Acceptance and Cultural Factors -- 1.3.4 Technological Barriers -- 1.4 ML and AI in Green Energy -- 1.4.1 Technological Assessment and Optimization -- 1.4.2 Predictive Net-Zero Initiative -- 1.4.3 Enhancing Energy Storage Systems -- 1.4.4 Energy Demand and Supply Forecasting -- 1.4.5 Setting Ambitious Goal -- 1.4.6 Activate Support and Financial Investment -- 1.5 Challenges in the Integration of ML and AI in Renewable Energy -- 1.6 Directive in ML and AI Improvement Toward Its.Application -- 1.6.1 Workforce Capacity Increase -- 1.6.2 Large-Scale Project Implementation -- 1.6.3 Public Awareness -- 1.6.4 Continuous Progress Monitoring and Strategies Adjustment -- 1.7 Conclusion -- References -- Chapter 2 Machine Learning-Driven Valorization of Organic Waste for Sustainable Bio-Hydrogen Production -- 2.1 Introduction -- 2.1.1 Objectives -- 2.2 Literature Review -- 2.3 Proposed Method -- 2.4 Results and Discussion -- 2.4.1 Bio-Hydrogen Production Efficiency Analysis -- 2.4.2 Performance Analysis -- 2.4.3 Adaptability Analysis -- 2.5 Conclusion -- Author Contributions -- Acknowledgment -- Data Availability Statement -- Funding Statement -- Conflict of Interest -- References -- Chapter 3 Application of Neural Networks for Model Prediction of Combustion and Emissions in Diesel Engines -- 3.1 Introduction.3.2 Artificial Neural Networks -- 3.2.1 Types of Artificial Neural Networks -- 3.3 AI and ANN in Internal Combustion Engines -- 3.4 ANN in Diesel Engines -- 3.4.1 ANN for Different Fuel Properties -- 3.4.2 ANN for Diesel Engine Performance -- 3.4.3 ANN for Diesel Engines Using Biodiesel Blends -- 3.4.4 ANN for Gaseous Fuels -- 3.4.4.1 MISO Model Studies -- 3.4.4.2 MIMO Model Studies -- 3.4.4.3 Comparative Studies -- 3.4.5 ANN for HCCI Engines -- 3.5 Conclusions -- References -- Chapter 4 Enhanced Energy Storage with Hybrid Nanoparticles and Machine Learning for Energy Sustainability -- 4.1 Introduction -- 4.2 Materials and Methods -- 4.3 Result and Discussion -- 4.4 Conclusion -- Author Contributions -- Acknowledgment -- Data Availability Statement -- Funding -- Conflict of Interest -- References -- Chapter 5 Model Prediction of Biomass Gasification Using Support Vector Machines -- 5.1 Introduction and Literature Survey -- 5.2 Materials and Methods -- 5.3 Results and Discussion -- 5.4 Conclusion -- References -- Chapter 6 Role of Machine Learning Techniques in Modeling and Optimization of Biomass Gasification Parameters in a Downdraft Gasifier -- 6.1 Introduction -- 6.2 Biomass Gasification -- 6.2.1 Gasification Process -- 6.2.2 Gasification Parameters -- 6.2.2.1 Biomass Characterization -- 6.2.2.2 Equivalence Ratio -- 6.2.2.3 Gasification Temperature -- 6.2.2.4 Biomass Consumption Rate -- 6.2.2.5 Cold Gas Efficiency (CGE) -- 6.2.2.6 Importance of Various Gasifying Agents in the Gasification Process -- 6.2.2.7 Effect of the Gasification Parameters on the Producer Gas -- 6.3 Machine Learning Techniques in Biomass Gasification -- 6.3.1 Gaussian Process Regression -- 6.3.2 Support Vector Machines -- 6.3.3 Artificial Neural Network -- 6.3.4 Decision Trees -- 6.4 Model Performance Metrics -- 6.5 Application of the ML Model in Biomass Gasification.6.6 Challenges and Prospects -- 6.7 Conclusion -- References -- Chapter 7 Response Surface Methodology-Based Multiattribute Optimization of a Hydrogen-Powered Dual-Fuel Engines -- 7.1 Introduction -- 7.2 Materials and Methods -- 7.2.1 Test Engine Setup and Fuel -- 7.2.2 Analysis of Variance -- 7.2.3 Response Surface Methodology -- 7.3 Results and Discussion -- 7.3.1 Correlation Analysis -- 7.3.2 Analysis of Variance -- 7.3.3 Surface Diagrams and Predictions -- 7.3.4 Parametric Optimization -- 7.4 Conclusion -- References -- Chapter 8 Addition of Nanoparticles to Biodiesel-Diesel Blends to Improve Engine Efficiency and Reduce Tailpipe Emission -- 8.1 Introduction -- 8.2 Background and Performance of Biodiesel Blends in Engine Efficiency -- 8.2.1 Properties of the Biodiesel -- 8.2.2 Performance -- 8.2.3 Performance of Biodiesel Blends in Emission Characteristics -- 8.3 Mechanisms of Nanoparticles in Combustion Improvement -- 8.4 Biodiesel-Diesel Blends Nanoparticle Method -- 8.4.1 Limitations -- 8.4.2 Future Work -- 8.5 Conclusion -- Author Contributions -- Statement of Interest -- Acknowledgment -- References -- Chapter 9 Hybrid Nanoparticles to Improve Solar-Based Energy Storage -- 9.1 Introduction -- 9.2 Thermal Energy Storage Systems -- 9.2.1 Sensible Heat Storage -- 9.2.2 Latent Heat Storage (LHS) -- 9.2.2.1 Phase Change Material (PCM) -- 9.2.3 Thermochemical Energy Storage -- 9.3 Solar Energy Storage Systems -- 9.3.1 TES for Solar Energy Storage Systems -- 9.3.2 Latent Heat TES in Solar Energy Storage Systems -- 9.4 Role of Nanotechnology in Solar Energy Storage -- 9.4.1 Types of Nanoparticles -- 9.4.2 Nanoparticles in Thermal Energy Storage -- 9.4.2.1 Inorganic-Based Nanomaterials -- 9.4.2.2 Carbon-Based Nanomaterials -- 9.4.2.3 Hybrid Nanomaterials -- 9.5 Applications of Nanoparticles in Solar Energy Storage -- 9.5.1 Solar Collectors.9.5.2 Solar Thermal Energy Conversion -- 9.5.3 Solar Photovoltaic System -- 9.5.4 Solar Heater -- 9.5.5 Solar Desalination -- 9.5.6 Other Applications -- 9.6 Conclusions and Future Recommendations -- References -- Chapter 10 Application of Artificial Intelligence to Model-Predict the Thermo-physical Property of Hybrid Nanofluids -- 10.1 Introduction -- 10.2 Materials and Methods -- 10.2.1 Synthesis -- 10.2.2 Machine Learning -- 10.2.2.1 Linear Regression -- 10.2.2.2 Tweedie Regression -- 10.2.2.3 Huber Regression -- 10.2.2.4 Extreme Gradient Boosting -- 10.3 Results and Discussion -- 10.3.1 Data Analysis and Correlation -- 10.3.2 Linear Regression Model -- 10.3.3 Huber Regression Model -- 10.3.4 Tweedie Regression Model -- 10.3.5 XGBoost Model -- 10.3.6 Model Comparison -- 10.4 Conclusion -- References -- Chapter 11 Optimization of Nanofluids for Heat Exchangers: Dealing with Sedimentation and Pump Losses -- 11.1 Introduction -- 11.2 Sedimentation -- 11.3 Pump Losses -- 11.4 Thermo-Economic Aspect of the Nanofluids -- 11.5 Conclusion -- References -- Chapter 12 Clean Combustion with Biogas and Nano-Biodiesel in CI Engines -- 12.1 Introduction -- 12.2 Materials and Methods -- 12.2.1 Engine Specifications -- 12.2.2 Experimental Design -- 12.2.3 Fuel Properties -- 12.3 Modeling and Optimization -- 12.3.1 RSM Modeling -- 12.3.2 ANN Modeling -- 12.3.3 Optimization of RSM and ANN model -- 12.4 Results and Discussion -- 12.4.1 RSM Model Analysis -- 12.4.2 ANN Model Analysis -- 12.4.3 Optimization of RSM and ANN Model -- 12.5 Conclusions -- References -- Chapter 13 A Differentiation of Energy Storage Methods -- 13.1 Introduction -- 13.1.1 Conventional Energy Storage -- 13.1.2 Mechanical Energy Storage -- 13.1.3 Electrical Energy Storage -- 13.1.4 Electrochemical Energy Storage -- 13.1.5 Thermal Energy Storage.13.1.6 Characteristics of Thermal Energy Storage -- 13.1.7 Sensible Heat Storage -- 13.1.8 Aquifer Thermal Energy Storage -- 13.1.9 Hot Water Energy Storage -- 13.1.10 Cavern Energy Storage -- 13.1.11 Gravel Energy Storage -- 13.1.12 Molten Salt Energy Storage -- 13.1.13 Borehole Energy Storage -- 13.1.14 Packed-Bed Energy Storage -- 13.1.15 Latent Heat Storage -- 13.1.15.1 Latent Heat Energy Storage by Phase Change Material -- 13.1.15.2 Encapsulation of PCM -- 13.1.15.3 Latent Heat Energy Storage by Salt Hydrates -- 13.1.16 Thermochemical Energy Storage -- 13.2 Artificial Intelligence (AI) -- 13.2.1 AI in Energy Sector -- 13.2.1.1 Artificial Neural Network (ANN) -- 13.2.1.2 Fuzzy Logic (FL) -- 13.2.1.3 Adaptive Neuro Fuzzy Inference System (ANFIS) -- 13.2.1.4 Particle Swarm Optimization (PSO) -- 13.2.1.5 Support Vector Machine (SVM) -- 13.2.1.6 Implementation of AI in Energy Storage -- 13.3 Conclusion -- References -- Chapter 14 Application of IoT and Machine Learning to Improve Biogas Production Through Anaerobic Digestion -- 14.1 Introduction -- 14.2 Biogas Production -- 14.3 Techniques for Biogas Production Enhancement -- 14.4 Literature Review -- 14.5 Implementation of Mathematical Techniques for.Biogas Production Enhancement -- 14.6 Conclusion -- References -- Index -- EULA.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).Alternative fuelsData processingBiomass energyData processingMotor fuelsData processingEnergy storageMaterialsData processingThermofluid systemsMaterialsData processingDiesel motor exhaust gasEnvironmental aspectsData processingMachine learningIndustrial applicationsAlternative fuelsData processing.Biomass energyData processing.Motor fuelsData processing.Energy storageMaterialsData processing.Thermofluid systemsMaterialsData processing.Diesel motor exhaust gasEnvironmental aspectsData processing.Machine learningIndustrial applications.Said Zafar1872332MiAaPQMiAaPQMiAaPQBOOK9911048920103321Machine Learning for Sustainable Energy Solutions4519437UNINA