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[null,"1.","Record Nr.",null,"UNINA9911048920103321"]
[null,null,"Autore",null,"Said Zafar"]
[null,null,"Titolo",null,"Machine Learning for Sustainable Energy Solutions"]
[null,null,"Pubbl/distr/stampa",null,"Newark : , : John Wiley & Sons, Incorporated, , 2026"]
[{"$nulls":4},"©2026"]
[null,null,"ISBN",null,"1-394-26743-6"]
[{"$nulls":4},"1-394-26742-8"]
[null,null,"Edizione",null,"[1st ed.]"]
[null,null,"Descrizione fisica",null,"1 online resource (307 pages)"]
[null,null,"Soggetti",null,"Alternative fuels - Data processing"]
[{"$nulls":4},"Biomass energy - Data processing"]
[{"$nulls":4},"Motor fuels - Data processing"]
[{"$nulls":4},"Energy storage - Materials - Data processing"]
[{"$nulls":4},"Thermofluid systems - Materials - Data processing"]
[{"$nulls":4},"Diesel motor exhaust gas - Environmental aspects - Data processing"]
[{"$nulls":4},"Machine learning - Industrial applications"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,"Nota di contenuto",null,"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 "]
[{"$nulls":4},"Conclusion -- References -- Chapter 2 Machine Learning-Driven Valorization of Organic Waste for Sustainable Bio-Hydrogen Production -- 2.1 Introduction -- 2.1.1 Objectives -- 2.2 Literature Review -- 2.3 Proposed Method -- 2.4 Results and Discussion -- 2.4.1 Bio-Hydrogen Production Efficiency Analysis -- 2.4.2 Performance Analysis -- 2.4.3 Adaptability Analysis -- 2.5 Conclusion -- Author Contributions -- Acknowledgment -- Data Availability Statement -- Funding Statement -- Conflict of Interest -- References -- Chapter 3 Application of Neural Networks for Model Prediction of Combustion and Emissions in Diesel Engines -- 3.1 Introduction."]
[{"$nulls":4},"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."]
[{"$nulls":4},"6.6 Challenges and Prospects -- 6.7 Conclusion -- References -- Chapter 7 Response Surface Methodology-Based Multiattribute Optimization of a Hydrogen-Powered Dual-Fuel Engines -- 7.1 Introduction -- 7.2 Materials and Methods -- 7.2.1 Test Engine Setup and Fuel -- 7.2.2 Analysis of Variance -- 7.2.3 Response Surface Methodology -- 7.3 Results and Discussion -- 7.3.1 Correlation Analysis -- 7.3.2 Analysis of Variance -- 7.3.3 Surface Diagrams and Predictions -- 7.3.4 Parametric Optimization -- 7.4 Conclusion -- References -- Chapter 8 Addition of Nanoparticles to Biodiesel-Diesel Blends to Improve Engine Efficiency and Reduce Tailpipe Emission -- 8.1 Introduction -- 8.2 Background and Performance of Biodiesel Blends in Engine Efficiency -- 8.2.1 Properties of the Biodiesel -- 8.2.2 Performance -- 8.2.3 Performance of Biodiesel Blends in Emission Characteristics -- 8.3 Mechanisms of Nanoparticles in Combustion Improvement -- 8.4 Biodiesel-Diesel Blends Nanoparticle Method -- 8.4.1 Limitations -- 8.4.2 Future Work -- 8.5 Conclusion -- Author Contributions -- Statement of Interest -- Acknowledgment -- References -- Chapter 9 Hybrid Nanoparticles to Improve Solar-Based Energy Storage -- 9.1 Introduction -- 9.2 Thermal Energy Storage Systems -- 9.2.1 Sensible Heat Storage -- 9.2.2 Latent Heat Storage "]
[{"$nulls":4},"(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."]
[{"$nulls":4},"9.5.2 Solar Thermal Energy Conversion -- 9.5.3 Solar Photovoltaic System -- 9.5.4 Solar Heater -- 9.5.5 Solar Desalination -- 9.5.6 Other Applications -- 9.6 Conclusions and Future Recommendations -- References -- Chapter 10 Application of Artificial Intelligence to Model-Predict the Thermo-physical Property of Hybrid Nanofluids -- 10.1 Introduction -- 10.2 Materials and Methods -- 10.2.1 Synthesis -- 10.2.2 Machine Learning -- 10.2.2.1 Linear Regression -- 10.2.2.2 Tweedie Regression -- 10.2.2.3 Huber Regression -- 10.2.2.4 Extreme Gradient Boosting -- 10.3 Results and Discussion -- 10.3.1 Data Analysis and Correlation -- 10.3.2 Linear Regression Model -- 10.3.3 Huber Regression Model -- 10.3.4 Tweedie Regression Model -- 10.3.5 XGBoost Model -- 10.3.6 Model Comparison -- 10.4 Conclusion -- References -- Chapter 11 Optimization of Nanofluids for Heat Exchangers: Dealing with Sedimentation and Pump Losses -- 11.1 Introduction -- 11.2 Sedimentation -- 11.3 Pump Losses -- 11.4 Thermo-Economic Aspect of the Nanofluids -- 11.5 Conclusion -- References -- Chapter 12 Clean Combustion with Biogas and Nano-Biodiesel in CI Engines -- 12.1 Introduction -- 12.2 Materials and Methods -- 12.2.1 Engine Specifications -- 12.2.2 Experimental Design -- 12.2.3 Fuel Properties -- 12.3 Modeling and Optimization -- 12.3.1 RSM Modeling -- 12.3.2 ANN Modeling -- 12.3.3 Optimization of RSM and ANN model -- 12.4 Results and Discussion -- 12.4.1 RSM Model Analysis -- 12.4.2 ANN Model Analysis -- 12.4.3 Optimization of RSM and ANN Model -- 12.5 Conclusions -- References -- Chapter 13 A Differentiation of Energy Storage Methods -- 13.1 Introduction -- 13.1.1 Conventional Energy Storage -- 13.1.2 Mechanical Energy Storage -- 13.1.3 Electrical Energy Storage -- 13.1.4 Electrochemical Energy Storage -- 13.1.5 Thermal Energy Storage."]
[{"$nulls":4},"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."]
[null,null,null,"Sommario/riassunto","Comprehensive insights into integrating modern engineering "]
[{"$nulls":4},"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)."]