Overview

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.

Full Product Details

Author:   Zafar Said (University of Sharjah, Unites Arab Emirates) ,  Prabhakar Sharma (Delhi Skill and Entrepreneurship University, India)
Publisher:   John Wiley & Sons Inc
Imprint:   John Wiley & Sons Inc
ISBN:  

9781394267408

ISBN 10:   1394267401
Pages:   304
Publication Date:   11 December 2025
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Hardback
Publisher's Status:   Active
Availability:   Awaiting stock  
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Table of Contents

List of Contributors Preface Chapter 1 Green energy led sustainable development: Barriers and opportunities Chapter 2 Machine Learning Driven Valorization of Organic Waste for Sustainable Bio-hydrogen Production Chapter 3 Application of Neural Networks for Model-Prediction of Combustion and Emissions in Diesel Engines Chapter 4 Enhanced Energy Storage with Hybrid Nanoparticles and Machine Learning for Energy Sustainability Chapter 5 Model prediction of biomass gasification using support vector machines Chapter 6 Role of machine learning techniques in modeling and optimization of biomass gasification parameters in downdraft gasifier Chapter 7 Response surface methodology-based multi-attribute optimization of a hydrogen powered dual-fuel engines Chapter 8 Addition of Nanoparticles in Biodiesel–Diesel Blends to Improve Engine Efficiency and Reduce Tailpipe Emission Chapter 9 Hybrid Nanoparticles to improve solar based energy storage Chapter 10 Application of artificial intelligence to model-predict the thermo-physical property of hybrid nanofluids Chapter 11 Optimization of Nanofluids for Heat Exchangers: Dealing with Sedimentation and Pump Losses Chapter 12 Navigating the Green Combustion Landscape: Optimizing Emissions and Performance in CI Engines Fuelled by Biogas and Nanoparticle-Doped Biodiesel Chapter 13 A Differentiation Of Energy Storage Methods Chapter 14 Application of IoT and Machine Learning to Improve Biogas Production Through Anaerobic Digestion Chapter 15 Optimization of the Biogas Production Process: Role of Mathematical Modeling and Artificial Intelligence Index

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Author Information

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.

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