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Full Description
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.
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
