Fuelling the Future: Intelligent Approaches for Harnessing Hydrogen Energy explores the transformative potential of hydrogen as a clean, renewable energy source. The opening chapter delves into the global need to shift towards sustainable energy solutions, emphasizing hydrogen’s role in reducing carbon emissions and supporting the transition to a greener economy. It introduces machine learning as a cutting-edge tool for solving complex challenges associated with hydrogen production and utilization, offering a glimpse into a smarter, data-driven approach to energy management. It also navigates key issues such as scalability, cost-efficiency, and environmental impacts, ensuring a holistic understanding of hydrogen’s future in energy systems.
Beyond optimization, the book addresses practical applications of hydrogen energy in transportation, industry, and storage.
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Dr. Deepanraj Balakrishnan is currently working as Research Faculty in the Department of Mechanical Engineering, Prince Mohammad Bin Fahd University, Saudi Arabia. He has extensive teaching and research experience and has contributed to numerous works from publishers of international repute as an author and editor. His area of interest includes Renewable Energy Utilization; Energy Conservation and Waste Management with special focus on Bio-Energy.
Dr Prabhakar Sharma is currently working as Assistant Professor in the Department of Mechanical Engineering, Delhi Skill and Entrepreneurship University, India. He has 13 years of academic experience and 15 Years industrial experience in the aviation sector. His research interests include renewable energy, hydrogen energy, Machine learning, and Optimization.
Fuelling the Future: Intelligent Approaches for Harnessing Hydrogen Energy presents insights into the use of machine learning tools to optimize hydrogen-based energy systems. This comprehensive guide explores the dynamic synergy between hydrogen energy solutions and machine learning applications, offering a roadmap for a sustainable and intelligent energy future. The book navigates the evolving landscape of hydrogen technologies, from production and storage to transportation and industrial applications with machine learning algorithms in optimizing efficiency, predictive analytics, and decision-making processes across the hydrogen value chain. The book presents a thorough examination of several machine learning algorithms applicable to hydrogen energy applications. A full explanation is given on how each strategy can be effectively used, allowing readers to pick and adopt the most appropriate approach for their circumstance. A series of real-world case studies demonstrates effective machine-learning applications in various hydrogen energy projects. These instances provide readers with useful insights into implementation tactics, problems encountered, and outcomes obtained, allowing them to draw practical lessons for their initiatives. Moreover, industry standards are integrated throughout the book, advising readers on compliance and best practices by recognizing the need to align with existing industry standards and regulations, helping professionals navigate the complicated regulatory landscape and modify machine learning solutions to suit industry standards. Fuelling the Future: Intelligent Approaches for Harnessing Hydrogen Energy serves as a strategic guide for students, researchers, and professionals to understand and capitalize on the transformational potential of incorporating machine learning into hydrogen technology.
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