Data-Driven Machine Learning Applications in Thermochemical Conversion Processes delves into the prospect of machine learning applications to optimize and enhance advanced thermochemical conversion processes, which are essential for converting biomass into energy and other valuable products. This book covers ML applications in higher heating value (HHV) predictions, catalyst screening, prediction of biofuels properties, material discovery and screening, as well as advancing emerging thermochemical conversion process technologies. Providing an in-depth examination of how big data analytics and ML models can be harnessed to predict system performance, understand complex reaction mechanisms, and accelerate development of innovative conversion technologies, as well as focusing on both theoretical and practical aspects, this book will be a welcome reference for researchers, engineers, and practitioners.
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Jude Okolie is an Assistant Professor of Chemical Engineering at Bucknell University. He previously served as an Assistant Professor of Engineering Pathways at the University of Oklahoma. His research focuses on the thermochemical conversion of waste materials into green fuels and the utilization of hydrochar, biochar, and activated carbon for environmental remediation. His work also involves the application of process simulation, artificial intelligence, and machine learning to address challenges related to climate change, environmental pollution, and sustainable agriculture.
Dr. Okolie has received several prestigious local and international awards, including the George Ira Hanson Energy Award for his work on thermochemical hydrogen production and the USASK Service Award for his contributions to diversity and equity. He is a two-time recipient of the esteemed Engineering Devolved Scholarship at USASK for his outstanding contributions to clean energy research. Dr. Okolie also led the sustainability program in France and played a key role in developing the sustainable engineering course at Bucknell University.Dr. Adewale Giwa is a faculty member in the Chemical and Water Desalination Engineering Program at the University of Sharjah, UAE. He specializes in Chemical Engineering and Industrial Chemical Processes, teaching courses on fluid mechanics, thermal sciences, and system design. His research focuses on membrane technologies, sustainable water treatment, and the integration of renewable energy in chemical processes. Dr. Giwa has authored over 90 peer-reviewed publications and secured over $3 million in research funding. Recognized as a top scientist globally, he is currently leading a project with IBM to enhance water access monitoring and forecasting.
Data-Driven Machine Learning Applications in Thermochemical Conversion Processes delves into the prospect of machine learning applications to optimize and enhance advanced thermochemical conversion processes, which are essential for converting biomass into energy and other valuable products. This book covers ML applications in higher heating value (HHV) predictions, catalyst screening, prediction of biofuels properties, material discovery and screening, as well as advancing emerging thermochemical conversion process technologies. Providing an in-depth examination of how big data analytics and ML models can be harnessed to predict system performance, understand complex reaction mechanisms, and accelerate development of innovative conversion technologies, as well as focusing on both theoretical and practical aspects, this book will be a welcome reference for researchers, engineers, and practitioners.
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