Data-Driven Machine Learning Applications in Thermochemical Conversion Processes
- 1st Edition - March 1, 2026
- Editors: Jude Okolie, Adewale Giwa, Patrick Okoye, Bilainu Oboirien
- Language: English
- Paperback ISBN:9 7 8 - 0 - 4 4 3 - 3 3 3 7 2 - 9
- eBook ISBN:9 7 8 - 0 - 4 4 3 - 3 3 3 7 3 - 6
Data-Driven Machine Learning Applications in Thermochemical Conversion Processes delves into the prospect of machine learning applications to optimize and enhance advanced thermo… Read more
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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.
- Presents a comprehensive perspective by integrating the disciplines of geology, engineering, policy, and economics to provide a nuanced, comprehensive volume on the subject
- Bridges the gap between data science and thermochemical process engineering
- Spans foundational features and digs deeper on root causes and remedies to challenges and limitations to yield a practical publication for a varied audience
- Uses cutting-edge characterization and modelling tools along with novel methodologies to make the subject practical, easy-to-understand and implement
- Serves as a valuable resource for professionals, researchers, students, educators, and policymakers
Researcher, engineers, graduate students and professionals interested in thermochemical conversion processes and machine learning and educators interested in integrating machine learning into their curriculum in the following disciplines: Chemistry and Chemical Engineer Environmental Engineers Sustainability professionals Water Engineers Petroleum and Mining Engineers
1. Machine Learning Introduction
2. Higher Heating Value Prediction
3. Catalysts Screening and Optimization
4. Biochar Properties Prediction
5. Hydrothermal Gasification and Pyrolysis Process Conditions Optimization
6. Kinetics And Reaction Mechanism Study with Machine Learning
7. Machine Learning Applications in Combustion (Process Parameter Predictions and Image Processing)
8. Machine Learning Applications in Nanomaterial Preparation for Thermochemical Processes
9. Machine Learning Applications in Emerging Thermochemical Technologies
10. Integrating Machine Learning into Biorefinery Operations
11. Bioinformatics Approaches for Microbial-Driven Thermochemical Conversion
12. Machine Learning Applications in Microfluidic Thermochemical Reactors
13. Machine Learning for Advancing Techno-Economic and Lifecycle Assessment of Thermochemical Conversion Processes
14. Energy Efficiency and Heat Integration
15. Machine Learning Application in Feedstock Selection and Durability
2. Higher Heating Value Prediction
3. Catalysts Screening and Optimization
4. Biochar Properties Prediction
5. Hydrothermal Gasification and Pyrolysis Process Conditions Optimization
6. Kinetics And Reaction Mechanism Study with Machine Learning
7. Machine Learning Applications in Combustion (Process Parameter Predictions and Image Processing)
8. Machine Learning Applications in Nanomaterial Preparation for Thermochemical Processes
9. Machine Learning Applications in Emerging Thermochemical Technologies
10. Integrating Machine Learning into Biorefinery Operations
11. Bioinformatics Approaches for Microbial-Driven Thermochemical Conversion
12. Machine Learning Applications in Microfluidic Thermochemical Reactors
13. Machine Learning for Advancing Techno-Economic and Lifecycle Assessment of Thermochemical Conversion Processes
14. Energy Efficiency and Heat Integration
15. Machine Learning Application in Feedstock Selection and Durability
- Edition: 1
- Published: March 1, 2026
- Language: English
JO
Jude Okolie
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.
Affiliations and expertise
Department of Chemical Engineering, Bucknell University, Lewisburg, PA, USAAG
Adewale Giwa
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.
Affiliations and expertise
Chemical and Water Desalination Engineering Program, University of Sharjah, Sharjah, United Arab EmiratesPO
Patrick Okoye
Patrick Okoye is an associate professor at the Instituto de Energias Renovables of the Universidad Nacional Autónoma de Mexico (IER-UNAM). He is interested in research and technology that will result in sustainable solutions to current challenges in energy and environmental pollution. His main research focus is the valorization of waste to produce porous materials that can be applied in heterogeneous catalysis, biofuels, energy storage, hydrogen storage, fine chemical synthesis, and liquid-phase adsorption processes. Dr. Okoye also works in the gamification of thermochemical processes and the application of machine learning, developing curricula and teaching courses on bioenergy, hydrogen and energy, fuel cells, and emerging contaminants at both undergraduate and graduate levels. He has published widely in reputable peer-reviewed journals and has been cited over 2,000 times. Dr. Okoye is a reviewer for several high-impact indexed journals and an editor in the Energy, Ecology, and Environment journal, Springer Nature.
Affiliations and expertise
Instituto de Energias Renovables-Universidad Nacional Autónoma de México, MexicoBO
Bilainu Oboirien
Professor Bilainu Oboirien works in the Department of Chemical Engineering at the University of Johannesburg in South Africa.
Affiliations and expertise
Professor Oboirien, Department of Chemical Engineering,, University of Johannesburg, South Africa