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.
1. Chapter 1 Machine learning introduction - p. 1 2. Chapter 2 Machine learning for the prediction of higher heating values of biomass, biochar, and bio-oils - p. 17 3. Chapter 3 Catalysts screening and optimization - p. 43 4. Chapter 4 Biochar properties prediction - p. 73 5. Chapter 5 Harnessing artificial intelligence in the development and application of pyrolysis and hydrothermal gasification for sustainable agro-waste valorization - p. 93 6. Chapter 6 Kinetics and reaction mechanism study with machine learning - p. 111 7. Chapter 7 Machine learning applications in combustion (process parameter predictions and image processing) - p. 139 8. Chapter 8 Machine learning applications in nanomaterial preparation for thermochemical processes - p. 159 9. Chapter 9 Machine learning applications in emerging thermochemical technologies: A focus on chemical looping combustion - p. 191 10. Chapter 10 Integrating machine learning into biorefinery operations - p. 213 11. Chapter 11 Bioinformatics approaches for microbial-driven thermochemical conversion - p. 231 12. Chapter 12 Machine learning applications in microfluidic thermochemical reactors - p. 275 13. Chapter 13 Machine learning for advancing techno-economic and lifecycle assessment of thermochemical conversion processes - p. 393 14. Chapter 14 Machine learning application in feedstock selection and durability - p. 415 15. Index - p. 439
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.
1. Chapter 1 Machine learning introduction - p. 1 2. Chapter 2 Machine learning for the prediction of higher heating values of biomass, biochar, and bio-oils - p. 17 3. Chapter 3 Catalysts screening and optimization - p. 43 4. Chapter 4 Biochar properties prediction - p. 73 5. Chapter 5 Harnessing artificial intelligence in the development and application of pyrolysis and hydrothermal gasification for sustainable agro-waste valorization - p. 93 6. Chapter 6 Kinetics and reaction mechanism study with machine learning - p. 111 7. Chapter 7 Machine learning applications in combustion (process parameter predictions and image processing) - p. 139 8. Chapter 8 Machine learning applications in nanomaterial preparation for thermochemical processes - p. 159 9. Chapter 9 Machine learning applications in emerging thermochemical technologies: A focus on chemical looping combustion - p. 191 10. Chapter 10 Integrating machine learning into biorefinery operations - p. 213 11. Chapter 11 Bioinformatics approaches for microbial-driven thermochemical conversion - p. 231 12. Chapter 12 Machine learning applications in microfluidic thermochemical reactors - p. 275 13. Chapter 13 Machine learning for advancing techno-economic and lifecycle assessment of thermochemical conversion processes - p. 393 14. Chapter 14 Machine learning application in feedstock selection and durability - p. 415 15. Index - p. 439
