Description
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
Table of Contents
1. Machine Learning Introduction2. Higher Heating Value Prediction3. Catalysts Screening and Optimization4. Biochar Properties Prediction5. Hydrothermal Gasification and Pyrolysis Process Conditions Optimization6. Kinetics And Reaction Mechanism Study with Machine Learning7. Machine Learning Applications in Combustion (Process Parameter Predictions and Image Processing)8. Machine Learning Applications in Nanomaterial Preparation for Thermochemical Processes9. Machine Learning Applications in Emerging Thermochemical Technologies10. Integrating Machine Learning into Biorefinery Operations11. Bioinformatics Approaches for Microbial-Driven Thermochemical Conversion12. Machine Learning Applications in Microfluidic Thermochemical Reactors13. Machine Learning for Advancing Techno-Economic and Lifecycle Assessment of Thermochemical Conversion Processes14. Energy Efficiency and Heat Integration15. Machine Learning Application in Feedstock Selection and Durability
