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Full 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.
Contents
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
