Machine Learning and Hybrid Modelling for Reaction Engineering
Theory and Applications
RSC · Print & ebook · December 20, 2023
Over the last decade, there has been a significant shift from traditional mechanistic and empirical modelling into statistical and data-driven modelling for applications in reaction engineering.
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- Who it’s for
- Theory and applications of hybrid modelling, from model construction to uncertainty analysis in reaction engineering.
- Genres
- Chemical & Biochemical Engineering, Molecular Modeling, Industrial Chemistry
- Published
- RSC, December 20, 2023
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Start with this bookAbout this book
Over the last decade, there has been a significant shift from traditional mechanistic and empirical modelling into statistical and data-driven modelling for applications in reaction engineering. In particular, the integration of machine learning and first-principle models has demonstrated significant potential and success in the discovery of (bio)chemical kinetics, prediction and optimisation of complex reactions, and scale-up of industrial reactors.
Continue reading
Summarising the latest research and illustrating the current frontiers in applications of hybrid modelling for chemical and biochemical reaction engineering, Machine Learning and Hybrid Modelling for Reaction Engineering fills a gap in the methodology development of hybrid models. With a systematic explanation of the fundamental theory of hybrid model construction, time-varying parameter estimation, model structure identification and uncertainty analysis, this book is a great resource for both chemical engineers looking to use the latest computational techniques in their research and computational chemists interested in new applications for their work.
Hybrid Reaction Models
Theory and applications of hybrid modelling, from model construction to uncertainty analysis in reaction engineering.
Come here for
- Hybrid models combining machine learning with first-principles models
- Applications to kinetics, complex reactions, and reactor scale-up
Expect
- Time-varying parameter estimation and model structure identification
- Research frontiers in chemical and biochemical reaction engineering
Where it sits
| Chemical & Biochemical Engineering | 78% |
|---|---|
| Molecular Modeling | 70% |
| Industrial Chemistry | 53% |
Percentages are BookFrontier’s reading of the book.
Publisher categories
- Molecular Modeling
- Chemical & Biochemical Engineering
- Authors
- Dongda Zhang, Ehecatl Antonio del Río Chanona
- Publisher
- RSC
- Published
- December 20, 2023
- Format
- Print & ebook
- Reading lane
- Molecular Modeling