Machine learning and hybrid modelling for reaction engineering : theory and applications / edited by Dongda Zhang and Ehecatl Antonio del Río Chanona.
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 significan...
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| Online Access: |
Full Text (via EBSCO) |
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| Other Authors: | , |
| Format: | eBook |
| Language: | English |
| Published: |
Cambridge :
Royal Society of Chemistry,
[2024]
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| Series: | Theoretical and computational chemistry series ;
no. 26 |
| Subjects: |
| Summary: | 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. 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. |
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| Physical Description: | 1 online resource (420 pages). |
| Bibliography: | Includes bibliographical references and index. |
| ISBN: | 9781837670178 183767017X 9781837670185 1837670188 |
| Source of Description, Etc. Note: | Description based on online resource; title from digital title page (viewed on January 31, 2024). |