Machine Learning and Hybrid Modelling for Reaction Engineering - Theory and Applications

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.

General

Imprint:	Royal Society of Chemistry
Country of origin:	United Kingdom
Series:	Theoretical and Computational Chemistry Series, Volume 26
Release date:	December 2023
First published:	2024
Editors:	Dongda Zhang • Ehecatl Antonio del Río Chanona
Dimensions:	234 x 156mm (L x W)
Pages:	394
ISBN-13:	978-1-83916-563-4
Categories:	Books Promotions
LSN:	1-83916-563-4
Barcode:	9781839165634

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