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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Produktdetaljer
ISBN
9781837670185
Publisert
2023
Utgave
1. utgave
Utgiver
Vendor
Royal Society of Chemistry
Språk
Product language
Engelsk
Format
Product format
Digital bok
Forfatter