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.
Reihe
Sprache
Verlagsort
Zielgruppe
Produkt-Hinweis
Maße
Höhe: 234 mm
Breite: 156 mm
Dicke: 33 mm
Gewicht
ISBN-13
978-1-83916-563-4 (9781839165634)
DOI
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Schweitzer Klassifikation
Dr. Dongda Zhang is a Lecturer at Department of Chemical Engineering, the University of Manchester. His research focuses on the application of hybrid modelling and data intelligence in complex reaction systems. These include chemical and biochemical process modelling, optimisation, control, and data analytics. He completed his PhD research at the University of Cambridge within two years and graduated after the university special approval on Thesis Early Submission (2016). He is an Honorary Research Fellow at Imperial College London, a member of the UK Biotechnology and Biological Sciences Research Council Pool of Experts, a member of Editorial Board for 'Biochemical Engineering Journal', an Associate Editor of 'Digital Chemical Engineering', and a member of the Industrial Management Board for the Centre for Process Analytics and Control Technology.
Dr Ehecatl Antonio Del Rio Chanona is a Lecturer at the Department of Chemical Engineering and the Sargent Centre for Process Systems Engineering, Imperial College London. His research interests include the application of optimisation and machine learning techniques to chemical engineering systems. He has been in receipt of numerous awards including the fellowship from the UK Engineering and Physical Sciences Research Council (2017), the Danckwerts-Pergamon Prize at the University of Cambridge (2017), the Sir William Wakeham award at Imperial College London (2019), and the Nicklin Medal by the Institution of Chemical Engineers in recognition for exceptional research that will have significant impact in areas of process systems engineering and adoption of intelligent and autonomous learning algorithms to chemical engineering (2020).
Herausgeber*in
University of Manchester, UK
Imperial College London, UK
Physical Model Construction;Data-driven Model Construction;Hybrid Model Construction;Model Structure Identification;Model Uncertainty Analysis;Interpretable Machine Learning for Kinetic Rate Model Discovery;Graph Neural Networks for the Prediction of Molecular Structure-Property Relationships;Reaction Network Simulation and Model Reduction;Hybrid Modelling Under Uncertainty: Effects of Model Greyness, Data Quality and Data Quantity;A Data-efficient Transfer Learning Approach for New Reaction System Predictive Modelling;Constructing Time-varying and History-dependent Kinetic Models via Reinforcement Learning;Surrogate and Multiscale Modelling for (Bio)reactor Scale-up and Visualisation;Statistical Design of Experiments for Reaction Modelling and Optimisation;Autonomous Synthesis and Self-optimizing Reactors;Industrial Data Science for Batch Reactor Monitoring and Fault Detection
