Zhang / del Río Chanona Machine Learning and Hybrid Modelling for Reaction Engineering

Theory and Applications
1. Auflage 2023
ISBN: 978-1-83767-017-8
Verlag: Royal Society of Chemistry
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 420 Seiten, Web PDF

Reihe: ISSN

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.

Autoren/Hrsg.

Zhang, Dongda

del Río Chanona, Ehecatl Antonio

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen
• Naturwissenschaften Chemie Physikalische Chemie
• Mathematik | Informatik EDV | Informatik Professionelle Anwendung Computersimulation & Modelle, 3-D Graphik
• Technische Wissenschaften Verfahrenstechnik | Chemieingenieurwesen | Biotechnologie Chemische Verfahrenstechnik

Inhaltsverzeichnis

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

Über Autor(innen)

Zhang, Dongda
Dr. Dongda Zhang is a University Lecturer at Department of Chemical Engineering & Analytical Science, the University of Manchester, and an Honorary Research Fellow at the Centre for Process Systems Engineering, Imperial College London. He is also a member of the BBSRC Pool of Experts in Industrial Biotechnology. He holds BSc degree at Tianjin University (2011), China and MSc degree at Imperial College London (2013). He completed his PhD research at the University of Cambridge within two years, and graduated at the beginning of third year after the university special approval on Thesis Early Submission (2016). He currently leads research in Machine Learning and Reaction Engineering at the Centre for Process Integration. He is interested in exploring how advanced hybrid modelling and machine learning technologies can be developed and applied to accelerate reaction system design and process digitalisation. He has been collaborating with industrial partners and research groups on the multiscale modelling, online optimisation and control, physical knowledge discovery, real-time visualisation, and scale-up of complex chemical/biochemical reaction systems. Since 2015, he has published over 30 research articles (>600 citations) in this field.

del Río Chanona, Ehecatl Antonio
Dr E. Antonio Del Rio Chanona is a Lecturer at the Department of Chemical Engineering and the 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 awarded numerous awards including the fellowship from the Engineering and Physical Sciences Research Council of the United Kingdom (2017) on “sustainable excretable biofuels process design and optimisation”, the Danckwerts-Pergamon Prize for the best PhD thesis of his year at the Department of Chemical Engineering and Biotechnology, University of Cambridge (2017), designated a Top Young Author by the International Federation of Automatic Control (2018), awarded the Sir William Wakeham award by the Department of Chemical Engineering at Imperial College London for outstanding research achievements and contribution to the department (2019), received the Nicklin Medal by the Institution of Chemical Engineers (IChemE) in recognition for exceptional research that will have significant impact in areas of process systems engineering, industrialisation of bioprocesses, and adoption of intelligent and autonomous learning algorithms to chemical engineering (2020).

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).