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Machine Learning and Hybrid Modelling for Reaction Engineering Theory and Applications

Zhang, Dongda del Río Chanona, Ehecatl Antonio
Innbundet / 2023 / Engelsk

Produktdetaljer

ISBN
9781839165634
Publisert
2023-12-20
Utgiver
Vendor
Royal Society of Chemistry
Vekt
813 gr
Høyde
234 mm
Bredde
156 mm
Aldersnivå
P, 06
Språk
Product language
Engelsk
Format
Product format
Innbundet
Antall sider
440

Redaktør
Zhang, Dongda
del Río Chanona, Ehecatl Antonio

Biographical note

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).

Machine Learning and Hybrid Modelling for Reaction Engineering Theory and Applications

Zhang, Dongda del Río Chanona, Ehecatl Antonio
Innbundet / 2023 / Engelsk
Zhang, Dongda del Río Chanona, Ehecatl Antonio
Innbundet / 2023 / Engelsk
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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.
Les mer
Machine Learning and Hybrid Modelling for Reaction Engineering summarises latest research and fills a gap in methodology development of hybrid models for reaction engineering applications.
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
Les mer
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.
Les mer

Relaterte produkter

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.
Les mer
Machine Learning and Hybrid Modelling for Reaction Engineering summarises latest research and fills a gap in methodology development of hybrid models for reaction engineering applications.
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
Les mer
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.
Les mer

Produktdetaljer

ISBN
9781839165634
Publisert
2023-12-20
Utgiver
Vendor
Royal Society of Chemistry
Vekt
813 gr
Høyde
234 mm
Bredde
156 mm
Aldersnivå
P, 06
Språk
Product language
Engelsk
Format
Product format
Innbundet
Antall sider
440

Redaktør
Zhang, Dongda
del Río Chanona, Ehecatl Antonio

Biographical note

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).

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