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.
Machine Learning and Hybrid Modelling for Reaction Engineering: Theory and Applications, Royal Society of Chemistry, 2023.
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Table of contents
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Part I: Model Construction Theory
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Chapter 1: Physical Model Constructionp1-23ByFernando Vega-Ramon;Fernando Vega-RamonDepartment of Chemical Engineering, The University of Manchester, UKSearch for other works by this author on:Dongda ZhangDongda ZhangDepartment of Chemical Engineering, The University of Manchester, UKSearch for other works by this author on:
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Chapter 2: Data-driven Model Constructionp24-55ByZhaoyan Zhang;Zhaoyan ZhangaDepartment of Chemistry, Imperial College London, UKSearch for other works by this author on:Dongda Zhang;Dongda ZhangbDepartment of Chemical Engineering, The University of Manchester, UKSearch for other works by this author on:Ehecatl Antonio del Rio ChanonaEhecatl Antonio del Rio ChanonacDepartment of Chemical Engineering, Imperial College London, UKSearch for other works by this author on:
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Chapter 3: Hybrid Model Constructionp56-84ByAlexander W. Rogers;Alexander W. RogersDepartment of Chemical Engineering, The University of Manchester, UKSearch for other works by this author on:Dongda ZhangDongda ZhangDepartment of Chemical Engineering, The University of Manchester, UKSearch for other works by this author on:
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Chapter 4: Model Structure Identificationp85-108ByMiguel Ángel de Carvalho Servia;Miguel Ángel de Carvalho ServiaaDepartment of Chemical Engineering, Imperial College London, UKSearch for other works by this author on:Ehecatl Antonio del Rio ChanonaEhecatl Antonio del Rio ChanonaaDepartment of Chemical Engineering, Imperial College London, UKbCentre for Process Systems Engineering, Department of Chemical Engineering, Imperial College London, UKSearch for other works by this author on:
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Chapter 5: Model Uncertainty Analysisp109-132ByHaiting Wang;Haiting WangaCentre for Process Systems Engineering (CPSE), Department of Chemical Engineering, Imperial College London, UKSearch for other works by this author on:Eduardo Iraola;Eduardo IraolabUniversitat Politécnica de Catalunya, Jordi Girona St 31, 08034 Barcelona, SpainSearch for other works by this author on:Cleo Kontoravdi;Cleo KontoravdiaCentre for Process Systems Engineering (CPSE), Department of Chemical Engineering, Imperial College London, UKSearch for other works by this author on:Ehecatl Antonio del Rio ChanonaEhecatl Antonio del Rio ChanonaaCentre for Process Systems Engineering (CPSE), Department of Chemical Engineering, Imperial College London, UKSearch for other works by this author on:
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Part II: Applications in Reaction Engineering
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Chapter 6: Interpretable Machine Learning for Kinetic Rate Model Discoveryp133-158ByMiguel Ángel de Carvalho Servia;Miguel Ángel de Carvalho ServiaaDepartment of Chemical Engineering, Imperial College London, UKSearch for other works by this author on:Ehecatl Antonio del Rio ChanonaEhecatl Antonio del Rio ChanonaaDepartment of Chemical Engineering, Imperial College London, UKbCentre for Process Systems Engineering, Department of Chemical Engineering, Imperial College London, UKSearch for other works by this author on:
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Chapter 7: Graph Neural Networks for the Prediction of Molecular Structure–Property Relationshipsp159-181ByJan G. Rittig;Jan G. RittigaRWTH Aachen University, Process Systems Engineering (AVT.SVT), Forckenbeckstr. 51, 52074 Aachen, GermanySearch for other works by this author on:Qinghe Gao;Qinghe GaobDelft University of Technology, Department of Chemical Engineering, Van der Maasweg 9, Delft 2629 HZ, The NetherlandsSearch for other works by this author on:Manuel Dahmen;Manuel DahmencForschungszentrum Jülich GmbH, Institute of Energy and Climate Research, Energy Systems Engineering (IEK-10), Wilhelm-Johnen-Str., 52428 Jülich, GermanySearch for other works by this author on:Alexander Mitsos;Alexander MitsosaRWTH Aachen University, Process Systems Engineering (AVT.SVT), Forckenbeckstr. 51, 52074 Aachen, GermanycForschungszentrum Jülich GmbH, Institute of Energy and Climate Research, Energy Systems Engineering (IEK-10), Wilhelm-Johnen-Str., 52428 Jülich, GermanySearch for other works by this author on:Artur M. SchweidtmannArtur M. SchweidtmannbDelft University of Technology, Department of Chemical Engineering, Van der Maasweg 9, Delft 2629 HZ, The NetherlandsSearch for other works by this author on:
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Chapter 8: Reaction Network Simulation and Model Reductionp182-207ByFernando Vega-Ramon;Fernando Vega-RamonaDepartment of Chemical Engineering, The University of Manchester, UKSearch for other works by this author on:Wei Wang;Wei WangbSchool of Chemistry and Material Sciences, Heilongjiang University, ChinaSearch for other works by this author on:Wei Wu;Wei WubSchool of Chemistry and Material Sciences, Heilongjiang University, ChinaSearch for other works by this author on:Dongda ZhangDongda ZhangaDepartment of Chemical Engineering, The University of Manchester, UKSearch for other works by this author on:
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Chapter 9: Hybrid Modelling Under Uncertainty: Effects of Model Greyness, Data Quality and Data Quantityp208-228ByAlexander W. Rogers;Alexander W. RogersaDepartment of Chemical Engineering, The University of Manchester, UKSearch for other works by this author on:Ziqi Song;Ziqi SongaDepartment of Chemical Engineering, The University of Manchester, UKSearch for other works by this author on:Fernando Vega Ramon;Fernando Vega RamonaDepartment of Chemical Engineering, The University of Manchester, UKSearch for other works by this author on:Keju Jing;Keju JingbDepartment of Chemical and Biochemical Engineering, Xiamen University, ChinaSearch for other works by this author on:Dongda ZhangDongda ZhangaDepartment of Chemical Engineering, The University of Manchester, UKSearch for other works by this author on:
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Chapter 10: A Data-efficient Transfer Learning Approach for New Reaction System Predictive Modellingp229-246BySam Kay;Sam KayDepartment of Chemical Engineering, The University of Manchester, UKSearch for other works by this author on:Dongda ZhangDongda ZhangDepartment of Chemical Engineering, The University of Manchester, UKSearch for other works by this author on:
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Chapter 11: Constructing Time-varying and History-dependent Kinetic Models Via Reinforcement Learningp247-273ByMax Mowbray;Max MowbrayaDepartment of Chemical Engineering, The University of Manchester, UKSearch for other works by this author on:Ehecatl Antonio Del Rio Chanona;Ehecatl Antonio Del Rio ChanonabSargent Centre for Process Systems Engineering, Department of Chemical Engineering, Imperial College London, UKSearch for other works by this author on:Dongda ZhangDongda ZhangaDepartment of Chemical Engineering, The University of Manchester, UKSearch for other works by this author on:
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Part III: Data Intelligence and Industrial Applications
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Chapter 12: Surrogate and Multiscale Modelling for (Bio)reactor Scale-up and Visualisationp275-302ByBovinille Anye ChoBovinille Anye ChoDepartment of Chemical Engineering, The University of Manchester, UKSearch for other works by this author on:
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Chapter 13: Statistical Design of Experiments for Reaction Modelling and Optimisationp303-318ByPhil Kay;Phil KayaJMP Statistical Discovery, SAS Software Ltd., Wittington House, Henley Road, Medmenham, Marlow SL7 2EB, UKSearch for other works by this author on:Benjamin Ingham;Benjamin InghambEngineering Building A, The University of Manchester, Oxford Road, Manchester, M13 9PL, UKSearch for other works by this author on:James WinterburnJames WinterburnbEngineering Building A, The University of Manchester, Oxford Road, Manchester, M13 9PL, UKSearch for other works by this author on:
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Chapter 14: Autonomous Synthesis and Self-optimizing Reactorsp319-357ByM. J. Nieves-RemachaM. J. Nieves-RemachaLilly Research Laboratories, Lilly S.A., Avenida de la Industria 30, Alcobendas, Madrid, 28108, SpainSearch for other works by this author on:
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Chapter 15: Industrial Data Science for Batch Reactor Monitoring and Fault Detectionp358-403ByI. Imanol Arzac;I. Imanol ArzacaDepartment of Chemical Engineering, KU Leuven, BelgiumSearch for other works by this author on:Carlos Perez-Galvan;Carlos Perez-GalvanbSOLVAY SA, BelgiumSearch for other works by this author on:Francisco J. Navarro-BrullFrancisco J. Navarro-BrullbSOLVAY SA, BelgiumcDepartment of Chemical Engineering, Imperial College London, UKSearch for other works by this author on:
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