Machine Learning and Hybrid Modelling for Reaction Engineering (eBook)

Theory and Applications

Ehecatl Antonio del Rio Chanona, Dongda Zhang (Herausgeber)

eBook Download: EPUB

2023
420 Seiten
Royal Society of Chemistry (Verlag)
978-1-83767-018-5 (ISBN)

Infos

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.

Inhalt

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

Produkt-Details

• Erscheint lt. Verlag: 20.12.2023
• Reihe/Serie: ISSN
• Reihe/Serie: ISSN
• Reihe/Serie: ISSN
• Reihe/Serie: Theoretical and Computational Chemistry Series
• Sprache: englisch
• Themenwelt: Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik
• Themenwelt: Naturwissenschaften ► Chemie ► Physikalische Chemie
• Themenwelt: Naturwissenschaften ► Chemie ► Technische Chemie
• Themenwelt: Technik
• Schlagworte: Data-driven Modelling • hybrid model construction • ML • uncertainty analysis
• ISBN-10: 1-83767-018-8 / 1837670188
• ISBN-13: 978-1-83767-018-5 / 9781837670185