Some error occured while loading the Quick View. Please close the Quick View and try reloading the page.
Artificial Intelligence in Process Systems Engineering is a didactic reference book on the application of machine learning and AI algorithms for process modelling, simulation and analysis. It introduces ML and AI concepts directly through domain-specific chemical process problems and datasets. In addition to basic algorithms, the book introduces specialized approaches such as a clustering-classification method for biomass characterization, molecular graph modularity descriptors for property prediction, synthetic data generation using generative methods, surrogate ANN-based optimization, and hybrid process models. The book is a learn-by-doing resource enabling students and practitioners to apply AI and ML to property prediction and process simulations, enhance process designs, overcome experimental data scarcity, and confidently integrate the governing physics and chemistry into data science frameworks.
Dr Elias Martinez Hernandez is a researcher at the Mexican Institute of Petroleum (IMP) in the Biomass Conversion Division. He obtained his first-class degree in Chemical Engineering from UNAM (Mexico) in 2009 and his PhD in Process Integration from the University of Manchester (UK) in 2013. He then worked as a Research Fellow at the University of Surrey and the University of Oxford. Dr Elias then was a lecturer in the Department of Chemical Engineering of the University of Bath before joining IMP in 2017. Dr Elias Martinez is a recognised scientist by the National Researcher System from Mexico's National Science and Technology Council. He is an Editorial Board Member of IChemE's journal Food and Bioproducts Processing. He has authored Wiley's Advanced Textbook: ‘Biorefineries and Chemical processes – Design, Integration and Sustainability Analysis’ and more than 40 peer-reviewed journal publications. Dr Elias has led research in Digital Chemical Engineering applied to bioenergy and biorefinery process development, the food-energy-water nexus and more recently in property prediction combining neural networks and community detection in molecular graphs. He has developed the following Digital Chemical Engineering tools: Biorefsys® – biorefinery systems analysis tool. Nexsym® a first-of-its-kind food-energy-water nexus simulator, has been featured and reviewed in several articles in this field. IMP Bio2Energy® – a tool to evaluate the techno-economics of biomass and bioenergy production considering the whole value chain and employing thermodynamic modelling of steam and water properties. Current developments include a decision support platform for bioenergy implementation and policy making in Mexico as part of a Newton Fund Impact Scheme project NFIS 540821111, and neural network models for process simulation.