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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 obtained his first-class degree in Chemical Engineering from UNAM in 2009 and his PhD in Process Integration from the University of Manchester in 2013. He was a Research Fellow at the University of Surrey and the University of Oxford, then a lecturer in the Department of Chemical Engineering of the University of Bath. He joined the Mexican Institute of Petroleum in 2017. Dr Elias Martinez is a recognised scientist by the National Researcher System in Mexico, serves as subject Editor of IChemE's journals Food and Bioproducts Processing and Process Engineering for Sustainable Development. He has authored two other reference textbooks on Biorefineries and Chemical processes – Design, Integration and Sustainability Analysis’ and “A New Systems Thinking Approach for Sustainable Resource Management. He published more than 60 peer-reviewed journal publications and book chapters. Dr Elias has led research in digital chemical engineering applied to bioenergy and biorefinery process development, the food-energy-water nexus and property prediction combining graph theory, machine learning and AI with first principles models.
Sindy Dayana Puerto Vargas is a PhD student in Engineering at the Mexican Petroleum Institute, and a visiting research student at the University of Zaragoza. She holds a degree in Chemical Engineering from the Universidad Industrial de Santander and a Master’s degree in Process Integration from the University of Guanajuato. She has experience in energy management, project analysis, and quality assurance within the energy sector. Her research focuses on the application of artificial intelligence and machine learning to develop hybrid models and tools for sustainable hydrogen production from biomass, integrating process engineering tools with technical, economic, and environmental criteria.