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.

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.