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Modelling of Chemical Process Systems
Step-by-step methodology for building chemical process systems models
Syed Ahmad Imtiaz (Edited by)
9780128238691, Elsevier Science
Paperback, published 1 July 2023
312 pages
23.5 x 19 x 2 cm, 0.45 kg
Models and simulations are widely being used for design, optimization, fault detection and diagnosis, and various other decision-making purposes. Increasingly, models are developed at different scales and levels, all the way from molecular level to the large-scale process systems scale. Modelling of Chemical Process Systems gives readers a feel for the multiscale modelling. As models have been developed for various applications, a general systematic method for building model has emerged. This book starts with the history of modelling and its usefulness, describing modelling steps in detail. Examples have been chosen carefully from both conventional chemical process systems to contemporary systems, including fuel cell and bioprocesses. Modelling theories are complemented with case studies that explain step-by-step modelling methodologies. This book also introduces the application of machine learning techniques to model chemical process systems. This makes the book an indispensable reference for academics and professionals working in modelling and simulation.
Part I Theory and Background 1. Introduction to process modelling 2. Model equations and modelling methodology Part II Micro Scale Modelling 3. Density functional theory (DFT) models for extraction of sulfur compounds from fuel oils by using ionic liquids 4. Molecular dynamics simulation in energy and chemical systems 5. Single event modelling of reaction kinetics 6. Modelling and simulation of batch and continuous crystallization processes Part III Macro Scale Modelling of Process Systems 7. Fuel processing systems 8. Crude to chemicals: Conventional FCC unit still relevant Part IV Machine Learning Techniques for Modelling Process Systems 9. Hybrid model for a diesel cloud point soft-sensor 10. Large-scale process models using deep learning
Subject Areas: Enterprise software [UFL], Chemical engineering [TDCB]
