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Modelling of Chemical Process Systems gives readers a feel for multiscale modeling. The book starts with the history of modeling and its usefulness, describing modeling steps in detail. Examples have been chosen carefully from both conventional chemical process systems to contemporary systems, including fuel cell and micro reforming processes. Each chapter is accompanied by a case study that explains the step-by-step modeling methodology. The book also introduces the application of machine learning techniques to model chemical process systems.
When combined, the information in the book makes it an indispensable reference for academics and professionals working in modeling and simulation.
- Includes case studies that explain step-by-step modeling methodologies
- Covers detailed multiscale modeling of chemical processes, providing examples from traditional and novel areas
- Provides modeling at microscopic and macroscale levels, including machine learning techniques
Graduate students, researchers in academia and industry, engineering consultants working in consulting companies and chemical engineering sectors (e.g. petroleum refining), pharmaceuticals, renewable energy
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 by using ionic liquids
4. Molecular dynamics simulation in chemical, and energy 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. Crude to Chemicals: Conventional FCC Unit Still Relevant
8. Modelling and simulation of solid oxide fuel cells
9. Fuel Reforming for Fuel Cells
Part IV Machine Learning Techniques for Modelling Process Systems
10. Supervised Learning Algorithms for Process Modelling
11. Large-Scale Process Models using Deep Learning
- No. of pages:
- © Elsevier 2022
- 3rd January 2022
- Paperback ISBN:
Dr. Imtiaz received his PhD (University of Alberta) degrees in Chemical Engineering. After completing his PhD degree, he worked as a Staff Consultant at Aspen Technology implementing advanced control systems for refineries, ethylene plants, methanol plants, and petro-chemical plants. He has implemented over thirty unit-wide model predictive controllers. He joined Memorial University as an Assistant Professor in 2010, promoted to Associate Professor in 2016. Currently he is the Head of the Department in Process Engineering at Memorial University. His research interests involve process monitoring, control systems, modelling, and alarm management. He teaches several graduate and undergraduate level including undergraduate course on Process Modelling and Model Analysis. He has published over 80 journal and conference articles in these fields. He holds several research grants including NSERC Discovery Grant. He is also a recipient of Imperial Oil University Research Award.
Dept Process Engineering, University of Alberta, Canada
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