Modelling of Chemical Process Systems
- 1st Edition - July 25, 2023
- Imprint: Elsevier
- Editor: Syed Ahmad Imtiaz
- Language: English
- Paperback ISBN:9 7 8 - 0 - 1 2 - 8 2 3 8 6 9 - 1
- eBook ISBN:9 7 8 - 0 - 1 2 - 8 2 4 1 9 9 - 8
Models and simulations are widely being used for design, optimization, fault detection and diagnosis, and various other decision-making purposes. Increasingly, models are de… Read more
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Request a sales quoteModels 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.
- Includes case studies that explain step-by-step modelling methodologies
- Covers detailed multiscale modelling of chemical processes, providing examples from traditional and novel areas
- Provides modelling insight at micro and macro-scale 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, etc.
- Cover image
- Title page
- Table of Contents
- Copyright
- Contributors
- Preface
- Part I: Theory and background
- 1: An introduction to modeling of chemical process systems
- Abstract
- 1: What is a model and modeling?
- 2: Historical perspective of simulation, systems engineering, and process systems modeling
- 3: Classification of models
- 4: Multiscale modeling
- 5: Modeling applications in processes
- 6: Scope of the book
- References
- 2: Model equations and modeling methodology
- Abstract
- 1: Process model and model equations
- 2: Model equations
- 3: Systematic method for building process models
- 4: Summary
- References
- Part II: Micro scale modeling
- 3: Density functional theory (DFT) models for the desulfurization and extraction of sulfur compounds from fuel oils using ionic liquids
- Abstract
- Acknowledgments
- 1: Introduction
- 2: Results and discussion
- 3: Conclusion and perspective for future developments
- 4: Summary
- References
- 4: Molecular dynamics simulation in energy and chemical systems
- Abstract
- 1: Introduction
- 2: Fundamentals of MD technique
- 3: Emerge of MD technique
- 4: Architecture of MD technique
- 5: Theoretical frameworks of MD technique
- 6: Algorithms and simulation packages for MD technique
- 7: Advantages and disadvantages of the MD technique
- 8: Applications/case studies of MD
- 9: Theoretical and practical challenges in MD implementation
- 10: Current status and future prospects of MD technique
- References
- 5: Single-event kinetic modeling of catalytic dewaxing on commercial Pt/ZSM-5
- Abstract
- 1: Introduction
- 2: Reactor modeling
- 3: Results and discussion
- 4: Conclusions
- References
- 6: Modeling and simulation of batch and continuous crystallization processes
- Abstract
- 1: Introduction to solution crystallization
- 2: Supersaturation and metastable limit
- 3: Kinetics of crystallization in supersaturation
- 4: Crystal size distribution and population balance equations
- 5: Modeling of batch and continuous crystallization processes
- Summary
- References
- Part III: Macro scale modeling of process systems
- 7: Fuel processing systems
- Abstract
- 1: The need for fuel processing units
- 2: Fundamentals of fuel processing
- 3: Recent developments in the reforming of common fuels
- 4: Electrochemical H2 production
- 5: Kinetic models for reforming
- 6: Reactor choice
- 7: Reactor modeling
- 8: Sizing of reactor for applications in fuel cells
- 9: Summary
- References
- 8: Crude to chemicals: The conventional FCC unit still relevant
- Abstract
- 1: History of direct crude processing
- 2: Update on crude to chemical processing
- 3: FCC unit: Conventional FCC units with high severity to maximize propylene and ethylene
- 4: Riser and regenerator mathematical model
- 5: FCC catalysts and role in crude to chemical technology
- 6: The future of crude to chemicals
- References
- Part IV: Machine learning techniques for modeling process systems
- 9: Hybrid model for a diesel cloud point soft-sensor
- Abstract
- 1: Introduction
- 2: Case study: A hybrid model for diesel cloud point prediction
- 3: Results
- 4: Summary
- Appendix
- References
- 10: Large-scale process models using deep learning
- Abstract
- 1: Large-scale system modeling challenges
- 2: Motivation for deep learning algorithms
- 3: Deep learning methods
- 4: Exploring key deep learning methods in large-scale process modeling
- 5: Application to modeling chemical and biological systems
- 6: Summary
- References
- Index
- Edition: 1
- Published: July 25, 2023
- Imprint: Elsevier
- No. of pages: 312
- Language: English
- Paperback ISBN: 9780128238691
- eBook ISBN: 9780128241998
SI
Syed Ahmad Imtiaz
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.
Affiliations and expertise
Department of Process Engineering, Memorial University, Newfoundland, CanadaRead Modelling of Chemical Process Systems on ScienceDirect