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This book explores effective modeling and simulation approaches for solving equations. The second edition covers modeling of multi-stage processes,multi-phase systems with thermodynamic processes including reactive distillation.
Ashok Kumar Verma retired as Professor in the Department of Chemical Engineering and Technology at Indian Institute of Technology (Banaras Hindu University) Varanasi. He holds a B.Sc. degree from Allahabad University, a B.E. degree in Chemical Engineering from University of Roorkee (now Indian Institute of Technology, Roorkee), an M.E degree in chemical engineering from Indian Institute of Sciences, Bangalore and a Ph.D. in Technology from Indian Institute of Technology, Kanpur. Ashok Kumar Verma joined the institute in 1984. He conducted post-doctoral research at Department of Chemical Engineering, University of Illinois, Chicago. His research experience includes modelling of liquid fluidized bed and heat transfer in slurry bubble columns. He has taught courses in modelling and simulation, process engineering, computer aided design, artificial intelligence in chemical engineering, transfer processes, and process control. His current activities include development of virtual lab, based on real time simulation, for chemical engineering courses. Ashok Kumar Verma has authored or coauthored about ten research papers appearing in a range of chemical engineering journals. He also authored or co-authored numerous papers in national and international proceedings and book titled 'Process design for Chemical and Environmental Engineering'. He has made presentations at several conferences and seminars and presented invited lectures at several workshops organised at other universities institutions and industries. Ashok Kumar Verma and his family reside in Varanasi, India.
1. Introduction to Modelling and Simulation 2. An Overview of Modelling and Simulation 3. Models Based on Simple Laws 4. Models Based on Laws of Conservation 5. Multi-Phase Systems without Reaction 6. Multi-Phase Systems with Reaction 7. Multi-Stage/Multi-Phase Systems with Thermodynamic Processes 8. Coupled Processes and Process Intensification 9. Dynamic Models 10. Population-Balance Models and Discrete-Event Models 11. Artificial Neural Network-Based Models and Use of AI and ML 12. Model Validation and Sensitivity Analysis 13. Case Studies 14. Simulation of Large Plants
