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Process Dynamics and Control. Modeling for Control and Prediction. Edition No. 1

  • ID: 5227383
  • Book
  • November 2006
  • 560 Pages
  • John Wiley and Sons Ltd
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Offering a different approach to other textbooks in the area, this book is a comprehensive introduction to the subject divided in three broad parts. The first part deals with building physical models, the second part with developing empirical models and the final part discusses developing process control solutions. Theory is discussed where needed to ensure students have a full understanding of key techniques that are used to solve a modeling problem.

Hallmark Features:
  • Includes worked out examples of processes where the theory learned early on in the text can be applied. - Uses MATLAB simulation examples of all processes and modeling techniques- further information on MATLAB can be obtained from - Includes supplementary website to include further references, worked examples and figures from the book
This book is structured and aimed at upper level undergraduate students within chemical engineering and other engineering disciplines looking for a comprehensive introduction to the subject. It is also of use to practitioners of process control where the integrated approach of physical and empirical modeling is particularly valuable.
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1 Introduction to Process Modelling.

2 Process Modelling Fundamentals.

3 Extended Analysis of Modelling for Process Operation.

4 Design for Process Modelling and Behavioural Models.

5 Transformation Techniques.

6 Linearization of Model Equations.

7 Operating points.

8 Process Simulation.

9 Frequency Response Analysis.


10 General Process Behaviour

11 Analysis of a Mixing Process.

12 Dynamics of Chemical Stirred Tank Reactors.

13 Dynamic Analysis of Tubular Reactors.

14 Dynamic Analysis of Heat Exchangers.

15 Dynamics of Evaporators and Separators.

16 Dynamic Modelling of Distillation Columns.

17 Dynamic Analysis of Fermentation Reactors

18 Physiological Modeling: Glucose-Insulin Dynamics and Cardiovascular Modelling.

19 Introduction to Black Box Modelling.

20 Basics of Linear Algebra.

21 Data Conditioning.

22 Principal Component Analysis

23 Partial Least Squares

24 Time-series Identification.

25 Discrete Linear and Non-linear State Space Modelling.

26 Model Reduction.

27 Neural Networks.

28 Fuzzy Modelling.

29 Neuro Fuzzy Modelling.

30 Hybrid Models.

31 Introduction to Process Control and Instrumentation.

32 Behaviour of Controlled Processes.

33 Design of Control Schemes.

34 Control of Distillation Columns.

35 Control of a Fluid Catalytic Cracker.

Appendix A. Modelling an Extraction Process.

A1: Problem Analysis.

A2: Dynamic Process Model Development.

A3 Dynamic Process Model Analysis.

A4 Dynamic Process Simulation.

A5: Process Control Simulation.


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Ben Betlem University of Twente, The Netherlands.

Brian Roffel University of Groningen, The Netherlands.
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