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Process Dynamics and Control, Modeling for Control and Prediction: Brian Roffel, Ben Betlem

Publicado: 2009-10-03 23:48:41

Process Dynamics and Control, Modeling for Control and Prediction
Brian Roffel, Ben Betlem

                                                                   

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1 Introduction to Process Modeling
1.1 Application of Process Models
1.2 Dynamic Systems Modeling
1.3 Modeling Steps
1.4 Use of Diagrams
1.5 Types of Models
1.6 Continuous versus Discrete Models
References
2 Process Modeling Fundamentals
2.1 System States
2.2 Mass Relationship for Liquid and Gas
2.3 Energy Relationship
2.4 Composition Relationship
3 Extended Analysis of Modeling for Process Operation
3.1 Environmental Model
3.2 Procedure for the Development of an Environmental Model for Process Operation
3.3 Example: Mixer
3.4 Example: Evaporator with Variable Heat Exchanging Surface
4 Design for Process Modeling and Behavioral Models
4.1 Behavioral Model
4.2 Example: Mixer
5 Transformation Techniques
5.1 Introduction
5.2 Laplace Transform
5.3 Useful Properties of Laplace Transform: limit functions
5.4 Transfer Functions
5.5 Discrete Approximations
5.6 z-Transforms
References
6 Linearization of Model Equations
6.1 Introduction
6.2 Non-linear Process Models
6.3 Some General Linearization Rules
6.4 Linearization of Model of the Level Process
6.5 Linearization of the Evaporator model
6.6 Normalization of the Transfer Function
6.7 Linearization of the Chemical Reactor Model
7 Operating Points
7.1 Introduction
7.2 Stationary System and Operating Point
7.3 Flow Systems
7.4 Chemical System
7.5 Stability in the Operating Point
7.6 Operating Point Transition
8 Process Simulation
8.1 Using Matlab Simulink
8.2 Simulation of the Level Process
8.3 Simulation of the Chemical Reactor
References
9 Frequency Response Analysis
9.1 Introduction
9.2 Bode Diagrams
9.3 Bode Diagram of Simulink Models
References
10 General Process Behavior
10.1 Introduction
10.2 Accumulation Processes
10.3 Lumped Process with Non-interacting Balances
10.4 Lumped Process with Interacting Balances
10.5 Processes with Parallel Balances
10.6 Distributed Processes
10.7 Processes with Propagation Without Feedback
10.8 Processes with Propagation With Feedback
11 Analysis of a Mixing Process
11.1 The Process
11.2 Mixer with Self-adjusting Height
12 Dynamics of Chemical Stirred Tank Reactors
12.1 Introduction
12.2 Isothermal First-order Reaction
12.3 Equilibrium Reactions
12.4 Consecutive Reactions
12.5 Non-isothermal Reactions
13 Dynamic Analysis of Tubular Reactors
13.1 Introduction
13.2 First-order Reaction
13.3 Equilibrium Reaction
13.4 Consecutive Reactions
13.5 Tubular Reactor with Dispersion
13.6 Dynamics of Adiabatic Tubular Flow Reactors
References
14 Dynamic Analysis of Heat Exchangers
14.1 Introduction
14.2 Heat Transfer from a Heating Coil
14.3 Shell and Tube Heat Exchanger with Condensing Steam
14.4 Dynamics of a Counter-current Heat Exchanger
References
15 Dynamics of Evaporators and Separators
15.1 Introduction
15.2 Model Description
15.3 Linearization and Laplace Transformation
15.4 Derivation of the Normalized Transfer Function
15.5 Response Analysis
15.6 General Behavior
15.7 Example of Some Responses
15.8 Separation of Multi-phase Systems
15.9 Separator Model
15.10 Model Analysis
15.11 Derivation of the Transfer Function
16 Dynamic Modeling of Distillation Columns
16.1 Column Environmental Model
16.2 Assumptions and Simplifications
16.3 Column Behavioral Model
16.4 Component Balances and Equilibria
16.5 Energy Balances
16.6 Tray Hydraulics
16.7 Tray Pressure Drop
16.8 Column Dynamics
Notation
Greek Symbols
References
17 Dynamic Analysis of Fermentation Reactors
17.1 Introduction
17.2 Kinetic Equations
17.3 Reactor Models
17.4 Dynamics of the Fed-batch Reactor
17.5 Dynamics of Ideally Mixed Fermentation Reactor
17.6 Linearization of the Model for the Continuous Reactor
References
18 Physiological Modeling: Glucose-Insulin Dynamics and Cardiovascular Modeling
18.1 Introduction to Physiological Models
18.2 Modeling of Glucose and Insulin Levels
18.3 Steady-state Analysis
18.4 Dynamic Analysis
18.5 The Bergman Minimal Model
18.6 Introduction to Cardiovascular Modeling
18.7 Simple Model Using Aorta Compliance and Peripheral Resistance
18.8 Modeling Heart Rate Variability using a Baroreflex Model
References
19 Introduction to Black Box Modeling
19.1 Need for Different Model Types
19.2 Modeling steps
19.3 Data Preconditioning
19.4 Selection of Independent Model Variables
19.5 Model Order Selection
19.6 Model Linearity
19.7 Model Extrapolation
19.8 Model Evaluation
20 Basics of Linear Algebra
20.1 Introduction
20.2 Inner and Outer Product
20.3 Special Matrices and Vectors
20.4 Gauss–Jordan Elimination, Rank and Singularity
20.5 Determinant of a matrix
20.6 The Inverse of a Matrix
20.7 Inverse of a Singular Matrix
20.8 Generalized Least Squares
20.9 Eigen Values and Eigen Vectors
References
21 Data Conditioning
21.1 Examining the Data
21.2 Detecting and Removing Bad Data
21.3 Filling in Missing Data
21.4 Scaling of Variables
21.5 Identification of Time Lags
21.6 Smoothing and Filtering a Signal
21.7 Initial Model Structure
References
22 Principal Component Analysis
22.1 Introduction
22.2 PCA Decomposition
22.3 Explained Variance
22.4 PCA Graphical User Interface
22.5 Case Study: Demographic data
22.6 Case Study: Reactor Data
22.7 Modeling Statistics
References
23 Partial Least Squares
23.1 Problem Definition
23.2 The PLS Algorithm
23.3 Dealing with Non-linearities
23.4 Dynamic Extensions of PLS
23.5 Modeling Examples
References
24 Time-series Identification
24.1 Mechanistic Non-linear Models
24.2 Empirical (linear) Dynamic Models
24.3 The Least Squares Method
24.4 Cross-correlation and Autocorrelation
24.5 The Prediction Error Method
24.6 Identification Examples
24.7 Design of Plant Experiments
References
25 Discrete Linear and Non-linear State Space Modeling
25.1 Introduction
25.2 State Space Model Identification
25.3 Examples of State Space Model Identification
References
26 Model Reduction
26.1 Model Reduction in the Frequency Domain
26.2 Transfer Functions in the Frequency Domain
26.3 Example of Basic Frequency-weighted Model Reduction
26.4 Balancing of Gramians
26.5 Examples of Model State Reduction Techniques
References
27 Neural Networks
27.1 The Structure of an Artificial Neural Network
27.2 The Training of Artificial Neural Networks
27.3 The Standard Back Propagation Algorithm
27.4 Recurrent Neural Networks
27.5 Neural Network Applications and Issues
27.6 Examples of Models
References
28 Fuzzy Modeling
28.1 Mamdani Fuzzy Models
28.2 Takagi-Sugeno Fuzzy Models
28.3 Modeling Methodology
28.4 Example of Fuzzy Modeling
28.5 Data Clustering
28.6 Non-linear Process Modeling
References
29 Neuro Fuzzy Modeling
29.1 Introduction
29.2 Network Architecture
29.3 Calculation of Model Parameters
29.4 Identification Examples
References
30 Hybrid Models
30.1 Introduction
30.2 Methodology
30.3 Approaches for Different Process Types
30.4 Bioreactor Case Study
Literature
31 Introduction to Process Control and Instrumentation
31.1 Introduction
31.2 Process Control Goals
31.3 The Measuring Device
31.4 The Control Device
31.5 The Controller
31.6 Simulating the Controlled Process
References
32 Behaviour of Controlled Processes
32.1 Purpose of Control
32.2 Controller Equations
32.3 Frequency Response Analysis of the Process
32.4 Frequency Response of Controllers
32.5 Controller Tuning Guidelines
References
33 Design of Control Schemes
33.1 Procedure
33.2 Example: Desulphurization Process
33.3 Optimal Control
33.4 Extension of the Control Scheme
33.5 Final Considerations
34 Control of Distillation Columns
34.1 Control Scheme for a Distillation Column
34.2 Material and Energy Balance Control
Summary
References
Appendix 34.I Impact of Vapor Flow Variations on Liquid Holdup
Appendix 34.II Ratio Control for Liquid and Vapor Flow in the Column
35 Control of a Fluid Catalytic Cracker
35.1 Introduction
35.2 Initial Input–output Variable Selection
35.3 Extension of the Basic Control Scheme
35.4 Selection of the Final Control Scheme
References

Appendix A. Modeling an Extraction Process
A1: Problem Analysis
A2: Dynamic Process Model Development
A3: Dynamic Process Model Analysis
A4: Dynamic Process Simulation
A5: Process Control Simulation Hints

                                                                   

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