Roffel, B: Advanced Practical Process Control

2004
ISBN: 978-3-540-40480-4
Verlag: Springer-Verlag GmbH

Buch, Englisch, 309 Seiten, Book w. online files / update, Format (B × H): 155 mm x 235 mm, Gewicht: 644 g

An application-oriented approach to process control. The reference text systematically explains process identification, control and optimization, the three key steps needed to solve a multivariable control problem. Theory is discussed as far as it is needed to understand and solve the defined problem, while numerous examples written in MATLAB illustrate the problem-solving approach.

Zielgruppe

Graduate

Autoren/Hrsg.

Roffel, Brian

Betlem, Ben

Fachgebiete

• Technische Wissenschaften Verfahrenstechnik | Chemieingenieurwesen | Biotechnologie Chemische Verfahrenstechnik
• Technische Wissenschaften Elektronik | Nachrichtentechnik Elektronik Sensorik
• Rechtswissenschaften Strafrecht
• Technische Wissenschaften Technik Allgemein Mess- und Automatisierungstechnik
• Technische Wissenschaften Technik Allgemein Mathematik für Ingenieure
• Technische Wissenschaften Maschinenbau | Werkstoffkunde Technische Mechanik | Werkstoffkunde Statik, Dynamik, Kinetik, Kinematik

Inhaltsverzeichnis

1 Introduction to Advanced Process Control Concepts.- 1.1 Process Time Constant.- 1.2 Domain Transformations.- 1.3 Laplace Transformation.- 1.4 Discrete Approximations.- 1.5 z-Transforms.- 1.6 Advanced and Modified z-Transforms.- 1.7 Common Elements in Control.- 1.8 The Smith Predictor.- 1.9 Feed-forward Control.- 1.10 Feed-forward Control in a Smith Predictor.- 1.11 Dahlin’s Control Algorithm.- References.- 2 Process Simulation.- 2.1 Simulation using Matlab Simulink.- 2.2 Simulation of Feed-forward Control.- 2.3 Control Simulation of a 2x2 System.- 2.4 Simulation of Dahlin’s Control Algorithm.- 3 Process Modeling and Identification.- 3.1 Model Applications.- 3.2 Types of Models.- 3.2.1 White Box and Black Box Models.- 3.2.2 Linear and Non-linear Models.- 3.2.3 Static and Dynamic Models.- 3.2.4 Distributed and Lumped Parameter Models.- 3.2.5 Continuous and Discrete Models.- 3.3 Empirical (linear) Dynamic Models.- 3.4 Model Structure Considerations.- 3.4.1 Parametric Models.- 3.4.2 Non-parametric Models.- 3.5 Model Identification.- 3.5.1 Introduction.- 3.5.2 Identification of Parametric Models.- 3.5.3 Identification of Non-parametric Models.- References.- 4 Identification Examples.- 4.1 SISO Furnace Parametric Model Identification.- 4.2 MISO Parametric Model Identification.- 4.3 MISO Non-parametric Identification of a Non-integrating Process.- 4.4 MIMO Identification of an Integrating and Non-integrating Process.- 4.5 Design of Plant Experiments.- 4.5.1 Nature of Input Sequence.- 4.5.2 PRBS Type Input.- 4.5.3 Step Type Input.- 4.5.4 Type of Experiment.- 4.6 Data File Layout.- 4.7 Conversion of Model Structures.- 4.8 Example and Comparison of Open and Closed Loop Identification.- References.- 5 Linear Multivariable Control.- 5.1 Interaction in Multivariable Systems.- 5.1.1 The Relative Gain Array.- 5.1.2 Properties of the Relative Gain Array.- 5.1.3 Some Examples.- 5.1.4 The Dynamic Relative Gain Array.- 5.2 Dynamic Matrix Control.- 5.2.1 Introduction.- 5.2.2 Basic DMC Formulation.- 5.2.3 One Step DMC.- 5.2.4 Prediction Equation and Unmeasurable Disturbance Estimation.- 5.2.5 Restriction of Excessive Moves.- 5.2.6 Expansion of DMC to Multivariable Problems.- 5.2.7 Equal Concern Errors.- 5.2.8 Constraint Handling.- 5.2.9 Constraint Formulation.- 5.3 Properties of Commercial MPC Packages.- References.- 6 Multivariable Optimal Constraint Control Algorithm.- 6.1 General Overview.- 6.2 Model Formulation for Systems with Dead Time.- 6.3 Model Formulation for Multivariable Processes.- 6.4 Model Formulation for Multivariable Processes with Time Delays.- 6.5 Model Formulation in Case of a Limited Control Horizon.- 6.6 Mocca Control Formulation.- 6.7 Non-linear Transformations.- 6.8 Practical Implementation Guidelines.- 6.9 Case Study.- 6.10 Control of a Fluidized Catalytic Cracker.- 6.11 Examples of Case Studies in MATLAB.- 6.12 Control of Integrating Processes.- 6.13 Lab Exercises.- 6.14 Use of MCPC for Constrained Multivariable Control.- References.- 7 Internal Model Control.- 7.1 Introduction.- 7.2 Factorization of Multiple Delays.- 7.3 Filter Design.- 7.4 Feed-forward IMC.- 7.5 Example of Controller Design.- 7.6 LQ Optimal Inverse Design.- References.- 8 Nonlinear Multivariable Control.- 8.1 Non-linear Model Predictive Control.- 8.2 Non-linear Quadratic DMC.- 8.3 Generic Model Control.- 8.3.1 Basic Algorithm.- 8.3.2 Examples of the GMC Algorithm.- 8.3.3 The Differential Geometry Concept.- 8.4 Problem Description.- 8.4.1 Model Representation.- 8.4.2 Process Constraints.- 8.4.3 Control Objectives.- 8.5 GMC Application to the CSTR System.- 8.5.1 Relative Degree of the CSTR System.- 8.5 2 Cascade Control Algorithm.- 8.6 Discussion of the GMC Algorithm.- 8.7 Simulation of Reactor Control.- 8.8 One Step Reference Trajectory Control.- 8.9 Predictive Horizon Reference Trajectory Control.- References.- 9 Optimization of Process Operation.- 9.1 Introduction to Real-time Optimization.- 9.1.1 Optimization and its Benefits.- 9.1.2 Hierarchy of Optimization.- 9.1.3 Issues to be Addressed in Optimization.- 9.1.4 Degrees of Freedom Selection for Optimization.- 9.1.5 Procedure for Solving Optimization Problems.- 9.1.6 Problems in Optimization.- 9.2 Model Building.- 9.2.1 Phases in Model Development.- 9.2.2 Fitting Functions to Empirical Data.- 9.2.3 The Least Squares Method.- 9.3 The Objective Function.- 9.3.1 Function Extrema.- 9.3.2 Conditions for an Extremum.- 9.4 Unconstrained Functions: one Dimensional Problems.- 9.4.1 Newton’s Method.- 9.4.2 Quasi-Newton Method.- 9.4.3 Polynomial Approximation.- 9.5 Unconstrained Multivariable Optimization.- 9.5.1 Introduction.- 9.5.2 Newton’s Method.- 9.6 Linear Programming.- 9.6.1 Example.- 9.6.2 Degeneracies.- 9.6.3 The Simplex Method.- 9.6.4 The Revised Simplex Method.- 9.6.5 Sensitivity Analysis.- 9.7 Non-linear Programming.- 9.7.1 The Lagrange Multiplier Method.- 9.7.2 Other Techniques.- 9.7.3 Hints for Increasing the Effectiveness of NLP Solutions.- References.- 10 Optimization Examples.- 10.1 AMPL: a Multi-purpose Optimizer.- 10.1.1 Example of an Optimization Problem.- 10.1.2 AMPL Formulation of the Problem.- 10.1.3 General Structure of an AMPL Model.- 10.1.4 General AMPL Rules.- 10.1.5 Detailed Review of the Transportation Example.- 10.2 Optimization Examples.- 10.2.1 Optimization of a Separation Train.- 10.2.2 A Simple Blending Problem.- 10.2.3 A Simple Alkylation Reactor Optimization.- 10.2.4 Gasoline Blending.- 10.2.5 Optimization of a Thermal Cracker.- 10.2.6 Steam Net Optimization.- 10.2.7 Turbogenerator Optimization.- 10.2.8 Alkylation Plant Optimization.- References.- 11 Integration of Control and Optimization.- 11.1 Introduction.- 11.2 Description of the Desalination Plant.- 11.3 Production Maximization of Desalination Plant.- 11.4 Linear Model Predictive Control of Desalination Plant.- 11.5 Reactor problem definition.- 11.6 Multivariable Non-linear Control of the Reactor.- References.- Appendix I. MCPC software guide.- I.1 Installation.- I.2 Model identification.- I.2.1 General process information.- I.2.2 Identification data.- I.2.3 Output details.- I.3 Controller design.- I.4 Control simulation.- I.5 Dealing with constraints.- I.6 Saving a project.- Appendix II. Comparison of control strategies for a hollow shaft reactor.- II.1 Introduction.- II.2 Model Equations.- II.3 Proportional Integral Control.- II.4 Linear Multivariable Control.- II.5 Non-linear Multivariable Control.- References.