Advanced practical process control / B. Roffel, B.H. Betlem.

Includes bibliographical references and index.

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.