Verwandte Artikel zu Advanced Practical Process Control
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Inhaltsangabe
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
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