Advanced Control Foundation: Tools, Techniques and Applications. Terrence Blevins Willy K. Wojsznis Mark Nixon

Transcription

1 Advanced Control Foundation: Tools, Techniques and Applications Terrence Blevins Willy K. Wojsznis Mark Nixon

2 Contents Acknowledgments About the Authors vii xvii Foreword xxi Chapter 1 INTRODUCTION 1 Chapter 2 MAXIMIZING RETURN ON CONTROL SYSTEM INVESTMENT Economic Incentive, Ammonia Plant Example, Reducing Process Variation Achieving Control Objectives, Single Loop Control, Multi-Loop Techniques, Advanced Control, Pulp Bleaching, Primary Reformer Temperature Control, Balancing Complexity with Benefits, 32 Bibliography, 33 Chapter 3 EVALUATING CONTROL SYSTEM PERFORMANCE Evaluating Control Performance, Improving Control Utilization, 42 ix

3 x ADVANCED CONTROL FOUNDATION: TOOLS, TECHNIQUES AND APPLICATIONS Transmitter Problems, Incorrect Tuning, Valve/Actuator Diagnostics, Changing Process Gain, Incorrect Split Range Setup, Loop Interaction, Addressing Process Variability, Changing Process Gain and Dynamics, Unmeasured Process Disturbances, Process Dynamics, Loop Interaction, Changing Limit Conditions, Quality Parameter Lab Measurement, Application Example, Workshop Exercises Introduction, Evaluating Control System Performance Workshop, Technical Basis, Control Performance Monitoring Infrastructure, Control Performance Evaluation Algorithms, 68 Bibliography, 75 Chapter 4 ON-DEMAND TUNING Process Identification, Simulation of Loop Response, On-Demand Tuning Workshop, Technical Basis, Basics of Relay Oscillation Tuning, Model-Based Tuning, Robustness-Based Tuning, Some Alternate Tuning Approaches, 109 Bibliography, 110 Chapter 5 ADAPTIVE TUNING Adaptive Control Examples, Continuous Reactor Control, Batch Reactor, Hydrogen to Nitrogen (H/N) Ratio in Ammonia Production, Neutralizer, Plant Master Control, 119

4 CONTENTS xi 5.2 Application Example, Enabling Model Identification, Applying Models to Loop Tuning, Adaptive Tuning Workshop, Technical Basis, Model-Free Adaptive Tuning, Model-Based Recursive Adaptive Tuning, Discrete Fourier Transform Adaptation Technique, Adaptive Tuning with Model Switching and Parameter Interpolation, 134 Bibliography, 142 Chapter 6 FUZZY LOGIC CONTROL Application Example, Fuzzy Logic Control Workshop, Technical Basis, Introduction to Fuzzy Logic Control, Building a Fuzzy Logic Controller, Fuzzy Logic PID Controller, Fuzzy Logic Control Nonlinear PI Relationship, FPI and PI Relationships, Automatic Tuning of a Fuzzy PID Controller, 169 Bibliography, 170 Chapter 7 NEURAL NETWORKS FOR PROPERTY ESTIMATION Example Pulp and Paper Industry, Property Estimator Application Example, Neural Networks for Property Estimation Workshop, Technical Basis, Data Collection, Identification of Input Delay, Input Sensitivity, Determining Input Weights, Nodes in the Hidden Layer, Correction for Process Changes, 191 Bibliography, 192

5 xii ADVANCED CONTROL FOUNDATION: TOOLS, TECHNIQUES AND APPLICATIONS Chapter 8 INTELLIGENT PID Recovery from Process Saturation, Control Using Wireless Transmitter, Application Examples, Control of a Bioreactor Using Wireless Devices, Compressor Surge Control, Intelligent PID Workshop, Technical Basis, Wireless Control, Recovery from Process Saturation, Extension to Include Rate, 220 Bibliography, 221 Chapter 9 CONTINUOUS DATA ANALYTICS Application Example, Defining Model Inputs, Model Building, Viewing Data Analytics On-line, Continuous Data Analytics Workshop, Technical Basis, Data Formatting for Predictive Model Development, Process Monitoring and Predictive Algorithms Review, Data Preprocessing and Scaling, PCA Modeling and On-line Fault Detection, PLS Modeling and On-line Prediction, 258 Bibliography, 260 Chapter 10 BATCH DATA ANALYTICS Batch Production Challenges, Role of Data Analytics in Facing Batch Production Challenges, Batch Analytics Overview, Application of Batch Analytics to Specialty Chemicals Production, Data Analytics Application Example Modeling and On-line Operation, Defining Model Input, Viewing Data Analytics On-line, Batch Data Analytics Workshop, 285

6 CONTENTS xiii 10.4 Technical Basis, Feedstock Property Modeling, Data Preprocessing and Validation, Multi-stage Batch Modeling, Alignment of Batch Data and On-line Model, Data Arrangement Unfolding, PCA Modeling and On-line Fault Detection, PLS Modeling and On-line Quality Prediction, 303 Bibliography, 305 Chapter 11 SIMPLE MPC MPC as a Replacement for PID, Commissioning MPC, MPC Replacement for PID with Feedforward, MPC Replacement for PID Override, Using MPC to Address Process Interactions, Application Examples, Evaporator Control, Dryer Control, Rotary Kiln Control, Pulp Brightness Control, MPC Control at Changing Production Rate, Batch Reactor Control, MPC Application Development Procedure, Process Analysis and MPC Configuration Design, Process Testing, Process Model Generation, Controller Generation, MPC Simulation and Tuning Validation, MPC Control Evaluation and Tuning Adjustment, Simple MPC Workshop, Technical Basis, The Basics of Process Modeling, Process Model Identification, Unconstrained Model Predictive Control, Integrating Constraints Handling, Optimization and Model Predictive Control, 356 Bibliography, 361

7 xiv ADVANCED CONTROL FOUNDATION: TOOLS, TECHNIQUES AND APPLICATIONS Chapter 12 MPC INTEGRATED WITH OPTIMIZATION Application Example Multiple Effect Evaporator, Evaporator Process Overview, Project Motivation and Design Considerations, Why Use Model Predictive Control?, Model Predictive Control Strategy Development, Model Development and Verification, MPC Operation, Tuning and Optimization, Dealing with Major Process Constraints, Evaporator Flush Control, Solids Measurement, Operational Results, Summary, Recent Work and Future Opportunities, Acknowledgments, Application Example CTMP Refiner, Process Description, Electrical Energy Consumption Model, Process Model Identification, MPC Control Strategy, Results, Summary, Acknowledgments, Application Example Heavy Oil Fractionator, MPC Integrated with Optimization Workshop, Technical Basis, MPC Operation Overview, On-line Multi-objective Optimizer Functionality Overview, Multi-objective MPC Optimization Background, Multi-objective Optimization Function for Infeasibility Handling, Multi-objective Optimization Function for Extending LP Functionality, System Functionality for Multi-objective Optimization, Unconstrained MPC Controller Supervised by the Optimizer, Constrained MPC Controller Integrated with the Optimizer, 433 Bibliography, 434

8 CONTENTS xv Chapter 13 ON-LINE OPTIMIZATION Why Optimization A Look at Boiler Load Allocation, Energy Optimization in a Pulp and Paper Mill, Introduction to Cogeneration, Introduction to Energy Management, An Integrated Real-time Energy Management System, Integrated EMS Case Study, Summary of Results, Summary, On-line Optimization Workshop, Technical Basis, 466 Bibliography, 468 Chapter 14 PROCESS SIMULATION Process Simulation Techniques, Developing a Process Simulation from the P&ID, Simulating Process Non-linearity, Other Considerations, Process Simulation Workshop, Theory Simulation Based on Step Response, 488 Bibliography, 493 Chapter 15 INTEGRATING ADVANCED CONTROL INTO A DCS Integrating with Plant Systems, Network and System Setup, Application Example, 507 Bibliography, 509 Appendix A 511 Glossary of Terms 517 Index 527