Contents

ACKNOWLEDGEMENT xiii

ABOUT THE AUTHORS xv

FOREWORD xvii

Chapter 1 INTRODUCTION 1

Chapter 2 SETTING THE FOUNDATION 5 Practice, 5

Overview 5 Opportunity Assessment 12 Examples 15

Application, 20 General Procedure 20 Application Detail 26 Rules of Thumb 74

Theory, 76 Process Time Constants and Gains 76 Process Time Delay 79 Ultimate Gain and Period 80 Peak and Integrated Error 82 Feedforward Control 84 Dead Time from Valve Dead Band 84

Nomenclature, 85 References, 86

vii

viii Advanced Control Unleashed

Chapter 3 APC PATHWAYS 89 Practice, 89


Application, 106 General Procedure 106 Application Detail 108 Rules of Thumb 115

References, 116

Chapter 4 EVALUATING SYSTEM PERFORMANCE 119 Practice, 119


Application, 129 General Procedure 129 Application Details 131 Rules of Thumb 143 Guided Tour 144

Theory, 147 Using Statistics for Control Performance Evaluation 150 Extending the Concept to the Multi-variable Environment 153 Addressing Advanced Control 154 Diagnostic Tools 156

References, 160

Chapter 5 ABNORMAL SITUATION MANAGEMENT 163 Practice, 163


Application, 168 General Procedure 168 Application Details 169 Rules of Thumb 171 Guided Tour 173

Theory, 177 Introduction to Expert Systems 177 Rules 178 Inference Engine 180 Facts 181

References, 182

Table of Contents ix

Chapter 6 AUTOMATED TUNING 183 Practice, 183


Application, 197 General Procedure 197 Application Detail 200 Rules of Thumb 202 Guided Tour 206

Theory, 208 Introduction to Auto Tuners 208 Basics of Relay-Oscillation Tuning 210 Model Based Tuning 218 Robustness Based Tuning 221 Adaptive Control 225

References, 237

Chapter 7 FUZZY LOGIC CONTROL 239 Practice, 239


Application, 241 General Procedure 241 Rules of Thumb 242 Guided Tour 242

Theory, 244 Introduction to Fuzzy Logic Control 244 Building a Fuzzy Logic Controller 247 Fuzzy Logic PID Controller 251 Fuzzy Logic Control Nonlinear PI Relationship 254 FPID and PID Relations 257 Automation of Fuzzy Logic Controller Commissioning 258

References, 259

Chapter 8 PROPERTIES ESTIMATION 261 Practice, 261

Overview 261 Opportunity Assessment 263 Example Dynamic Linear Estimator 265 Examples - Neural Networks 269

Application, 274 General Procedure 274 Application Detail 279

X Advanced Control Unleashed

Rules of Thumb 289 Guided Tour 289

Theory, 294 Dynamic Linear Estimator 294 Neural Networks 296

References, 305

Chapter 9 MODEL PREDICTIVE CONTROL 307 Practice, 307


Application, 337 General Procedure 337 Application Detail 339 Rules of Thumb 353 Guided Tour 355

Theory, 362 The Basics of Process Modeling 364 Identifying the Process Model 367 Unconstrained Model Predictive Control 369 Integrating Constraints Handling, Optimization and Model Predictive Control 373

References, 381

Chapter 10 VIRTUAL PLANT 383 Practice, 383


Application, 389 General Procedure 389 Online Adaptation 393 Application Detail 395 Rules of Thumb 399 Guided Tour 400

Theory, 403 References, 408

Appendix A ADDITIONAL OPPORTUNITY ASSESSMENT QUESTIONS 409

Appendix B BATCH-TO-CONTINUOUS TRANSITION 415

Appendix C DEFINITIONS 419

Table of Contents xi

Appendix D TOP 20 MISTAKES 425

INDEX 431

Front MatterDedicationAcknowledgementAbout the AuthorsForeword

Table of Contents1. Introduction2. Setting the Foundation2.1 Practice2.1.1 Overview2.1.1.1 Measurement2.1.1.2 Final Element2.1.1.3 Effect on APC

2.1.2 Opportunity Assessment2.1.3 Examples2.1.3.1 Neutralization Process2.1.3.2 Distillation Process

2.2 Application2.2.1 General Procedure2.2.2 Application Detail2.2.2.1 Valve Selection2.2.2.2 Valve Installation2.2.2.3 The Variable-Speed Drive Alternative2.2.2.4 Measurement Selection2.2.2.5 Measurement Installation2.2.2.6 Open Loop Response2.2.2.7 PID Controller Tuning2.2.2.8 Cascade Control2.2.2.9 Feedforward Control

2.2.3 Rules of Thumb

2.3 Theory2.3.1 Process Time Constants and Gains2.3.2 Process Time Delay2.3.3 Ultimate Gain and Period2.3.4 Peak and Integrated Error2.3.5 Feedforward Control2.3.6 Dead Time from Valve Dead Band

2.4 Nomenclature2.5 References

3. APC Pathways3.1 Practice3.1.1 Overview3.1.2 Opportunity Assessment3.1.3 Examples3.1.3.1 Global Production Maximum3.1.3.2 Production Limited by Operating Constraint3.1.3.3 Shifting Bottlenecks

3.2 Application3.2.1 General Procedure3.2.2 Application Detail3.2.2.1 Neutralizer3.2.2.2 Paper Machine3.2.2.3 Reactor3.2.2.4 Popular Excuses

3.3.3 Rules of Thumb

3.3 References

4. Evaluating System Performance4.1 Practice4.1.1 Overview4.1.2 Opportunity Assessment4.1.3 Examples4.1.3.1 Control Utilization and Its Impact4.1.3.2 Control Variability4.1.3.3 Quality Variable Monitoring4.1.3.4 Caster Monitoring

4.2 Application4.2.1 General Procedure4.2.2 Application Details4.2.2.1 Continuous Control4.2.2.2 Loop Not in Normal Mode4.2.2.3 Limited Output4.2.2.4 Bad I/O4.2.2.5 High Variability4.2.2.6 Batch Control4.2.2.7 Multivariate Analysis4.2.2.8 Scores Plot4.2.2.9 Hotelling T^24.2.2.10 Contribution Plot

4.2.3 Rules of Thumb4.2.4 Guided Tour

4.3 Theory4.3.1 Using Statistics for Control Performance Evaluation4.3.2 Extending the Concept to the Multi-Variable Environment4.3.3 Addressing Advanced Control4.3.3.1 Abnormal Inputs4.3.3.2 Control Limited4.3.3.3 Incorrect Mode4.3.3.4 Control Index

4.3.4 Diagnostic Tools4.3.4.1 System Communication Model

4.4 References

5. Abnormal Situation Management5.1 Practice5.1.1 Overview5.1.2 Opportunity Assessment5.1.3 Examples5.1.3.1 Alarm Screening5.1.3.2 Fault Detection

5.2 Application5.2.1 General Procedure5.2.2 Application Details5.2.2.1 Selection of the Problem5.2.2.2 Define Process I/O Requirement5.2.2.3 Capture Expert Knowledge5.2.2.4 Test the Expert System5.2.2.5 Maintain the Knowledge Database


5.3 Theory5.3.1 Introduction to Expert Systems5.3.2 Rules5.3.3 Inference Engine5.3.4 Facts5.3.4.1 Other Structures

5.4 References

6. Automated Tuning6.1 Practice6.1.1 Overview6.1.2 Opportunity Assessment6.1.3 Examples6.1.3.1 Liquid Flow Control6.1.3.2 Liquid Pressure Control6.1.3.3 Static Mixer Concentration Control6.1.3.4 Continuous Reactor Pressure Control6.1.3.5 Continuous Reactor Temperature Control6.1.3.6 Continuous Recycle Tank and Reactor Level Control6.1.3.7 Batch Reactor Temperature Control6.1.3.8 Surge Tank Level Control6.1.3.9 Continuous Column Temperature Control6.1.3.10 Continuous Distillate Receiver Level Control6.1.3.11 Furnace Pressure Control6.1.3.12 Boiler Drum Level Control

6.2 Application6.2.1 General Procedure6.2.2 Application Detail6.2.2.1 Selection of the Proper Test Conditions6.2.2.2 Selection of the Proper Tuning Method (Rule)

6.2.3 Rules of Thumb6.2.3.1 Rules of Thumb on Step Size6.2.3.2 Rules of Thumb on Tuning Methods

6.2.4 Guided Tour

6.3 Theory6.3.1 Introduction to Auto Tuners6.3.2 Basics of Relay-Oscillation Tuning6.3.2.1 Process Testing6.3.2.2 Evaluation of Process Characteristics6.3.2.3 Calculation of PID Controller Parameters6.3.2.4 Developing The Process Model

6.3.3 Model Based Tuning6.3.3.1 Self-Regulating Process6.3.3.2 Integrating Process

6.3.4 Robustness Based Tuning6.3.5 Adaptive Control6.3.5.1 Model-Free Adaptive Tuning6.3.5.2 Model-Based Adaptive Tuning6.3.5.3 Model Switching Adaptation in the Time Domain6.3.5.4 Discrete Fourier Transform Adaptation Technique

6.4 References

7. Fuzzy Logic Control7.1 Practice7.1.1 Overview7.1.2 Opportunity Assessment7.1.3 Examples7.1.3.1 Temperature Control7.1.3.2 Moisture Control

7.2 Application7.2.1 General Procedure7.2.2 Rules of Thumb7.2.3 Guided Tour

7.3 Theory7.3.1 Introduction to Fuzzy Logic Control7.3.2 Building a Fuzzy Logic Controller7.3.2.1 Fuzzification7.3.2.2 Fuzzy Logic Inference Rules7.3.2.3 Defuzzification

7.3.3 Fuzzy Logic PID Controller7.3.3.1 Membership Functions7.3.3.2 Fuzzy Logic Inference Rules

7.3.4 Fuzzy Logic Control Nonlinear PI Relationship7.3.5 FPID and PID Relations7.3.6 Automation of Fuzzy Logic Controller Commissioning

7.4 References

8. Properties Estimation8.1 Practice8.1.1 Overview8.1.1.1 Dynamic Linear Estimator8.1.1.2 Neural Networks

8.1.2 Opportunity Assessment8.1.2.1 Dynamic Linear Estimator8.1.2.2 Neural Network

8.1.3 Example - Dynamic Linear Estimator8.1.3.1 Distillation Process

8.1.4 Examples - Neural Networks8.1.4.1 Soft Sensor Tracking an Online Analyzer8.1.4.2 Soft Sensor Tracking a Laboratory Measurement8.1.4.3 Soft Sensor with No Feedback Element8.1.4.4 Continuous Digester8.1.4.5 Fermentation Process

8.2 Application8.2.1 General Procedure8.2.1.1 Dynamic Linear Estimator8.2.1.2 Neural Network

8.2.2 Application Detail8.2.2.1 Dynamic Linear Estimator8.2.2.2 Neural Network


8.3 Theory8.3.1 Dynamic Linear Estimator8.3.2 Neural Networks8.3.2.1 Data Collection8.3.2.2 Identification of Input Delay8.3.2.3 Input Sensitivity8.3.2.4 Determining Input Weights8.3.2.5 Nodes in the Hidden Layer8.3.2.6 Correction for Process Changes8.3.2.7 State-of-the-Art Implementation

8.4 References

9. Model Predictive Control9.1 Practice9.1.1 Overview9.1.2 Opportunity Assessment9.1.3 Examples9.1.3.1 Distillation Tower Control9.1.3.2 Evaporator Control9.1.3.3 Dryer Control9.1.3.4 Rotary Kiln Control9.1.3.5 Variable-Dead Time Bleach Plant Control9.1.3.6 MPC for Processes with Varying Delay9.1.3.7 Addressing Mixed Dynamics9.1.3.8 Batch Reactor Control

9.2 Application9.2.1 General Procedure9.2.2 Application Detail9.2.2.1 Process Analysis9.2.2.2 Process Testing9.2.2.3 Process Model Development9.2.2.4 Controller Design9.2.2.5 MPC Controller Testing in Simulation9.2.2.6 MPC Commissioning/Operation


9.3 Theory9.3.1 The Basics of Process Modeling9.3.2 Identifying the Process Model9.3.3 Unconstrained Model Predictive Control9.3.4 Integrating Constraints Handling, Optimization and Model Predictive Control9.3.4.1 Simple Constraints Handling9.3.4.2 Integrated Optimization: Combining Constraints Handling, Optimization, and Model Predictive Control

9.4 References

10. Virtual Plant10.1 Practice10.1.1 Overview10.1.2 Opportunity Assessment10.1.3 Examples10.1.3.1 Failure Handling10.1.3.2 Control Response10.1.3.3 Operator Training

10.2 Application10.2.1 General Procedure10.2.2 Online Adaptation10.2.3 Application Detail10.2.4 Rules of Thumb10.2.5 Guided Tour

10.3 TheoryReferences

Appendix A: Additional Opportunity Assessment Questions1. Increase Benefits in All Areas2. Improve Yields3. Reduce Energy and Utilities4. Reduce Effluent5. Reduce Operating Cost6. Reduce Maintenance Cost7. Reduce Lab Cost8. Improve Product Quality9. Reduce Rework10. Reduce Shutdowns and Upsets11. Reduce Cycle Time12. Avoid Costs for Other Investments

Appendix B: Batch-to-Continuous TransitionAppendix C: DefinitionsAppendix D: Top 20 MistakesIndexABCDEFGHIJKLMNOPQRSTUVWZ

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