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Index
Cover
Title Page
Copyright
Dedication
Contents
Acknowledgments
About the Authors
Foreword
1 - Introduction
2 - Maximizing Return on Control System Investment
2.1 Economic Incentive
2.1.1 Ammonia Plant Example
2.2 Reducing Process Variation – Achieving Control Objectives
2.2.1 Single Loop Control
2.2.2 Multi-Loop Techniques
2.3 Advanced Control
2.3.1 Pulp Bleaching
2.3.2 Primary Reformer Temperature Control
2.4 Balancing Complexity with Benefits
BIBLIOGRAPHY
3 - Evaluating Control System Performance
3.1 Evaluating Control Performance
3.2 Improving Control Utilization
3.2.1 Transmitter Problems
3.2.2 Incorrect Tuning
3.2.3 Valve/Actuator Diagnostics
3.2.4 Changing Process Gain
3.2.5 Incorrect Split Range Setup
3.2.6 Loop Interaction
3.3 Addressing Process Variability
3.3.1 Changing Process Gain and Dynamics
3.3.2 Unmeasured Process Disturbances
3.3.3 Process Dynamics
3.3.4 Loop Interaction
3.3.5 Changing Limit Conditions
3.3.6 Quality Parameter – Lab Measurement
3.4 Application Example
3.5 Workshop Exercises – Introduction
3.6 Evaluating Control System Performance Workshop
3.7 Technical Basis
3.7.1 Control Performance Monitoring Infrastructure
3.7.2 Control Performance Evaluation Algorithms
BIBLIOGRAPHY
4 - On-Demand Tuning
4.1 Process Identification
4.1.1 Simulation of Loop Response
4.2 On-Demand Tuning Workshop
4.3 Technical Basis
4.3.1 Basics of Relay Oscillation Tuning
4.3.2 Model-Based Tuning
4.3.3 Robustness-Based Tuning
4.3.4 Some Alternate Tuning Approaches
BIBLIOGRAPHY
5 - Adaptive Tuning
5.1 Adaptive Control – Examples
5.1.1 Continuous Reactor Control
5.1.2 Batch Reactor
5.1.3 Hydrogen to Nitrogen (H/N) Ratio in Ammonia Production
5.1.4 Neutralizer
5.1.5 Plant Master Control
5.2 Application Example
5.2.1 Enabling Model Identification
5.2.2 Applying Models to Loop Tuning
5.3 Adaptive Tuning Workshop
5.4 Technical Basis
5.4.1 Model-Free Adaptive Tuning
5.4.2 Model-Based Recursive Adaptive Tuning
5.4.3 Discrete Fourier Transform Adaptation Technique
5.4.4 Adaptive Tuning with Model Switching and Parameter Interpolation
BIBLIOGRAPHY
6 - Fuzzy Logic Control
6.1 Application Example
6.2 Fuzzy Logic Control Workshop
6.3 Technical Basis
6.3.1 Introduction to Fuzzy Logic Control
6.3.2 Building a Fuzzy Logic Controller
6.3.3 Fuzzy Logic PID Controller
6.3.4 Fuzzy Logic Control Nonlinear PI Relationship
6.3.5 FPI and PI Relationships
6.3.6 Automatic Tuning of a Fuzzy PID Controller
BIBLIOGRAPHY
7 - Neural Networks for Property Estimation
7.1 Example – Pulp and Paper Industry
7.2 Property Estimator Application Example
7.3 Neural Networks for Property Estimation Workshop
7.4 Technical Basis
7.4.1 Data Collection
7.4.2 Identification of Input Delay
7.4.3 Input Sensitivity
7.4.4 Determining Input Weights
7.4.5 Nodes in the Hidden Layer
7.4.6 Correction for Process Changes
BIBLIOGRAPHY
8 - Intelligent PID
8.1 Recovery from Process Saturation
8.2 Control Using Wireless Transmitter
8.3 Application Examples
8.3.1 Control of a Bioreactor Using Wireless Devices
8.3.2 Compressor Surge Control
8.4 Intelligent PID Workshop
8.5 Technical Basis
8.5.1 Wireless Control
8.5.2 Recovery from Process Saturation
8.5.3 Extension to Include Rate
BIBLIOGRAPHY
9 - Continuous Data Analytics
9.1 Application Example
9.1.1 Defining Model Inputs
9.1.2 Model Building
9.2 Viewing Data Analytics On-line
9.3 Continuous Data Analytics Workshop
9.4 Technical Basis
9.4.1 Data Formatting for Predictive Model Development
9.4.2 Process Monitoring and Predictive Algorithms Review
9.4.3 Data Preprocessing and Scaling
9.4.4 PCA Modeling and On-line Fault Detection
9.4.5 PLS Modeling and On-line Prediction
BIBLIOGRAPHY
10 - Batch Data Analytics
10.1 Batch Production Challenges
10.1.1 Role of Data Analytics in Facing Batch Production Challenges
10.1.2 Batch Analytics Overview
10.1.3 Application of Batch Analytics to Specialty Chemicals Production
10.2 Data Analytics Application Example – Modeling and On-line Operation
10.2.1 Defining Model Input
10.2.2 Viewing Data Analytics On-line
10.3 Batch Data Analytics Workshop
10.4 Technical Basis
10.4.1 Feedstock Property Modeling
10.4.2 Data Preprocessing and Validation
10.4.3 Multi-stage Batch Modeling
10.4.4 Alignment of Batch Data and On-line Model
10.4.5 Data Arrangement – Unfolding
10.4.6 PCA Modeling and On-line Fault Detection
10.4.7 PLS Modeling and On-line Quality Prediction
BIBLIOGRAPHY
11 - Simple MPC
11.1 MPC as a Replacement for PID
11.2 Commissioning MPC
11.3 MPC Replacement for PID with Feedforward
11.4 MPC Replacement for PID Override
11.5 Using MPC to Address Process Interactions
11.6 Application Examples
11.6.1 Evaporator Control
11.6.2 Dryer Control
11.6.3 Rotary Kiln Control
11.6.4 Pulp Brightness Control
11.6.5 MPC Control at Changing Production Rate
11.6.6 Batch Reactor Control
11.7 MPC Application Development Procedure
11.7.1 Process Analysis and MPC Configuration Design
11.7.2 Process Testing
11.7.3 Process Model Generation
11.7.4 Controller Generation
11.7.5 MPC Simulation and Tuning Validation
11.7.6 MPC Control Evaluation and Tuning Adjustment
11.8 Simple MPC Workshop
11.9 Technical Basis
11.9.1 The Basics of Process Modeling
11.9.2 Process Model Identification
11.9.3 Unconstrained Model Predictive Control
11.9.4 Integrating Constraints Handling, Optimization and Model Predictive Control
BIBLIOGRAPHY
12 - MPC Integrated with Optimization
12.1 Application Example – Multiple Effect Evaporator
12.1.1 Evaporator Process Overview
12.1.2 Project Motivation and Design Considerations
12.1.3 Why Use Model Predictive Control?
12.1.4 Model Predictive Control Strategy Development
12.1.5 Model Development and Verification
12.1.6 MPC Operation, Tuning and Optimization
12.1.7 Dealing with Major Process Constraints
12.1.8 Evaporator Flush Control
12.1.9 Solids Measurement
12.1.10 Operational Results
12.1.11 Summary
12.1.12 Recent Work and Future Opportunities
12.1.13 Acknowledgments
12.2 Application Example – CTMP Refiner
12.2.1 Process Description
12.2.2 Electrical Energy Consumption Model
12.2.3 Process Model Identification
12.2.4 MPC Control Strategy
12.2.5 Results
12.2.6 Summary
12.2.7 Acknowledgments
12.3 Application Example – Heavy Oil Fractionator
12.4 MPC Integrated with Optimization Workshop
12.5 Technical Basis
12.5.1 MPC Operation Overview
12.5.2 On-line Multi-objective Optimizer Functionality Overview
12.5.3 Multi-objective MPC Optimization Background
12.5.4 Multi-objective Optimization Function for Infeasibility Handling
12.5.5 Multi-objective Optimization Function for Extending LP Functionality
12.5.6 System Functionality for Multi-objective Optimization
12.5.7 Unconstrained MPC Controller Supervised by the Optimizer
12.5.8 Constrained MPC Controller Integrated with the Optimizer
BIBLIOGRAPHY
13 - On-line Optimization
13.1 Why Optimization – A Look at Boiler Load Allocation
13.2 Energy Optimization in a Pulp and Paper Mill
13.2.1 Introduction to Cogeneration
13.2.2 Introduction to Energy Management
13.2.3 An Integrated Real-time Energy Management System
13.2.4 Integrated EMS Case Study
13.2.5 Summary of Results
13.2.6 Summary
13.3 On-line Optimization Workshop
13.4 Technical Basis
BIBLIOGRAPHY
14 - Process Simulation
14.1 Process Simulation Techniques
14.2 Developing a Process Simulation from the P&ID
14.3 Simulating Process Non-linearity
14.4 Other Considerations
14.5 Process Simulation Workshop
14.6 Theory – Simulation Based on Step Response
BIBLIOGRAPHY
15 - Integrating Advanced Control into a DCS
15.1 Integrating with Plant Systems
15.2 Network and System Setup
15.3 Application Example
BIBLIOGRAPHY
Appendix A
Glossary of Terms
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