Process Control Fundamentals-Analysis, Design, Assessment, and Diagnosis

List of Figures
List of Tables
1 Introduction
1.1 Single-input-single-output and Multi-input-Multi-output Controllers
1.2 Regulator and Servo Control Problems
1.3 Dynamic Behaviour of Processes
2 Models for Control
2.1 Linearization
2.2 State-space representation of dynamic models
2.3 Transfer function models
2.4 Problems and Solutions
2.5 Exercises
3 Process Identification
3.1 Identification of First-order Processes
3.2 Identification of Second-order Processes
3.3 Identification of First-order with Dead Time
3.3.1 Identification of overdamped second-order systems
3.4 Problems and Solutions
4 Analysis of Transfer Function models
4.1 Introduction
4.2 Partial Fractions Approach for Solving Transfer Functions
4.3 Stability of Transfer function
4.4 Problems and Solutions
4.5 Exercises
5 Controllers and analysis of closed loop transfer functions
5.1 PID Controllers
5.2 Analysis of Block Diagram
5.3 Routh Test
5.4 Problems and Solutions
5.5 Exercises
6 Controller tuning
6.1 Stability based on Zeigler Nichols tuning
6.2 Tuning based on direct synthesis
6.2.1 Inverse response systems
6.2.2 Systems with delay element
6.2.3 Unstable Systems
6.3 Internal Model Control Method
6.4 Problems and solutions
6.5 Exercises
7 Multi-loop and multivariable control
7.1 Relative gain array
7.2 Cascade control
7.3 Static decoupler
7.4 Dynamic decoupling
7.5 Multivariable PID control
7.6 Problems and Solutions
8 Model Predictive Control
8.1 Introduction to MPC
8.1.1 Key aspects of MPC
8.2 Development of discrete models
8.3 MPC Formulation
8.3.1 MPC demonstration through a simple example
8.4 Bias removal in MPC
8.5 Problems and Solutions
9 Fundamentals of controller performance assessment
9.1 Performance assessment of control loops
9.2 Control loop performance assessment for step type changes in load
9.2.1 Model based approach - DS/IMC tuning rule based Indices
9.3 Algorithm for development of SIMC based performance indices
9.4 Idle index technique for identification of sluggish control loops
9.5 Detecting Oscillations
9.5.1 Introduction to ACF
9.6 Regularity of zero-crossings of the auto-correlation function
9.7 Control loop performance assessment based on variability in the process output
9.8 Minimum Variance Index
9.9 Scaling exponent based measure for controller performance assessment
9.9.1 Implications of the scaling exponent to control loop performance assessment
9.10 Problems and Solutions
10 Fundamentals of controller performance diagnosis
10.1 Control Valve and Stiction
10.2 Modeling of Stiction
10.2.1 One parameter model for valve stiction
10.3 Identification of stiction in control valves
10.3.1 Shape based formalism for stiction detection
10.3.2 Issues in stiction detection
10.3.3 Hammerstein model based approach to Stiction detection
10.3.4 Least Squares approach for model parameter identification
10.4 Oscillations due to improper controller tuning
11 Case Studies
11.1 Introduction
11.2 2x2 Distillation Column
11.2.1 RGA Analysis
11.2.2 Decoupler
11.2.3 PI Controller Tuning
11.2.4 Model Predictive Controller
11.3 3x3 Distillation Column
11.3.1 RGA Analysis
11.3.2 Decoupler
11.3.3 PID Controller Tuning
11.3.4 Model Predictive Controller
11.4 CSTR
11.4.1 Model Predictive Controller
Bibliography