1st Edition
Process Control Fundamentals Analysis, Design, Assessment, and Diagnosis
The field of process control has evolved gradually over the years, with emphasis on key aspects including designing and tuning of controllers. This textbook covers fundamental concepts of basic and multivariable process control, and important monitoring and diagnosis techniques.
It discusses topics including state-space models, Laplace transform to convert state-space models to transfer function models, linearity and linearization, inversion formulae, conversion of output to time domain, stability analysis through partial fraction expansion, and stability analysis using Routh table and Nyquits plots. The text also covers basics of relative gain array, multivariable controller design and model predictive control. The text comprehensively covers minimum variable controller (MVC) and minimum variance benchmark with the help of solved examples for better understanding. Fundamentals of diagnosis of control loop problems are also explained and explanations are bolstered through solved examples. Pedagogical features including solved problems and unsolved exercises are interspersed throughout the text for better understanding.
The textbook is primarily written for senior undergraduate and graduate students in the field of chemical engineering and biochemical engineering for a course on process control.
The textbook will be accompanied by teaching resource such a collection of slides for the course material and a includsolution manual for the instructors.
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
Biography
Raghunathan Rengaswamy is an institute chair professor at the department of chemical engineering and a core member of the recently established Robert Bosch Center for Data Science and AI (RBC-DSAI) at the Indian Institute of Technology Madras. Prior to this, he was professor, chemical engineering and co-director of the process control and optimization Consortium (PCOC) at Texas Tech University, USA, associate and full professor at Clarkson University, New York and assistant professor at Indian Institute of Technology Bombay, India. He has also been a visiting professor at Purdue University, USA, University of Delaware, USA and University of Alberta, Canada. His research interests include data analytics, fault detection, diagnosis (FDD), development of sensor placement (SP) algorithms for FDD, and controller performance assessment (CPA). He has more than two decades of teaching and research experience and has published more than 100 research papers in journals of national and international repute. Babji Srinivasan is currently working as an assistant professor, department of chemical engineering, Indian Institute of Technology Gandhinagar. His current research interests include design, control and monitoring of complex systems with human-in-the-loop, design for enhancing situation awareness in humans, modeling, control, and optimization of wastewater treatment systems. He worked as a postdoctoral research scientist at Columbia University in New York. He has taught courses including control theory, probability, and random process, modern control theory and design of experiments at undergraduate and graduate level. Nirav P Bhatt is presently working as an assistant professor, department of biotechnology, Indian Institute of Technology Madras, India. His current research interests include modeling and identification of complex biological and human-made networks, particularly, smart infrastructure, reinforcement, and machine learning approaches. He has taught courses including bioprocess control, process control, optimal control, modeling and simulation methods to bioprocess, chemical and electrical engineers at undergraduate and graduate level. He has published several journal and conference papers in areas of control and learning techniques and its applications in process and engineering industries.
