CONTROL OF CHEMICAL PROCESSES
Academic Year 2024/2025 - Teacher: MATTIA FRASCAExpected Learning Outcomes
Knowledge and understanding
The student will learn fundamental knowledge on physical and empirical process modeling, on the dynamical response and stability and on the main control techniques applied in industrial processes
Applied knowledge and understanding
The student will learn the working principles and the tuning techniques of a PID controller. The student will also learn the fundamental of MATLAB to apply the theoretical topics discussed in the course
Making judgements
The student will be able to evaluate which PID tuning techniques and advanced control schemes are more suitable for the application under exam
Communication skills
The student will be able to know the theoretical and technical fundamental aspects related to the control of dynamical systems and discuss with process engineers about these issues
Learning skills
The student will be able to use the basic topics of the course to study more advanced control schemes, such as those based on multivariables
Course Structure
Lectures and class exercises.
Required Prerequisites
Attendance of Lessons
Detailed Course Content
1. INTRODUCTION TO PROCESS CONTROL (Prof. Frasca)
Introductory considerations on control. Control objectives and benefits.
2. MODELLING OF CHEMICAL PROCESSES (Prof. Frasca)
Mathematical modelling principles. Balancing equations, procedures and examples. Linearization.
3. PROCESS DYNAMICS (Prof. Frasca)
The Laplace Transform. Input-output models. Transfer functions. Block diagrams. Response to canonical inputs. Response to arbitrary signals. Frequency response
4. DYNAMIC BEHAVIOR OF TYPICAL PROCESS SYSTEMS (Prof. Frasca)
Dynamic behavior of first order systems. Dynamic behaviour of second order systems. Dynamic behaviour of first order systems with dead time. Pole dominance
5. STABILITY (Prof. Frasca)
The concept of stability. Stability and location of poles. Criteria for analysis of stability. Routh test. Bode criterion.
6. EMPIRICAL MODEL IDENTIFICATION (Prof. Frasca)
Introduction. Empirical Model building procedure. The process reaction curve. Statistical model identification.
7. PID CONTROLLERS (Prof. Frasca)
The feedback loop. The PID algorithm. Proportional, integral and derivative mode. The PID controller. Methods for PID tuning: PID controller tuning for dynamic performance. Methods for PID tuning: the Ziegler-Nichols closed-loop method. Digital implementation of PIDs. Practical issues of PID application.
8. ENHANCEMENTS TO SINGLE-LOOP PID FEEDBACK CONTROL (Prof. Frasca)
General principles. Cascade control. Feedforward control.
9. SENSORS E ACTUATORS FOR INDUSTRIAL PROCESSES (Prof.ssa Gambuzza)
Main characteristics of sensors. Temperature sensors. Pressure sensors. Flow sensors. Displacement sensors. Main characteristics of actuators. Control valves.
MATLAB EXERCISES (Prof. Frasca, Prof.ssa Gambuzza)
Matlab excercises for the topics covered by theory.
Textbook Information
1. T. E. Marlin, Process Control, McGraw Hill, 2nd Ed.
2 J. J. D’Azzo, C. H. Houpis, Linear control system analysis and design, McGraw Hill, 4th Ed.
3 J. Fraden, Handbook of modern sensors, Springer.
Course Planning
| Subjects | Text References | |
|---|---|---|
| 1 | Modeling | 1 |
| 2 | Stability | 2 |
| 3 | PID Controllers | 1 |
| 4 | Tuning techniques for PID | 1 |
| 5 | Sensors & Actuators | 3 |
| 6 | MATLAB exercises |
Learning Assessment
Learning Assessment Procedures
Oral exam
Examples of frequently asked questions and / or exercises
All topics of the book may be discussed at the examination
Il docente è disponibile anche a incontri di ricevimento in modalità telematica, previo appuntamento/The teacher is also available for online discussion. In this case please send an email to fix an appointment