CONTROL OF CHEMICAL PROCESSES

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

Lectures and class exercises.

Should teaching be carried out in mixed mode or remotely, it may be necessary to introduce changes with respect to previous statements, in line with the programme planned and outlined in the syllabus.

1. INTRODUCTION TO PROCESS CONTROL

Introductory considerations on control. Control objectives and benefits.

2. MODELLING OF CHEMICAL PROCESSES

Mathematical modelling principles. Balancing equations, procedures and examples. Linearization.

3. PROCESS DYNAMICS

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

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

The concept of stability. Stability and location of poles. Criteria for analysis of stability. Routh test. Bode criterion.

6. EMPIRICAL MODEL IDENTIFICATION

Introduction. Empirical Model building procedure. The process reaction curve. Statistical model identification.

7. PID CONTROLLERS

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

General principles. Cascade control. Feedforward control.

MATLAB EXERCISES

Matlab excercises for the topics covered by theory.

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.