Process Automation
2023-2024
1041422 - PROCESS AUTOMATION (Master in Control Engineering, year I) - Number of ECTS credits: 6
Language of instruction: English
Comunications
[Monday 18 December 2023] Test: bring sheets of paper, pens and a scientific calculator only; support material will be provided by the lecturer
[Monday 11 and Thursday 14 December 2023] No lessons
[Monday 27 November 2023] No lessons
[Thursday 16 November 2023] Lessons from 9:00 to 11:00
[Monday 30 October and Thursday 2 November 2023] No lessons
[Monday 25 September 2023] First lessons, a.y, 2023-2024
Course material and lesson diary
The course material and the lesson diary can be found on the Moodle platform of Sapienza (https://elearning.uniroma1.it/) within the course "Process Automation"
Course schedule
Monday, 14:00-16:00, Room A7, DIAG, via Ariosto 25
Thursday, 09:00-13:00, Room A7, DIAG, via Ariosto 25
Office hour
Refer to the Teaching page
Exams
January 2024
February 2024
March/April 2024 (for students enrolled on supplementary years of studies)
June 2024
July 2024
September 2024
October 2024 (for students enrolled on supplementary years of studies)
Course objectives
The course aims at providing basic concepts and methodologies related to the most used control methodology in the framework of process control, and at applying them to suitably modelled industrial process examples.
Expected learning results
The students will be able to manage basic Internal Model Control and Model Predictive Control methodologies with specific reference to process control problems.
Course program
Process Control Overview. Significance of process control. Objectives. Levels of Process Control. Process Dynamics and Mathematical Models. Regulatory Control. Control System Design. Multivariable Control. Batch Process Automation. Automation and Process Safety.
Dynamic models for process control.
Classic process control. Internal Model Control (IMC). PID tuning with IMC. Time-delay systems. Delay margin. Smith Predictor. Robustness to time delay mismatches.
Introduction to Model Predictive Control. The Model Predictive Control (MPC) principle. Relevance of MPC in current industrial process automation. Basic notions about Linear Programming and Dynamic Programming.
Model Predictive Controllers. MPC elements: prediction model, objective function, control law. MPC algorithms: Dynamic Matrix Control, Model Algorithmic Control, Predictive Functional Control. State space formulation.
Stability of Model Predictive Control.
Prerequisites
Basics of system and control theory.
Reference texts:
Dale E. Seborg, Thomas F. Edgar, Duncan A. Mellichamp, "Process Dynamics and Control", Wiley, 2nd ed., 2003, 736 p., ISBN 978-0471000778.
Rivera, Daniel E. "Internal model control: a comprehensive view." Arizona State University (1999).
Eduardo F. Camacho, Carlos Bordons Alba, “Model Predictive Control”, Series: Advanced Textbooks in Control and Signal Processing, XXII, 2nd ed. 2004, 405 p., ISBN 978-0-85729-398-5.
Slides and notes by A. Pietrabissa (https://elearning.uniroma1.it/).