PROCESS MODELING AND CONTROL
Academic Year 2024/2025 - Teacher: Arturo BUSCARINOExpected Learning Outcomes
The student will learn fundamental knowledge in process modeling , on the main identification and control techniques for processes.
Applied knowledge and understanding
The student will learn different identification methods, preprocessing problem and validation techniques for the proposed model. 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 the proper structure of the model to be identified and the validation technique 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 modeling and identification of 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 identification techniques, such as recursive and closed-loop identification methods.
Course Structure
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 program planned and outlined in the syllabus.
Required Prerequisites
Attendance of Lessons
Detailed Course Content
1. INTRODUCTION TO PROCESS MODELING AND IDENTIFICATION (Prof.ssa Gambuzza)
Introductory notions on modeling. Objectives and advantages of process identification.
2. LEAST SQUARE METHOD (Prof.ssa Gambuzza)
Linear regression and least square method
3. NONPARAMETRIC IDENTIFICATION (Prof.ssa Gambuzza)
Transient analysis of step and impulse response
4. IDENTIFICATION OF ARX, ARMAX MODELS (Prof.ssa Gambuzza)
Autoregressive processes (AR) and moving average (MA) with exogenus inputs (ARX e ARMAX)
5. PREPROCESSING DATA (Prof.ssa Gambuzza)
Disturbances in the data recorded, occasional bursts and outliers, missing data, non-continuous data records, drift and offset.
6. MODEL VALIDATION (Prof.ssa Gambuzza)
Once identified the model structure for the process we test whether this model is good enough, it is valid for its purpose.
7. IDENTIFICATION OF INDUSTRIAL PROCESSES (Prof. Buscarino)
Application of the identification techniques to industrial processes.
8. MODELING AND CONTROL OF PHYSICAL PROCESSES (Prof. Buscarino)
From the process to the model: data acquisition from a physical process, preprocessing, analysis, identification and control.
9. MODEL PREDICTIVE CONTROL (Prof.ssa Gambuzza)
Basic notions on model predictive control.
10. PROCESS MODELING AND CONTROL LAB (Prof. Buscarino)
Experimental activities oriented towards modeling and control of physical processes.
MATLAB EXERCISES (Prof.ssa Gambuzza, Prof. Buscarino)
MATLAB exercises for the topics covered by theory.
Textbook Information
[2] T. Södeström, P. Stoica: System identification, Prentice Hall, 1989
Course Planning
| Subjects | Text References | |
|---|---|---|
| 1 | Introduction to modeling | [1],[2] |
| 2 | Least square method | [1],[2] |
| 3 | Nonparametric identification | [1],[2] |
| 4 | Identification of ARX, ARMAX models | [1],[2] |
| 5 | Preprocessing data | [1],[2] |
| 6 | Model validation | [1],[2] |
| 7 | Identification of industrial processe | |
| 8 | Modeling and control of physical processes | |
| 9 | Laboratory activities |
Learning Assessment
Learning Assessment Procedures
Learning assessment may also be carried out online, should the conditions require it