34437 - AUTOMAZIONE DEI PROCESSI INDUSTRIALI M (L-Z)

Learning outcomes

The course aims to provide the conceptual, methodological, and practical bases that allow to analyze and design automatic control systems of industrial plants and business processes. At the end of the course, the student is able to model, analyze and setup a controller for dynamic systems with discrete events, in the area of industrial automation. Some significant cases of industrial processes and business process will be analyzed. Finally, optimization algorithms, optimal control, and Machine Learning algorithms and their application to industrial process automation are also presented.

 

Course contents

INTRODUCTION TO INDUSTRIAL AUTOMATION

• From the Industrial Revolution to Industry 4.0

• Classification of plants, processes, and control systems

• Types and main components of production lines

• Supervision, control, monitoring

• Production plants and their automation challenges

• Computer Integrated Manufacturing (CIM)

• Pyramid model of a CIM system: Field, Machine, Cell, Plant, Company

• Field control, procedure control, control of...

MODELING OF INDUSTRIAL PROCESSES

• Modeling of discrete event dynamic systems using Petri Nets

• Discrete event dynamic systems: definitions and properties

• Modeling discrete event systems with Petri Nets: places, transitions, flow relation between places and transitions, Petri graph, marking function

• Evolution of Dynamic Petri Nets: enabling and firing of transitions, incidence matrix, occurrence vector, reachability analysis, graphical analysis of Petri nets

• Petri Net simulators: WoPeD and PIPE 2.0 software

• Petri Net modeling of industrial production systems: physical and functional approaches

MODELING OF BUSINESS PROCESSES

• Workflow and production processes

• Flow of materials and information

• Resource control and management

• Examples of modeling, simulation, and control of production processes

• • Producer/consumer system model
• • Client/server system model with unit capacity or unlimited request bufferodel of a production process with 3 warehouses, three robots, a conveyor belt, two machine tools

• Implementation and simulation in WoPeD and PIPE 2.0 of the systems described above

ANALYSIS AND CONTROL OF PETRI NETS AND APPLICATION TO INDUSTRIAL PROCESS CONTROL

• Analysis of industrial processes described with Petri Nets: liveness, boundedness, reversibility, reachability and coverability tree and graph, reduction techniques, P-invariants, T-invariants, siphons, traps, deadlocks

• Control and supervision of an industrial process using Petri Nets: control through invariants

• Action planner based on Petri Nets

• Examples of control using Petri Nets: automated guided vehicle (AGV) system

IMPLEMENTATION ON PLC

• Introduction to the translation of Petri Nets into Grafcet code

OPTIMIZATION AND CONTROL METHODS

• Optimization and optimization algorithms

• Linear Quadratic (LQ) optimal control

• Model Predictive Control (MPC)

MACHINE LEARNING FOR INDUSTRIAL AUTOMATION

• Machine Learning for fault diagnosis and predictive maintenance of industrial plants and machinery

• Fundamentals of Machine Learning: main algorithms such as Support Vector Machine, Random Forest

• Application of Machine Learning to Industrial Automation

• Condition Monitoring (CM) and Predictive Maintenance (PM) within Smart Factory frameworks

• Brief overview of Matlab/Simulink Predictive Maintenance package

GENERATIVE AI TOOLS

• "Prompt Engineering" Applied to Industrial Automation

Readings/Bibliography

• Official course documentation supplied by the instructor

• Gerasimos Rigatos, State-Space Approaches for Modelling and Control in Financial Engineering (Systems theory and machine learning methods), Editore:Springer, 2017

• KLS Sharma, Overview of Industrial Process Automation, second edition. Elsevier LtD, 2017

• John O. Moody, Panos J. Antsaklis, Supervisory Control of Discrete Event Systems using Petri Nets, Editore: Kluwer Academic Publishers, ISBN: 0-7923-8199-8

• C. Bonivento, L. Gentili, A. Paoli, Sistemi di automazione industriale, Editore: McGraw-Hill, Anno edizione: 2011, ISBN: 88-386-6693-3

• P. Chiacchio, F. Basile, Tecnologie informatiche per l'automazione, Editore: McGraw-Hill, Anno edizione: 2004, ISBN: 88-386-6147-2
  

• Luca Ferrarini, Automazione Industriale: Controllo Logico con Reti di Petri, Editore: Pitagora Editrice, Anno edizione: 2001, ISBN: 88-371-1296-3

• Luca Ferrarini, Luigi Piroddi, Esercizi di Controllo Logico con Reti di Petri, Editore: Pitagora Editrice, Anno edizione: 2002, ISBN: 88-371-1340-4

• Pedro Larrañaga et al, Industrial Applications of Machine Learning. Editore: Chapman & Hall/CRC Data Mining and Knowledge Series

Teaching methods

In-person lectures.
Matlab/Simulink software
Excel
Generative AI tools applied to teaching

Assessment methods

COMPLETE WRITTEN EXAM: compulsory exercises and theory questions
OPTIONAL ORAL EXAM upon the student’s request
Please refer to the course page on virtuale.unibo.it for a detailed description of the above-mentioned assessment methods.

PROJECT: as an alternative to the written exam, students may complete an optional project report on a topic agreed upon with the student.

 

IMPORTANT NOTE FOR STUDENTS WITH SPECIFIC LEARNING DISABILITIES OR OTHER DISABILITIES

 

Students with specific learning disabilities or temporary or permanent disabilities are advised to contact the relevant University office well in advance (https://site.unibo.it/studenti-con-disabilita-e-dsa/en ): the office will propose any appropriate accommodations to the students concerned. These accommodations must in any case be submitted to the lecturer for approval at least 15 days in advance. The lecturer will assess their suitability, also in relation to the learning objectives of the course.

IMPORTANT NOTE 2

 

Examination in a controlled environment with prohibition of AI use

 

The use of AI tools during the examination is prohibited in any form, including chatbots, text generators, advanced translation tools, content summarisation tools, and tools integrated into electronic devices.

Any use of AI is considered a violation of the principles of honesty and fairness set out in the Policy and may result in the examination being invalidated by the lecturer, who remains responsible for assessing the validity of the examination.

This examination format applies to assessments intended to evaluate individual skills that cannot be delegated to AI, such as:
• critical understanding;
• logical argumentation skills;
• independent problem-solving;
• application of fundamental knowledge;
• personal reconstruction of procedures, methodological steps, or concepts.

As a preventive measure against possible misconduct, the following is required:

• electronic devices must be handed in or placed in a controlled airplane/offline mode;
• only materials provided by the lecturer may be used (examination sheets and examination text/formula sheet); no notes or books are permitted.

AI can be a useful tool to support individual study through further exploration, summaries, and self-assessment activities. However, with regard to the assessment of learning, the use of AI during the in-person examination is prohibited. Any use constitutes a violation of academic integrity.

Teaching tools

Computer in aula, eventuale laboratorio didattico, lavagna, proiettore, strumenti di AI

Office hours

See the website of Paolo Castaldi