Module Description

Module: Industrial Process Automation

Courses:

TitleTypeHrs/WeekPeriod
Industrial Process AutomationLecture2Winter Semester
Industrial Process AutomationRecitation Section (small)2Winter Semester

Module Responsibility:

Prof. Alexander Schlaefer

Admission Requirements:

None

Recommended Previous Knowledge:

mathematics and optimization methods
principles of automata 
principles of algorithms and data structures
programming skills

Educational Objectives:

Professional Competence

Theoretical Knowledge

The students can evaluate and assess discrete event systems. They can evaluate properties of processes and explain methods for process analysis. The students can compare methods for process modelling and select an appropriate method for actual problems. They can discuss scheduling methods in the context of actual problems and give a detailed explanation of advantages and disadvantages of different programming methods. The students can relate process automation to methods from robotics and sensor systems as well as to recent topics like 'cyberphysical systems' and 'industry 4.0'.

Capabilities

The students are able to develop and model processes and evaluate them accordingly. This involves taking into account optimal scheduling, understanding algorithmic complexity, and implementation using PLCs.

Personal Competence

Social Competence

The students work in teams to solve problems.

Autonomy

The students can reflect their knowledge and document the results of their work. 

ECTS-Credit Points Module:

6 ECTS

Examination:

Written exam

Workload in Hours:

Independent Study Time: 124, Study Time in Lecture: 56


Course: Industrial Process Automation

Lecturer:

Alexander Schlaefer

Language:

English

Period:

Winter Semester

Content:

- foundations of problem solving and system modeling, discrete event systems
- properties of processes, modeling using automata and Petri-nets
- design considerations for processes (mutex, deadlock avoidance, liveness)
- optimal scheduling for processes
- optimal decisions when planning manufacturing systems, decisions under uncertainty
- software design and software architectures for automation, PLCs

Literature:

J. Lunze: „Automatisierungstechnik“, Oldenbourg Verlag, 2012
Reisig: Petrinetze: Modellierungstechnik, Analysemethoden, Fallstudien; Vieweg+Teubner 2010
Hrúz, Zhou: Modeling and Control of Discrete-event Dynamic Systems; Springer 2007
Li, Zhou: Deadlock Resolution in Automated Manufacturing Systems, Springer 2009
Pinedo: Planning and Scheduling in Manufacturing and Services, Springer 2009