Process Automation

Production Planning in Practice 4 cr

Content: The course consists of project works with Arena, SAPr3 and APO

Assessment Methods and Criteria: Exercises and assignments.

 

Process Automation Project Work 5 cr

After completing the course, the student

• Understands the structure and requirements for plantwide automation systems;
• Is able to configure a small DCS system for lab unit processes using ABB 800xA system;
• Knows the basics in process automation programming languages;
• Understands the benefits, limitations and properties of industrial field buses and can apply this knowledge in the automation system design;
• Understands the meaning of process system interfaces (OPC, ODBC).

Content: Operation of plantwide distributed control system (DCS), PLC
programming languages (IEC 61131-3), structure and operation of
Profibus and Foundation Fieldbus field buses. Design of user
interfaces (HMI): events, alarms and trends. History data
collection from processes, reporting, software interfaces in
process automation (OPC, ODBC) and future development of field
buses (Ethernet, WLAN).
Basics in PLC programming, configuration and deployment of
traditional I/O and field buses.

Assessment Methods and Criteria: Lectures, project work and exam. Exam 50 % of the grade, project work 50 %.

 

Control Applications in Process Industries 6 cr

Content: The aim of the course is to give an overview of control strategies used in process industry. Classical and modern control theory is discussed briefly. Process dynamics, process modelling and identification, single-loop control and controller design, multivariable control, discrete time systems and design of digital controllers, model predictive control, selected topics in advanced process control and case studies.

Assessment Methods and Criteria: Lectures, exercises, homeworks and exam. A possibility for bonus points to the exam from the homeworks.

 

Process Monitoring Methods 5 cr

Content: The aim of the course is to give an overview on process monitoring and an introduction to neural and fuzzy process control methods. The main principles of the statistical and neural data-based process monitoring methods (principal component analysis, partial least squares regression, self-organizing maps) as well as their combinations and applications will be covered. An insight how neural and fuzzy methods can be used to improve control systems will be given. The course will combine conceptual frameworks with a practical approach.

Assessment Methods and Criteria: Lectures, exercises, homework and exam. A possibility for bonus points to the exam from homeworks.

 

Total 20 cr

NOTE! Students with A3 Process Automation as specialization field are recommended to take courses of Computer

 

Science in the C module:

Data Structures and Algorithms 5 cr

Learning Outcomes: Having completed the course, you can define, compare and implement basic data structures and algorithms as well as name and select them, for example, as dictionaries, sorting problem, and graph traversing. In addition, you can identify and describe given data structure or algorithm and give examples of its operation(s). Moreover, you can discuss other focal data structures and algorithms by means of the terminology typically used in this domain.

Content: Linear data structures, trees and graphs. Searching and sorting
methods. Principles of algorithm analysis.

 

Introduction of Software Engineering 5 cr

Learning Outcomes: You can present and motivate the phases of software engineering (Requirements Engineering, Software Architecture, Software Design and Implementation, Software Testing, Software Evolution) and the main cross-cutting activities of software engineering (Software Processes, Agile Software Development, Configuration Management). You are able to read and understand software engineering literature, and motivate the importance of software engineering.

Content: The course provides a broad, but practical view of major areas in software engineering. Supporting activities, are also discussed. The course consists of lectures and exercises.

Assessment Methods and Criteria: Exercises and possibly examination.

 

Algorithmic Methods of Data Mining 5 cr

Content: The course covers general topics in data mining, such as pattern discovery, clustering of data, and approximation of probability distributions. A special emphasis is put on algorithmic techniques to analyze discrete data.

Assessment Methods and Criteria: Examination and Exercise work

 

Communication Network 5 cr

Content: IP networks and routing. TCP and Internet congestion control. Some important Internet application layer services and protocols. Basics of network security.

Assessment Methods and Criteria: Compulsory: Examination 70 %, assignment(s) 30 %.