Study Guide

Objective

This course is aiming at creating an overview of mathematical methods used in process automation. In this course these mathematical methods are considered from the practical process and automation design point of view. Modelling and simulation computing takes place both manually and using technical computing programs. The course includes a core content part of 40 %, and an additional part of 60 % concerning in-work learning and personal projects related to the core content topics. After completing the course, participants are capable to
• explain basic properties of analogue and digital data transfer
• define static, steady state describing, and dynamic, time-dependent process models
• perform process experiments, and based on them make empirical, continuous and discrete models
• use common software in computing dynamic models
• compute steady states of pipelines, tanks, and evaporator units, and make dynamic models and simulators using block programming.

Content

Which are analogue and binary coded signals, and A/D and D/A conversions?
What are process models for, and how do they look like in process automation?
How do we get process models based on first principle balance computing, and how do we use them for simulators?
How do we get dynamic, continuous Laplace models and discrete ARX models based on process experiments?
How do we use artificial intelligence and signal processing in process automation?