# SCO-20306 Modelling Dynamic Systems

## Course

Credits 6.00

 Teaching method Contact hours Lectures 16 Practical extensively supervised 40 Practical intensively supervised 40
 Course coordinator(s) dr. ir. LG van Willigenburg Lecturer(s) prof. dr. ir. G van Straten dr. ir. LG van Willigenburg dr. RJC van Ooteghem Examiner(s) prof. dr. ir. G van Straten dr. ir. LG van Willigenburg

English

### Assumed knowledge on:

Introduction to Process Engineering for Technologists; Mathematics T

### Continuation courses:

SCO-21306 Control Engineering and Process Control; SCO-31306 Systems and Control Theory; SCO-31806 Parameter Estimation and Model Structure Identification

### Contents:

What are the most important means of engineers for system design, such as the design of a bioreactor or a climate control system? What are the most important means of scientists to answer research or questions such as how bacteria influence the growth rate in a bioreactor or new animal grazing and vegetation are related? Answer: mathematical models and measurements to identify and verify the properties of these models.
From a rough non-mathematical system description, such as a sketch, diagram or drawing, this course teaches you how to construct a mathematical model using scientific knowledge and measurement data.
Furthermore you will learn how to analyse the dynamic behaviour of the modelled system by mathematical analysis. Reality is hardly ever completely described by a mathematical model. The resistance a weed control device experiences when moving through the ground is not known precisely. Not all chemicals or their concentrations, present in a stirred tank or a bioreactor, are known perfectly. In the case of climate control several coefficients which determine heat transport phenomena such as the heat transport through the walls are not known perfectly. Therefore measurement data are useful since they provide additional information regarding these uncertainties. They can therefore be used to improve or verify the mathematical model. One third of the course is devoted tot estimation of the parameters from observational data.

### Learning outcomes:

Students are expected to:
- be able to translate a rough non-mathematical description of a system into a mathematical systems model using scientific knowledge and measurement data;
- be able to put mathematical systems models into the state-space form;
- know and recognise the different types of variables of a systems model written in state-space form. - know and be able to determine system properties such as stability and equilibria from the mathematical systems model in state-space form;
- know the basic properties of elementary linear systems and the representation of linear systems through transfer functions;
- know and be able to apply the least squares method to estimate unknown parameters of a mathematical systems model in state-space form.

### Activities:

Lectures, tutorials, computer exercises, experiments, self-education.

### Examination:

Based on a final written exam and the judgement of results obtained with the computer exercises and the practical experiment.

Programme Phase Specialization Period Compulsory for: BAT Biosystems Engineering BSc 2MO MBT Biotechnology MSc E: Environmental Biotechnology 2MO BBT Biotechnology BSc 2MO MBT Biotechnology MSc D: Process Technology 2MO MFT Food Technology MSc H: Sustainable Food Processing 2MO