Course content

Theme 1: Process modelling - the basis of computational engineering: Review on the systematic construction of dynamic models for physical-chemical-biological processes. Extension into large-scale models and their abstraction into network models and the application of time-scale assumptions leading to cruder process descriptions on a longer time scale. Theme 2: Analysis of linear and nonlinear dynamics: Focus on stability with possible excursions into observability and controllability. Theme 3: Matching the model to the process: Focus on multi-dimensional regression and variance analysis of linear-in-parameter models. Extension to system identification of basic dynamic models. Nonlinear parameter identification and model-based design of experiments. Theme 4: Hybride systems: Discrete-event dynamic systems and their interaction with continuous processes aiming at supervisory control, planning and logistic issues.

Learning outcome

At the end of the course the students should know how to: - Establish large-scale process models on different time scales. - Perform a model property analysis. - Use experimental data for the identification of process parameters. - Gain insight into the functioning and properties of hybrid systems.

Learning methods and activities

Lectures and exercises, small projects. Oral examination. Result is given as a letter grade.

Compulsory assignments

• Exercises

Recommended previous knowledge

Chemical engineering basics, matrix algebra and an introduction to numerical methods and optimisation.

Course materials

The relative emphasis of the themes is adjusted to the group of participants at the beginning of the course. Lecture script plus distributed material.

Credit reductions