Course content

This course gives a general background on problems, methods, and tools for process systems engineering.

Theme 1: Process modelling - the basis of computational engineering: Review of the systematic construction of steady state and dynamic models for physical-chemical-biological processes.

Theme 2: Basics of numerical solution algorithms for process models. Rootfinding and integration of ordinary differential equations. Differential-algebraic equation formulations and solution methods. Analysis of such process models.

Theme 3: Using state of the art tools in python to solve complex mathematical problems from Process Systems Engineering.

Theme 4: Introduction to optimization for parameter estimation. Model discrimination.

Theme 5: Data-driven models - linear static models and brief outlook on neural networks.

Learning outcome

At the end of the course, the students will know how to establish simple and large-scale process models, and how to perform analysis of these models.

The student will further have knowledge of the working principles of the most common numerical simulation algorithms, and be able to implement basic versions of them in a programming language (e.g. python).

The student will know how to use state-of-the-art python tools to solve important problems from Process Systems Engineering.

Finally, the student will be able to understand and apply tools for adjusting model parameters to experimental data, and have basic knowledge of data-driven models such linear static models and neural networks.

Learning methods and activities

Lectures and mandatory exercises.

Compulsory assignments

• Exercises

Recommended previous knowledge

Chemical engineering basics, matrix algebra.

Course materials

Lecture slides and handouts.

Credit reductions