Course content

Establish dynamic models for physical-chemical-biological processes systematically. Time-scale based primary abstraction and construction of models based on the conservation principles, augmented by transport, state-variable transformation and geometry. Approximation of distributed models as finite-dimensional ordinary differential equations. Abstraction into network models. Model reduction based on time-scale considerations, systematic elimination of index problems. Analysis of linear and nonlinear dynamics, stability, observability and controllability. Matching the model to the process: system identification vs parameter identification, basic dynamic model identification. Nonlinear parameter identification and model-based design of experiments.

Learning outcome

The course provides an extensive introduction to process modeling aiming primarily at its use in process systems engineering.

Learning methods and activities

The course is given as a combination of lectures, exercises, self-study and projects. Appropriate software tools such as MATLAB will be used in the course. Portfolio assessment is the basis for the grade in the course. The portfolio includes a final written exam (80%) and exercises (20%). The results for the parts are given in %-scores, while the entire portfolio is assigned a letter grade. If there is a re-sit examination, the examination form may change from written to oral.

Compulsory assignments

• Exercises

Recommended previous knowledge

Chemical engineering basics, matrix algebra and an introduction to numerical methods, basic contril and frequency domain techniques..

Course materials

Katalin Hangos & Ian Cameron: Process Modelling and Model Analysis, Academic Press, 2001. Lecture notes and handouts and notes.

Credit reductions