Mathematical models offer a means of formulating and presenting knowledge that can be used to solve technical problems. This is called model-based process engineering.

This approach is applied to all chemical engineering processes, but in research it means the development of advanced process models and the solution of complicated problems. The field is characterized by the use of mathematical models, computational methods for simulation, optimization and statistical analysis, engineering programming, computational tools and simulation software.

Today, predictions of the performance of a process system are almost entirely made by simulations with mathematical models. The development of models with the ability to provide physical explanations, with sufficiently broad ranges of validity is central to many research projects. In order to be able to make reliable predictions, the parameters of the model must be calibrated against experimental data. Research is being carried out on both general methods for model adaptation and specific areas of application.

The evaluation of the robustness of a process step involves studying how various disturbances in operation affect the performance of that step and the quality of the product. This is an active area of research, and several studies are in progress in the fields of pharmaceuticals and special metals. Both deterministic worst-case scenario analysis and stochastic probability analysis are used.

The optimization of process conditions is a very active area of research. Optimization studies are carried out on both whole processes and single steps, often based on multiple object functions. Special attention has been devoted to the optimization of dynamic processes, especially in industrial chromatographic separation of proteins, but also in changes in production in chemical processes and power plants.