The goal of the research activities at the Institute of Process Systems Engineering is the development and optimization of individual processing steps as well as entire process flowsheets on the basis of a holistic view of available data, mathematical models derived from them and the algorithms used for analysis and optimization. By means of statistical analysis and the formulation and solution of specific optimization problems, model candidates are purposefully discriminated and parameterised with minimal experimental effort. Mechanistic and data-driven models are combined in a problem-specific fashion and developed with the help of machine learning methods. The developed models are used to derive problem formulations regarding the optimal reaction control, sizing of unit operations, as well as the optimal design of entire processes, with the objective to solve the problems as efficiently and reliably as possible.
The methodological work focuses on four application areas and their combination. All four areas deal with the objective of process intensification, while different means of pursuing this objective address different scales. While reactive and hybrid processes primarily serve process intensification on the process and equipment level, innovative equipment concepts generally pursue intensification on the equipment and transport level, which is achieved by structuring, miniaturization and the targeted improvement of transport phenomena. Especially in the field of biological processes, process intensification at the phase and molecular level also plays a special role, since the process performance depends heavily on the optimal integration and immobilization of the catalysts.
A more detailed description of the specific research topics can be found in the sections of the individual working groups