Prof. Dr.-Ing. Kai Sundmacher
Prof. Dr.-Ing. Kai Sundmacher
Phone: +49 391 6110 351
Fax: +49 391 6110 353
Room: N. 309
Links: Publications


Kristin Czyborra
Phone: +49 391 6110 350
Fax: +49 391 6110 353
Room: N 3.10
Susanne Wandenälis (CES Editorial Office)
Phone:+49 391 6110 317Fax:+49 391 6110 353

Process Systems Engineering

Process Systems Engineering

Technical systems of ever increasing complexity change our environment to a dramatic extent. Research on these systems is ultimately triggered by the key question: How can the Earth’s resources be better utilized in the future?


In the past decades, continuous progress in increasing the productivity, selectivity and sustainability of chemical and biotechnological production processes has been made. Neverthe­less, in order to cope with the challenges of the future, new breakthroughs in process systems engineering are necessa­ry in order to intensify existing processes drastically, to identify novel dream processes for synthesizing chemical substances and trans­forming energy, to push the transition from fossil to renewable resources, to close carbon dioxide cycles, to enhance conversion efficiencies significantly, and to incorporate new functionali­ties in materials and products.

For this purpose, new scientifically founded process engineering approaches need to be developed, able to deal with the inherent multi-level structure of production systems. Very efficient process systems can be designed if engineers succeed to consider all hierarchical levels involved in a process system simultaneously, i.e. from the molecular level up to the plant level. A multi-level design workflow must be fed with reliable thermodynamic and kinetic sub-models, validated and parametrized by use of experimental data harvested at different levels of the process hierarchy. Experimental data are an indispensable element required to discriminate between rivaling models, to identify models, and to quantify uncertainties of model predictions. Hence, only by closely combining mathematical process models and experimental data, an advanced quantitative understanding of complex process systems can be attained for opening new paths to translate scientific advancements into practical solutions.

Furthermore, natural living systems feature unique properties which have never been observed so far in the world of technical systems, e.g. the ability of cells for self-replication, the self-adaptivity of living cellular communities to large perturbations of environmental conditions, or the high specificity of many enzymes acting as catalysts in complex metabolic reaction networks. On the long run it would be desirable to mimic some of the functional principles of biological devices in order to understand how they work, and to create new “screw drivers” for the systems engineering toolboxes. This will become reality if process systems engineering principles can be successfully combined with synthetic biology approaches.


The vision statement given above forms the background for the PSE group’s research strategy. The group closely combines mathematical methods, novel design approaches and careful experimental validation techniques which are relevant to widespread interest. A core competency of the group is the understanding and modeling the dynamics of complex process systems as well as synthesizing process systems from functional units. In the last few years, we have continued to expand our competency in these directions by integrating new theoretical concepts and challenging process examples including chemical production systems, energy conversion systems, and biological production systems.

Our process design approach, the Elementary Process Functions Methodology (EPF), forms the umbrella for all research activities in the PSE group. In the last few years, it was further extended and developed into several directions. The most important recent achievements include: (1) integration of molecular decision variables, (2) inclusion of microkinetic reaction networks, (3) consideration of uncertain­ties of model parameters and process input variables, (4) optimal design and operation of multi­dimensional particulate processes, (5) integration of optimal reactor design with plant-wide optimization, (6) integration of multi-objective criteria in process optimization, (7) identification of optimal designs for chemical energy conversion systems, and (8) optimal operation and design of biological production systems.

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