Process Systems Engineering
Technical systems of ever-increasing complexity are changing our environment to a dramatic extent. Research on these systems is ultimately triggered by the key question: How can Earth’s resources be utilized in a sustainable manner?
In recent decades, we have seen continuous progress in increasing the productivity and selectivity of chemical, energy and biotechnological conversion processes. Nevertheless, in order to cope with the grand challenges of the future, new breakthroughs in Process Systems Engineering are necessary in order to achieve drastic performance improvements to existing processes, to invent dream process technologies for synthesizing chemicals and transforming energy to the highest level of efficiency, to accelerate the transition from fossil to renewable raw materials, to organize a comprehensive circular economy for as many material streams as possible, and to achieve higher product quality and functionality objectives.
For this purpose, scientifically founded process systems engineering frameworks need to be developed to be able to deal with the inherent multi-level structure of chemical production systems. Highly efficient process systems can be designed if engineers succeed in considering each hierarchical level involved in a production system simultaneously, i.e. from the molecular level up to the process network level. A multi-level design workflow must be fed with reliable quantitative thermodynamic and kinetic models, validated and parameterized by use of experimental data harvested at different levels of the process hierarchy. Experimental data are indispensable for discriminating between rival models, for identifying model parameters, and achieving uncertainty quantification of model predictions. Hence, only by closely combining mathematical process modeling and systematic harvesting of experimental data can an advanced quantitative understanding of complex process systems be accomplished in order to open up new pathways for translating scientific advancements into practical solutions.
Experimental data are indispensable for discriminating between rival models, for identifying model parameters, and achieving uncertainty quantification of model predictions.Furthermore, natural living systems feature unique properties which have so far never been observed in the world of technical systems, for example the ability of cells to self-replicate, the self-adaptivity of living cellular communities to large perturbations of environmental conditions, or the high specificity of many enzymes acting as catalysts in metabolic reaction networks. In the long run it would be fantastic to mimic the functional principles of biological systems in order to understand how they work, and to create new “screw-drivers” for the toolboxes of systems engineering. This might become reality if process systems engineering principles could be successfully combined with synthetic biology approaches.
The above vision statement forms the background to the PSE group’s research strategy. The group closely combines mathematical methods, novel design approaches and careful experimental validation techniques which are relevant to a wide range of interests. The core competencies of the group are 1) understanding and modeling the dynamics of complex process systems and 2) synthesizing optimal process systems from functional modules. In recent years, we have continued to expand our competency in these directions by integrating new theoretical concepts and challenging process examples from the areas of chemical production, energy conversion, and biotechnology.
Our hierarchical process design approach, the Elementary Process Functions (EPF) methodology, constitutes 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 uncertaintiesof model parameters and process input variables, (4) optimal design and operation of multidimensional particulate processes, (5) integration of optimal reactor designwith plant-wide optimization, (6) integration of multi-objective criteriain process optimization, (7) identification of optimal designs for chemical energy conversion systems, and (8) optimal operation and design of biological production systems.