Model-Based Experimental Analysis (MEXA)
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Many not (sufficiently well) understood processes play a major role in chemical engineering processes. Such processes, as for instance multi-phase reactions, absorption, extraction and rectification, can thus not be quantified in a reasonably accurate manner. In order to achieve a more accurate prediction of the kinetics of such processes, a sound understanding of the underlying kinetic phenomena is necessary.
The Model-based Experimental Analysis (MEXA) methodology aims at achieving a detailed understanding of these kinetic phenomena and their interactions, represented in form of mechanistic mathematical models. Only based on such sound models, a rational product- and process design becomes feasible.
The common approach in research and application is to look at modeling and experimentation mostly independent of each other. Only at the end, a model is validated against a set of experimental data. In contrast, the MEXA methodology describes an iterative process of experimentation and modeling to systematically derive valid (mechanistic) models by performing targeted experimental investigations. This includes methods for the solution of inverse problems, for parameter estimation and identifiability analysis, for model discrimination and incremental refinement. Furthermore, an adequate treatment of uncertainties, e.g., inevitable measurement errors, is essential.
The formulation of a mechanistic process model typically leads to a system of (partial-) differential-algebraic equations. Such models are analyzed and solved with the help of commercial, customized and in-house developed software tools.
In the recent past, one focus of the MEXA group has been modeling of kinetic phenomena in multi-phase, reactive systems. Other topics include property modeling, biofuel design, in-line monitoring of microgel synthesis and modeling of electrochemical membrane reactors. The group also focuses on development and validation of innovative process analytics. The so-called mesoscale-view ensures that results, which have been generated on the small scale, can easily be transferred to the production scale.