The new design and improvement of processes in chemical engineering nowadays is based most often on process simulations. Nearly always objectives for quality and costs have to be considered. Finding not only good, but the best compromises between these competing objectives is difficult, but essential for the decision process. Currently, in many practical planning processes, good compromises are found empirically, without the guarantee of optimality, and without any relation to other good, or even best compromises.
We develop methods to efficiently compute a whole set of best compromises, based on up to date simulations. Furthermore, we provide tools for decision support, which visualize the set of best compromises interactively for the engineer. In this manner, single solutions are shown in the context of the whole decision space, such that they can be judged objectively and according to the specific needs of the user.
Here is an overview of our latest projects of the cluster »Datenbasierte Prozess- und Produktionsplanung«: