Abstract
A novel approach for the systematic and hierarchical derivation of process models is presented. Model candidates for different unit phenomena are collected and rated on the basis of the model structure, origin and the modeler's belief. The process model is created as a superstructure with the competing partial models. Thereby, it is possible to determine the best possible combination through optimization with respect to different objective functions. The systematic procedure has been implemented into the online web modeling platform MOSAIC. Based on the superstructure, optimization code for the state-of-the art optimization and simulation software can automatically be created. Based on two case studies, the new approach is demonstrated, namely a process model for the hydroformylation of long-chain olefins and a model for the pressure drop in packed columns with foaming components.