Robert Kraus

Hierarchical Simulation of integrated Chemical Processes with a Web based Modeling Tool

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 modelers 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.

MOSAIC: An Online Modeling Platform Supporting Automatic Discretization of Partial Differential Equation Systems

Abstract Partial differential algebraic equation systems (PDAE) frequently appear in chemical engineering and their discretization is most often an issue when preparing a simulation wherein they appear. In this contribution, an algorithm is presented and implemented facilitating the general analysis of PDAE systems appearing often in chemical engineering problems and their discretization via orthogonal collocation on finite elements. The recognition of differentiating variables, the application of varying boundary conditions, and the subdivision into independent PDAE systems as well as the implementation of the actual discretization is discussed.

Generation of first and higher order derivative information out of the documentation level

Abstract The accurate and efficient evaluation of first and higher order derivative information of mathematical process models plays a major role in the field of Process Systems Engineering. Although it is well known that the chosen methods for derivative evaluation may have a major impact on solution efficiency, a detailed assessment of these methods is rarely made by the modeler. Since standard modeling tools and some solvers normally only support own default methods for derivative evaluation, the evaluation of further methods can be a tedious work, and thus requiring the connection of different tools. In this contribution the implementation of a general method for generation of derivative information out of the documentation level is presented. Exploiting the possibility of code generation given by the web-based modeling environment MOSAIC ( Kuntsche et al. 2011 ), derivative information of model equations is generated either by symbolic derivatives or by coupling the models with state of the art automatic differentiation (AD) tools. This offers the modeler different methods of getting exact derivative values and opens the possibility of integrating the assessment of derivative evaluations within the modeling and simulation workflow.