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

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