Chemical Process Modeling in Modelica
Ali Baharev, Arnold Neumaier · Linköping electronic conference proceedings · 2012
Chemical process models are highly structured.Information on how the hierarchical components are connected helps to solve the model efficiently.Our ultimate goal is to develop structure-driven optimization methods for solving nonlinear programming problems (NLP).The structural information retrieved from the JModelica environment will play an important role in the development of our novel optimization methods.Foundations of a Modelica library for general-purpose chemical process modeling have been built.Multiple steady-states in ideal two-product distillation were computed as a proof of concept.The Modelica source code is available at the project homepage.The issues encountered during modeling may be valuable to the Modelica language designers.