A systematic model based methodology has been developed and its application highlighted through the solvent recovery section of a soybean oil extraction process, with emphasis on the effect of design variables on the process performance. First, the most representative compounds present in the vegetable oil were defined. Basic and critical properties were then computed by means of appropriate property prediction software. Temperature dependant properties were modeled using and extending available correlations. The process model was developed through the PRO II commercial simulator and validated by matching the steady state simulation results with available plant data. The validated process model was then used to optimize the performance of the process by manipulating a selected set of design variables. The optimization results indicated that the process was already within the optimum zone; however, improvements in the amount of the hexane recovered were possible.

The ability to predict the key physical and chemical properties of polymeric materials from their repeat-unit structure and chain-length architecture prior to synthesis is of great value for the design of polymer-based chemical products, with new functionalities and improved performance. Computer aided molecular design (CAMD) methods can expedite the design process by establishing input-output relations between the type and number of functional groups in a polymer repeat unit and the desired macroscopic properties. A multi-scale model-based approach that combines a CAMD technique based on group contribution plus models for predicting polymer repeat unit properties with atomistic simulations for providing first-principles arrangements of the repeat units and for predictions of physical properties of the chosen candidate polymer structures, has been developed and tested for design of polymers with desired properties. A case study is used to highlight the main features of this multi-scale model-based approach for the design of a polymer-based product.