“Systematic Identification Method for Data Analysis and Phase Equilibria Modelling for Lipids Systems”
Abstract:
Industrial use of lipids has been increasing as a consequence of increased developments related to bio-based economies. In addition to applications in food-products, lipids are used by many industrial sectors, for example, biodiesel, edible oil, health, and personal care.
Phase equilibria predictions for chemical systems with lipids play a major role in process-product modeling, simulation and design. Due to the large number of lipid-compounds involved, predictive methods like group contribution based methods are particularly suitable for estimation of pure compound and mixture properties that may not be available.
Limited experimental data availability and poor performances of currently available group contribution based models is therefore an obstacle for obtaining the necessary information regarding phase equilibria of chemical systems with lipids. In this work, a systematic identification-regression method for phase equilibrium modelling is presented.
The aim of the method is to improve the quality of phase equilibrium prediction for the selected group contribution based models. By applying the identification method, a new set of binary interaction parameters regressed from vapor-liquid equilibrium data for chemical systems with lipids is presented for the Original UNIFAC model together with regression statistics and model performance.