Predicting the phase behavior of fatty acid methyl esters and their mixtures using the GC-SAFT-VR approach

Biodiesel fuels, which consist of a blend of long chain fatty acid methyl esters, have attracted increased interest in recent years as possible alternates to fuels derived from petroleum. An understanding of the thermophysical properties and phase behavior of these molecules are therefore important to the investigation and design of biodiesel processes; however, such data is limited, making the development of molecular based modeling approaches that can accurately and reliably determine the thermophysical properties of fatty acid methyl esters an important research area. Here we apply the group contribution based statistical associating fluid theory for potential of variable range (GC-SAFT-VR) equation of state to the study of long chain fatty acid methyl esters that comprise biodiesel fuels. With minimal reliance on experimental data, the GC-SAFT-VR equation of state offers a predictive approach that allows for the description of hetero-segmented chains that can correlate and predict the phase behavior of pure associating and non-associating fluids and their mixtures. Model parameters for the CO, CH3, CH2, CHCH2, OCH2, and OCH3 functional groups and their cross interactions were taken from earlier work and used here in a transferable fashion to predict the thermophysical properties and phase behavior of pure fatty acid methyl esters and their mixtures with other fatty acid methyl esters, alcohols, and carbon dioxide. It is shown that the GC-SAFT-VR approach can be used as a purely predictive tool without fitting any new model parameters) to accurately predict the phase behavior of the fatty acid methyl ester fluids and their mixtures studied.