Toward Advanced, Predictive Mixing Rules in SAFT Equations of State

Abstract

A novel mixing rule that bridges the Statistical Associating Fluid Theory (SAFT)-type equations of state and activity coefficient models is proposed. Applying this mixing rule to the PC-SAFT equation of state is the focus of this article. In comparison to the original PC-SAFT equation, and even the underlying activity coefficient model, this mixing rule provides high-accuracy predictions of equilibrium properties for a range of mixtures, being able to predict phenomena that neither PC-SAFT or the underlying activity coefficient model would be able to predict on their own. A few limitations are identified including the case of cross-associating mixtures, due to the difficulty of separating associative interactions from dispersive interactions in activity coefficient models. Nevertheless, it is also shown that the new mixing rule is able to predict bulk properties very accurately, including volumetric properties which activity coefficient models alone are not able to predict. Given that one is able to use predictive activity coefficient models within this mixing rule, such as UNIFAC and COSMO-SAC, this new mixing rule opens the doors for the development of fully predictive SAFT equations of state for mixture systems.