Multi-objective optimization of PC-SAFT parameters for ionic liquids from density and viscosity data using entropy scaling

• Viscosity and PVT data used for PC-SAFT parameterization. • Multi-objective optimizations are performed for two cation series of ionic liquids. • Including viscosity data only leads to a small loss of accuracy in density. • Accurate viscosity models over the full range of available experimental data. • Similar or better performance in modeling phase equilibrium with CO 2 , CH 4 , and H 2 O. Equations of state using the Statistical Associating Fluid Theory (SAFT EoS) have found tremendous success in the thermodynamic modeling of ionic liquids (ILs) and mixtures. Traditionally, SAFT EoS parameters are fit to pure component pressure-volume-temperature (PVT) (density) data and vapor pressure data. We have recently combined the PC-SAFT EoS with entropy scaling theory to correlate and predict the viscosity of ILs and IL mixtures. We found that the PC-SAFT EoS parameters for ionic liquids regressed to PVT data can sometimes lead to relatively large deviations in the viscosity correlations, especially at high pressure. Here, we investigate the effect of including viscosity data along with PVT data for the PC-SAFT parameter regression of two series of 1-n-alkyl-3-methyl imidazolium ionic liquids ([C n MIm][Tf 2 N] and [C n MIm][BF 4 ]). From analyzing the Pareto fronts, the inclusion of viscosity data to PVT data for PC-SAFT parameters resulted in only a small loss in accuracy for the density, but with much improved viscosity correlations through entropy scaling. We found that the parameter sets obtained from density and viscosity data regression are less prone to numerical pitfalls, i.e. fictitious SAFT critical points, than the parameter sets obtained from PVT data alone. In addition, the predicted ( k ij =0) phase equilibrium (VLE) of ionic liquids and mixtures with CO 2 , CH 4 , and water were equal to, if not better than the predictions using PVT data alone. Overall, the use of pure PVT and viscosity data in the parameterization of PC-SAFT yields a more widely applicable prediction method for both thermodynamic and transport properties.