Thermodynamic Modeling of Ionic Liquid Systems: Development and Detailed Overview of Novel Methodology Based on the PC-SAFT

We present the results of an extensive study on a novel approach of modeling ionic liquids (ILs) and their mixtures with molecular compounds, incorporating perturbed-chain statistical associating fluid theory (PC-SAFT). PC-SAFT was used to calculate the thermodynamic properties of different homologous series of ILs based on the bis(trifluormethylsulfonyl)imide anion ([NTf2]). First, pure fluid parameters were obtained for each IL by means of fitting the model predictions to experimental liquid densities over a broad range of temperature and pressure. The reliability and physical significance of the parameters as well as the employed molecular scheme were tested by calculation of density, vapor pressure, and other properties of pure ILs (e.g., critical properties, normal boiling point). Additionally, the surface tension of pure ILs was calculated by coupling the PC-SAFT equation of state with density gradient theory (DGT). All correlated/predicted results were compared with literature experimental or simulation data. Afterward, we attempted to model various thermodynamic properties of some binary systems composed of IL and organic solvent or water. The properties under study were the binary vapor-liquid, liquid-liquid, and solid-liquid equilibria and the excess enthalpies of mixing. To calculate cross-interaction energies we used the standard combining rules of Lorentz-Berthelot, Kleiner-Sadowski, and Wolbach-Sandler. It was shown that incorporation of temperature-dependent binary corrections was required to obtain much more accurate results than in the case of conventional predictions. Binary corrections were adjusted to infinite dilution activity coefficients of a particular solute in a given IL determined experimentally or predicted by means of the modified UNIFAC (Dortmund) group contribution method. We concluded that the latter method allows accurate and reliable calculations of bulk-phase properties in a totally predictive manner.