Optimized GC-PC-SAFT parameterization for high-accuracy thermodynamic prediction of diverse ionic liquids

Ionic liquids (ILs) are emerging solvents, and the reliable prediction of thermodynamic properties is challenging for process design because of their diverse structures. The integration of the perturbed-chain statistical associating fluid theory equation of state (PC-SAFT EoS) with the group contribution (GC) method establishes a robust framework for thermodynamic property prediction. However, challenges remain in modeling properties of ILs through GC-PC-SAFT EoS, primarily due to limited group universality and insufficient systematic parameterization. In this study, complex anions and cations are divided into multiple smaller sub-groups, and the group contribution parameters of the GC-PC-SAFT EoS were determined based on extensive experimental data and the global search algorithm. The optimized parameterization achieved the accurate calculation, with average absolute relative deviations of 1.2% for the density and 2.5% for isobaric heat capacity across 129 ILs over a wide temperature and pressure range. Furthermore, model validation confirmed its strong predictive capability for the density, isobaric heat capacity, and speed of sound of both pure ILs and their binary mixtures.