A review of group contribution models to calculate thermodynamic properties of ionic liquids for process systems engineering

Abstract

Ionic liquids are compounds with interesting physical and chemical properties that can be applied to a wide variety of processes. The knowledge of their thermodynamic properties is essential for the design of products and processes. These properties include heat capacity, density, viscosity, thermal conductivity, melting point, surface tension, electrical conductivity, refractive index, thermal decomposition temperature, normal boiling point, critical properties, freezing point, isobaric expansivity, isothermal compressibility and static dielectric constant. However, the experimental database available of such properties for ionic liquids is limited, thus affecting the process design and modeling. Different thermodynamic models have been developed to estimate these properties and, group contribution models offer several advantages for these applications. This review covers different group contribution models reported and applied to estimate the thermodynamic properties of ionic liquids. The application, performance, and accuracy of these models for predicting the ionic liquids properties were analyzed and discussed. Some approaches combining group contribution models with artificial neural networks to estimate the thermodynamic properties of ionic liquids have been also described and exemplified. This review offers a survey of a variety of group contribution approaches that can be used to predict several thermodynamic properties of ionic liquids for process systems engineering.