A New Group Contribution Method For Prediction of Density of Pure Ionic Liquids over a Wide Range of Temperature and Pressure

Abstract

A detailed knowledge of reliable data on physical properties of ionic liquids (ILs) is of great importance, because ILs are still considered as potential replacements for volatile organic solvents in modern and sustainable (“greener”) processes of chemical industry. In particular, liquid density is a very important property that is required in many design problems of chemical engineering and material science. Therefore, development of new methods for estimation of density of ILs is essential. In this work we propose a new method based on generalized empirical correlation and group contributions. It was developed based on a comprehensive database of experimental data containing over 18500 data points for a great variety of 1028 ILs. The collected data covers temperature and pressure ranges of 253–473 K and 0.1–300 MPa, respectively. Molar volume at reference temperature (298.15 K) and pressure (0.1 MPa) was assumed to be additive with respect to defined set of both cationic and anionic functional groups, whereas a Tait-type equation with four adjustable parameters was adopted to describe temperature–pressure dependence of density (P–ρ–T). The model parameters, including contributions to molar volume for 177 functional groups, as well as universal coefficients describing the P–ρ–T surface, were fitted to experimental data for 828 ILs with an average absolute relative deviation (%AARD) of 0.53%. Then, the model was evaluated by a calculation of density for 200 ILs not included in the correlation set. We showed that the proposed GCM allows the accurate prediction of high pressure densities for a variety of ILs. The resulting %AARD of prediction was 0.45% which is the one of the lowest values compared with similar correlations reported in literature. Moreover, we showed that the presented method is able to accurately capture other volumetric properties of pure ILs such as molar volume and derivative properties (thermal expansion coefficient and isothermal compressibility) as well as their temperature and pressure dependencies.

- Accurate Force Fields for Molecular Simulation

“….6 (Abildskov, Ellegaard, and O'Connell 2010a). The agreement is normally better than comparable methods (Paduszynski and Domanska 2012). Figure 9.2 shows that the measurements by different workers (Gardas et al 2007;Jacquemin et al 2007) are different for both the reference densities and compressibilities, since the low pressure intercepts and the slopes of the lines are not the same.…”

Predicting Density and Refractive Index of Ionic Liquids

“…For that, the same group structure atached to diferent substitutes may have diferent group values. Paduszyński and Domańnska [7] proposed a simple and generalized correlation to estimate the density of ILs as a function of temperature and pressure. They divided the ILs in 177 functional groups which are classiied in three subgroups: cation cores, anion cores and substituents.…”

“…On the one hand, density of ILs is related to the mechanics and engineering components of a process and is usually used to determine parameters like rates of liquid-liquid phase separation, mass transfer, power requirements of mixing and pumping and equipment sizing [6]. In fact, density of ILs is necessary to solve material or energy balance equations of chemical processes in industry [7]. On the other hand, refractive index of ILs is more linked to speciic chemical properties like the structuredness, polarity and relative hydrogen bonding donating and accepting ability which help to determine solubilities, partition constants and reaction rates [8].…”

“…Therefore, developing reliable predictive methods and correlations to estimate the density of ILs in a wide range of temperature is essential. Furthermore, this kind of method allows a beter understanding of the inluence of the structure of the ILs on the density and on other physicochemical properties [7].…”

“…We have compiled in this chapter published studies concerning methods to predict or estimate density of ILs according to the classiication established by Paduszyński and Domańska [7]. "riely, some authors have used methods based on quantitative structure-property relationships (QSPRs) [11ª15] and on artiicial neural networks ("NNs) [16ª18].…”