An ion-specific electrolyte non-random two-liquid segment activity coefficient model with improved predictive capabilities for aqueous electrolyte solutions

Abstract

The accurate prediction of activity coefficients of electrolyte solutions is of great importance for many engineering applications. The electrolyte non-random two-liquid (eNRTL) model is a commonly used semi-empirical activity coefficient model for electrolyte solutions. This work presents a modified version of the eNRTL model, which aims to extend its predictive capabilities. An ion-specific parameterization scheme is developed to replace the conventionally used salt-specific parameterization scheme. Consequently, this new method allows for the prediction of the properties of electrolytes consisting of ions for which optimal ion-specific parameter values have been determined, which is inherently not possible when using salt-specific parameters. In order to capture the features of solvent-ion interactions, a segment-based local composition term is used and experimental activity data of both the salt and the solvent are utilized for parameter fitting. The modified eNRTL model with optimal ion-specific parameters is applied to aqueous electrolyte solutions and shows satisfying prediction performance.