Generalized Quasi-Random Lattice model for electrolyte solutions: Mean activity and osmotic coefficients, apparent and partial molal volumes and enthalpies

• Published in: Fluid phase equilibria Vol. 479; pp. 69 - 84
• Main Author: Moggia, Elsa
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 15.01.2019
• Subjects: Mean activity coefficient model; Pseudolattice theory; Thermodynamic data prediction; Pseudolattice theory; Mean activity coefficient model; Thermodynamic data prediction
• ISSN: 0378-3812; 1879-0224
• DOI: 10.1016/j.fluid.2018.09.008

Electrolytes are the subject of a vast number of theoretical and experimental investigations concerned with a variety of modern applications. The modeling of thermodynamic properties of ionic solutions is thus a fundamental research topic that has been supported in many ways for many decades. There is however still a lack of models that are truly predictive over wide ranges of concentration, temperature and pressure conditions. In this article, the Quasi-Random Lattice (QRL) model is presented in a generalized form that allows for evaluating relevant thermodynamic properties of binary electrolyte solutions. The semi-predictive character of the model yields a powerful and competitive representation of electrolyte data over well defined concentration ranges. The additional experimental information to support the generalized version of QRL is very modest compared to the number of data points typically used in regression techniques for best-fit purposes. The thermodynamic consistency of the improved QRL model is demonstrated by the level of agreement with experimental data concerning mean activity and osmotic coefficients, apparent and partial molal volumes and enthalpies, for a variety of aqueous 1:1, 2:1, and 3:1 electrolytes. [Display omitted] •The article evaluates the most relevant thermodynamic properties of ionic solutions.•The article is built on a very competitive model for the mean activity coefficient.•Regression techniques are not required by the model.•The model parameterization requires a small number of experimental data.•The article advances toward a truly predictive model of the electrolytic behavior.