Data Analysis for Electrolyte Systems: A Method Illustrated on Alkali Halides in Water

• Published in: Journal of chemical and engineering data Vol. 66; no. 8; pp. 2976 - 2990
• Main Authors: Vaque Aura, Santiago, Roa Pinto, Juan-Sebastian, Ferrando, Nicolas, de Hemptinne, Jean-Charles, ten Kate, Antoon, Kuitunen, Susanna, Diamantonis, Nikolaos, Gerlach, Thomas, Heilig, Manfred, Becker, Gaetan, Brehelin, Mathias
• Format: Journal Article
• Language: English
• Published: American Chemical Society 12-08-2021
• Subjects: Chemical Sciences; Activity coefficient; Solvents; Salts; Osmotic coefficient; Enthalpy
• ISSN: 0021-9568; 1520-5134
• DOI: 10.1021/acs.jced.1c00105

In the recent years, the need to improve the thermodynamic models, particularly making them more precise and predictable for complex systems, has increased [ Hendriks, E. Industrial Requirements for Thermodynamics and Transport Properties. Ind. Eng. Chem. Res. 2010, 49, 11131 11141 and Kontogeorgis, G. Industrial Requirements for Thermodynamic and Transport Properties. Ind. Eng. Chem. Res. 2021, 60, 4987 5013]. The complexity of electrolyte systems is due to the strong interactions between ions, the hydration forces that take place during salt dissociation, and the physical forces at high concentrations of solute. Consequently, the existing thermodynamic electrolyte models show some limitations and are far from being completely optimized for industrial simulations. Thermodynamics based on electrolyte mixtures and mixed solvents requires further development and research [ Poling, B. E. The Properties of Gases and Liquids, 5th ed.; McGraw-Hill Education, 2001].The EleTher Joint Industrial Project (JIP) aims at promoting research in this field. A practical workflow is under development where the first stage consists in analyzing the data. The present work is part of this larger scope and presents a strategy to analyze the internal and external consistency of experimental data for electrolyte mixtures. The focus is on alkali halide salts in water. Internal consistency allows testing the quality of data by confronting them to each other and using the thermodynamic relationships between these data. For that purpose, the data are confronted with a model that was initially optimized and the deviations are analyzed. For this purpose, the electrolyte non-random two-liquid (eNRTL) thermodynamic model was selected. Some deviations are attributed to the model inadequacy (high temperature or high molalities). Others are to be attributed to data inconsistencies. On the other hand, the objective of external consistency is to evaluate the consistency between different systems of the same family. The Bromley model is used for this purpose, as it contains a single adjustable parameter. Investigating the trend of this parameter with type of compound or with temperature may provide valuable information regarding the underlying physics, in addition to identifying outliers.