New models for predicting thermophysical properties of ionic liquid mixtures

Abstract

Potential applications of ILs require the knowledge of the physicochemical properties of ionic liquid (IL) mixtures. In this work, a series of semi-empirical models were developed to predict the density, surface tension, heat capacity and thermal conductivity of IL mixtures. Each semi-empirical model only contains one new characteristic parameter, which can be determined using one experimental data point. In addition, as another effective tool, artificial neural network (ANN) models were also established. The two kinds of models were verified by a total of 2304 experimental data points for binary mixtures of ILs and molecular compounds. The overall average absolute deviations (AARDs) of both the semi-empirical and ANN models are less than 2%. Compared to previously reported models, these new semi-empirical models require fewer adjustable parameters and can be applied in a wider range of applications.

Effect of salts on the solubility of ionic liquids in water: experimental and electrolyte Perturbed-Chain Statistical Associating Fluid Theory

“…Thus, EoS combine the advantages of requiring a very low number of binary parameters with the use of a small number of ready available (or easily estimable) pure-component parameters 33. This opens the door for developing predictive models, which are becoming the preferred approaches in different research fields at both academia and industry 34. To the best of our knowledge, EoS have not been previously used for modelling complex water + salt + IL systems.…”

The physicochemical properties of a room-temperature liquidus binary ionic liquid mixture of [HNMP][CH3SO3]/[Bmim]Cl and its application for fructose conversion to 5-hydroxymethylfurfural

“…Insight into the relationship between the composition of DSIL and its physicochemical property has been presented for several binary IL mixtures. [11][12][13] As far as the melting point is concerned, the variation of the abundance of each ion could result in an IL mixture with the melting point or glass-transition temperature being much lower than its component IL.…”

Modelo simple y generalizado para estimar la conductividad térmica de líquidos iónicos

“…El modelo se extiende a mezclas binarias utilizando el modelo propuesto por Huang et al (2015), en particular para mezclas formadas por (1)agua+(2)líquido iónico, tal como se muestra en la tabla 5 y figura 4. En total, tres mezclas binarias son analizadas en este trabajo, representando 21 datos experimentales recolectados por la NIST-LIs.…”

“…El modelo generalizado de conductividad térmica es extendido a mezclas binarias utilizando la regla de mezcla simple propuesta por Huang et al (2015) y la expresión matemática se ilustra en las ecuaciones 5 y 6.…”

“…(5) (6) Donde λ m es la conductividad térmica de la mezcla, x 1 y x 2 son las fracciones molares de cada componente puro y π es un parámetro de interacción binaria que depende de cada mezcla y debe ser estimado utilizando datos experimentales. Es importante anotar que la ecuación 5 se compone de dos términos que representan lo ideal y no ideal (Huang et al (2015)).…”