Density Prediction of Mixtures of Ionic Liquids and Molecular Solvents Using Two New Generalized Models

Engineers
often demand generalized models without sophisticated and long-time
computations. To date, such models are still lacking for the density
prediction of ionic liquid (IL) mixtures. In this paper, corresponding
states principle combining with new mixing rules is employed to develop
two new generalized models for density prediction of IL mixtures,
including an extended Riedel (ER) model and an artificial neural network
(ANN) model. A total of 1985 data points of binary and ternary mixtures
of IL with molecular solvents, such as water, alcohols, ketones, ethers,
hydrocarbons, esters, and acetonitrile, are used to verify the models.
Average absolute relative deviations of the ER model and the ANN model
are 0.92% and 0.37%, respectively, which indicates both the developed
models can achieve a universal and accurate density prediction of
IL mixtures. Moreover, the ER model does not contain any fitted parameters
and thus provides a real predictive method.