Estimation of viscosity of binary mixtures of ionic liquids and solvents using an artificial neural network based on the structure groups of the ionic liquid

A feed-forward neural network was constructed and tested to estimate the viscosities of binary mixtures of five different types of ionic liquids with various polar and non-polar solvents within a range of temperatures. The ionic liquids investigated had various anions and consisted of either the imidazolium, ammonium, pyridinium, pyrrolidinium, or isoquinolinium cations, with various cation alkyl side-chains. Together, a total of 1996 experimental data were collected from previously published literature and divided randomly into three different datasets: 1775 data points making up the training and validation datasets, and 221 data points selected as a test dataset. The molecular weight and group structure of the ionic liquid, the molecular weight and reduced boiling temperature of the solvent, and the molar composition, temperature, and pressure of the system were selected as the independent input variables. Results indicated that the network structure presented in this study is capable to estimate the viscosity of such nonideal binary mixtures, consisting of a range of ionic liquids and solvents, with an average relative error of 0.6%.