Modeling the viscosity of ionic liquids using combined friction theory with perturbed hard-chain equation of state and neural network approaches

Abstract

Fast and accurate prediction and correlation of thermophysical properties are always important concerns for researchers and engineers. This work is the extension of our earlier friction theory (FT)-based approach for estimating the dynamic viscosities of pure ionic liquids using a perturbed hard-chain equation of state. The model used three molecular parameters along with 5 correlation coefficients in the friction terms. The correlation coefficients were obtained using 167 data points at 0.1 MPa with an average absolute deviation (AAD) of 0.61 % and then used to predict the viscosity values at higher pressures. Using the FT-based model, 2907 experimental viscosity data for 20 pure ILs were predicted. The calculations were carried out in 273–373 K range and pressures up to 298.9 MPa for 2907 data points with an AAD of 2.63 %. The present FT-based model was not able to accurately predict the viscosities of ILs outside the temperature range used for the fit procedure. Also, comparing the precision of our FT-based model with two rough-hard-sphere theory-based models of Hosseini et al. and Hossain-Teja as well as the group-contribution method led to AADs of 2.46 % (for 2664 data points), 2.65 % (for 1605 data points), 3.25 % (for 890 data points), and 7.73 % (for 2558 data points, respectively. Viscosities of several mixtures of IL + IL type were also correlated using the present model along with some simple combining equations and a binary interaction parameter as well. The AAD of the calculated viscosities from the experimental ones was found to be 5.10 % for 960 data points examined. In addition, a neural network was trained on 3074 data points to represent the viscosity of studied pure ILs. The artificial neural network (ANN) structure utilized two layers, four input parameters, and 13 neurons for each layer. This specific structure of ANN was carefully designed to ensure an optimal performance in predicting the viscosity of ILs. The overall AAD was obtained 1.44 %.