Density, viscosity and refractive index prediction of binary and ternary mixtures systems of ionic liquid

Different artificial neural networks architectures were developed to predict the density, viscosity and refractive index of binary and ternary mixtures of ionic liquids using their individual properties. All neural network implemented were evaluated using the root mean square error (RMSE) and the average percentage deviations (APD) for the training and validation phase. The individual models implemented show great values of R 2 (greater than 9.995∙10-01) and low errors in terms of RMSE, that corresponds with an APD less than 7.766∙10-02 %, with the exception of the model for the prediction of viscosity where the error raised to 3.338 %. The results show that the different individual artificial neural networks implemented are a useful tool to predict the density, viscosity and refractive index of binary and ternary mixtures of ionic liquids and with reasonably