Prediction of osmotic coefficients for ionic liquids in various solvents with artificial neural network

Abstract

The relationship between the structural descriptions and osmotic coefficients of binary mixtures containing sixteen different ionic liquids and seven kinds of solvents has been investigated by back propagation artificial neural network (BP ANN). The influence of temperature on the osmotic coefficients was considered and the concentrations of ionic liquids were close to 1 mol kg-1 , except in acetonitrile. Multi linear regression (MLR) was used to choose the variables for the artificial neural network (ANN) model. A three layer BP ANN with seven variables containing structural descriptions of the ionic liquids and the character of the solvent as input variables was developed. Compared with experimental data, the osmotic coefficients calculated using the ANN model had a high squared correlation coefficient (R 2) and a low root mean squared error (RMSE).

Optimization of chromatographic separation of aripiprazole and impurities: Quantitative structure-retention relationship approach

“…Furthermore, the model regression statistics and the correlation coefficient values indicated good fitting of the predicted model to experimental data (Table III). 29 Table III Considering that sB-A and sC-D could not be estimated by the means of QSRR-ANN model, they were evaluated using other ANNs. Although it is uncommon for ANNs to model small data sets, they still can be considered in circumstances where classical regression modelling tools are not applicable.…”