Prediction of partition coefficients of alkaloids in ionic liquids based aqueous biphasic systems using hybrid group method of data handling (GMDH) neural network

In this study, a hybrid GMDH-neural network model was developed in order to predict partition coefficients of alkaloids in aqueous biphasic system using different ionic liquids with the same inorganic salt. In order to accomplish this modeling, feed's weight percent compositions along with slope of tie-line (STL), tie-line length (TLL) and difference of molecular weight of salt and Ionic liquid were taken as the inputs and the desired partition coefficients (K) were estimated. Furthermore, the data set was divided into two parts: 80% of the data points were used for training and 20% for testing. For evaluation of the model's performance, partition coefficients obtained from the GMDH model were compared with their experimental values using different statistical measures. The proposed model can successively correlate and predict K-values and result a great agreement with the experimental data.