Prediction of partition coefficients of guanidine hydrochloride in PEG-phosphate systems using neural networks developed with differential evolution algorithm

The complex problem of determining the partition coefficient of the guanidine hydrochloride in aqueous two-phase systems has been less studied. For this reason, an artificial neural network was developed to predict the partition coefficients of guanidine hydrochloride in poly (ethylene glycol) 4000/phosphate/guanidine hydrochloride/water system. The neural model (topology and internal structure) was determined using a neuro-evolutionary technique based on differential evolution algorithm, designed in different variants. This model was able to predict the guanidine hydrochloride concentrations in each phase with a mean relative error of 1.4%, which closely matched the experimental data. (C) 2015 The Korean Society of Industrial and Engineering Chemistry. Published by Elsevier B.V. All rights reserved.