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

•The partition coefficient of guanidine hydrochloride in ATPS is determined.•The study is conducted by experiment and simulation.•The PEG/salt ratio, guanidine hydrochloride concentration and pH were considered.•An optimum neural network is determined with a neuro-evolutionary technique.•The predictions closely matched the experimental data, with an error of 1.4%.

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.