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.