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Mohsen Pirdashti,Kamyar Movagharnejad,Abbas Ali Rostami,Behnia Shahrokhi 한국화학공학회 2017 Korean Journal of Chemical Engineering Vol.34 No.7
The current study employed response surface methodology (RSM) with a face-centered central composite design (CCD) to indicate the essential variables on the partition coefficient of guanidine hydrochloride (GuHCl) in the poly (ethylene glycol) (PEG)-phosphate aqueous two-phase system (ATPS). To evaluate the partition coefficients of GuHCl in the mentioned ATPS, the pH (7.0, 8.5 and 10.0), GuHCl concentration (1.0, 3.5 and 6.0% w/w), PEG molecular weight (2,000, 4,000 and 6,000 gmol−1) and PEG/potassium phosphate concentrations ratio were selected as independent variables. A quadratic model is suggested to find the impact of these variables. The suggested model has a strong harmony with the experimental data. The results of the model display that the GuHCl concentration and weight percent of the salt in feed have a large and small influence on the GuHCl partitioning.
Mohsen Pirdashti,Silvia Curteanu,Kamyar Movagharnejad,Elena Niculina Dragoi,Farshad Rahimpour 한국공업화학회 2015 Journal of Industrial and Engineering Chemistry Vol.27 No.-
The complex problem of determining the partition coefficient of the guanidine hydrochloride in aqueoustwo-phase systems has been less studied. For this reason, an artificial neural network was developed topredict the partition coefficients of guanidine hydrochloride in poly (ethylene glycol) 4000/phosphate/guanidine hydrochloride/water system. The neural model (topology and internal structure) wasdetermined using a neuro-evolutionary technique based on differential evolution algorithm, designed indifferent variants. This model was able to predict the guanidine hydrochloride concentrations in eachphase with a mean relative error of 1.4%, which closely matched the experimental data.