화학공학소재연구정보센터
홈 로그인 로그아웃 연락처 사이트맵
Journal of Industrial and Engineering Chemistry, Vol.27, 268-275, July, 2015
DOI10.1016/j.jiec.2015.01.001  Export Citation
Prediction of partition coefficients of guanidine hydrochloride in PEG-phosphate systems using neural networks developed with differential evolution algorithm
Mohsen Pirdashti, Kamyar Movagharnejad, Silvia Curteanu1, †, Elena Niculina Dragoi1, and Farshad Rahimpour2
  • Faculty of Chemical Engineering, Babol University of Technology, PO Box 484, Babol, Iran
  • 1Department of Chemical Engineering, Faculty of Chemical Engineering and Environmental Protection, Gheorghe Asachi Technical University, Str. Prof. dr. Doc. Dimitrie Mangeron, No. 73, 700050 Iasi, Romania
  • 2Biotechnology Research Lab, Chemical Engineering Department, Faculty of Engineering, Razi University, Kermanshah 67149-67346, Iran
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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.
Keywords:Aqueous two-phase system;Guanidine hydrochloride;Partition coefficient;Artificial neural network;Differential evolution algorithm
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