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

被引:19
|
作者
Pirdashti, Mohsen [1 ]
Movagharnejad, Kamyar [1 ]
Curteanu, Silvia [2 ]
Dragoi, Elena Niculina [2 ]
Rahimpour, Farshad [3 ]
机构
[1] Babol Univ Technol, Fac Chem Engn, Babol Sar, Iran
[2] Gheorghe Asachi Tech Univ, Fac Chem Engn & Environm Protect, Dept Chem Engn, Iasi 700050, Romania
[3] Razi Univ, Fac Engn, Dept Chem Engn, Biotechnol Res Lab, Kermanshah 6714967346, Iran
关键词
Aqueous two-phase system; Guanidine hydrochloride; Partition coefficient; Artificial neural network; Differential evolution algorithm; AQUEOUS 2-PHASE SYSTEMS; BOVINE SERUM-ALBUMIN; POLYETHYLENE-GLYCOL; CHAOTROPIC AGENTS; PURIFICATION; OPTIMIZATION; METHODOLOGY; RECOVERY; DEHYDROGENASE; EQUILIBRIA;
D O I
10.1016/j.jiec.2015.01.001
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
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.
引用
收藏
页码:268 / 275
页数:8
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