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

来源：

JOURNAL OF INDUSTRIAL AND ENGINEERING CHEMISTRY | 2015年 / 27卷

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

页码：268 / 275

页数：8

共 34 条

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