Application of PSO-artificial neural network and response surface methodology for removal of methylene blue using silver nanoparticles from water samples

被引：66

作者：

Khajeh, Mostafa ^{[1
]}

Kaykhaii, Massoud ^{[2
]}

Sharafi, Arezoo ^{[2
]}

机构：

[1] Univ Zabol, Dept Chem, Zabol, Iran

[2] Univ Sistan & Baluchestan, Fac Sci, Dept Chem, Zahedan, Iran

来源：

JOURNAL OF INDUSTRIAL AND ENGINEERING CHEMISTRY | 2013年 / 19卷 / 05期

关键词：

Methylene blue; Silver nanoparticles; Artificial neural network; Particle swarm optimization; Response surface methodology; ADSORPTION; OPTIMIZATION; EQUILIBRIUM; EXTRACTION;

D O I：

10.1016/j.jiec.2013.01.033

中图分类号：

O6 [化学];

学科分类号：

0703 ;

摘要：

In this study, a simple and fast method for preconcentration and determination of trace amount of methylene blue (MB) from water samples was developed by silver nanoparticles based solid-phase extraction method and UV-Vis spectrophotometry. Response surface methodology and hybrid of artificial neural network- particle swarm optimization (ANN-PSO) have been used to develop predictive models for simulation and optimization of solid phase extraction method. Under the optimum conditions, the detection limit and relative standard deviation were 15.0 mu g L-1 and <2.7%, respectively. The preconcentration factor was 83. The method was applied to preconcentration and determination of methylene blue from water samples. (c) 2013 The Korean Society of Industrial and Engineering Chemistry. Published by Elsevier B.V. All rights reserved.

引用

收藏

页码：1624 / 1630

页数：7

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