Application of PSO-artificial neural network and response surface methodology for removal of methylene blue using silver nanoparticles from water samples |
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Authors: | Mostafa Khajeh Massoud Kaykhaii Arezoo Sharafi |
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Affiliation: | 1. Department of Chemistry, University of Zabol, P.O. Box 98615-538, Zabol, Iran;2. Department of Chemistry, Faculty of Sciences, University of Sistan & Baluchestan, Zahedan, Iran |
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Abstract: | 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 μg 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. |
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