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

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 15.0μgL−1 and <2.7%, respectively. The preconcentration factor was 83. The method was applied to preconcentration and determination of methylene blue from water samples.