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

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.

► Methylene blue has adverse effect on human health (breathing problem, eye burn).
► It is essential to remove MB dye from its aqueous solution.
► The nanoparticles have large specific area and internal diffusion resistance is absence.
► The nanoparticles have a higher efficiency for the removing of pollutant.
► The RSM and hybrid ANN-PSO was used to optimize the extraction percent of MB.