Developing supervised models for estimating methylene blue removal by silver nanoparticles

ABSTRACT Treatment of wastewater from synthetic constituents such as methylene blue (MB) which are extensively harmful to environment and human health is known as important issue in different industries. Also, implementing experiments to solve problems in engineering issues are highly time consuming and costly, so in the current work, the least square support vector machine (LSSVM) is coupled with two different evolutionary algorithms as particle swarm optimization (PSO) and genetic algorithm (GA) to predict removal MB on silver nanoparticles. The proposed algorithms have been evaluated with the experimental data graphically and statistically. The coefficients of determination for LSSVM–PSO and LSSVM–GA have been reported as 0.99999 and 0.99952, respectively. Also these methods have been compared with existing artificial neural network. The comparisons show that proposed methods have acceptable accuracy to predict MB extraction by silver nanoparticles.

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