Carbaryl removal from aqueous solution by Lemna major biomass using response surface methodology and artificial neural network

Abstract In this present work, biosorption process of carbaryl from aqueous solution on Lemna major biomass was studied in a batch process. Both response surface methodology (RSM) and artificial neural network (ANN) model involving 29 experiments were applied to optimize and stimulate the adsorption process. The effects of operating parameters such as initial concentration, pH, biomass dose and contact time on the adsorption of carbaryl were analyzed through a three level four factor based on Box–Behnken design (BBD) using RSM. The proposed quadratic model showed good fit of the experimental data with coefficient of determination ( R 2 ) value of 0.992 and Fisher F -value of 132.33. Response surface plots were used to determine the interaction effects of main factors and optimum conditions of process. The optimum adsorption conditions were found to be initial concentration 34 ppm, pH 5.22, biomass dose of 0.86 g and contact time 12.52 min. ANN model developed from the same design provided reasonable predictive performance ( R 2  = 0.921) of carbaryl adsorption. Both the model was compared by the coefficient of determination ( R 2 ) and individual importance order of the operating parameters. Finally both the model fitted well to the experimental data. The rate of the biosorption process followed pseudo-second-order kinetics while equilibrium data well fitted to the Freundlich and Langmuir isotherm model. The maximum biosorption capacity of the biomass for carbaryl was found to be 6.21 mg g −1 .