Modeling of adsorption of Methylene Blue dye on Ho-CaWO4 nanoparticles using Response Surface Methodology (RSM) and Artificial Neural Network (ANN) techniques

The aim of this study is to evaluate the applicability of Ho-CaWO4 nanoparticles prepared using the hydrothermal method for the removal of Methylene Blue (MB) from aqueous solution using adsorption process. The effects of contact time, Ho-CaWO4 nanoparticles dose and initial MB concentration on the removal of MB were studied using the central composite design (CCD) method. Response Surface Methodology (RSM) and Artificial Neural Network (ANN) modeling techniques were applied to model the process and their performance and predictive capabilities of the response (removal efficiency) was also examined. The adsorption process was optimized using the RSM and the optimum conditions were determined. The process was also modelled using the adsorption isotherm and kinetic models. The ANN and RSM model showed adequate prediction of the response, with absolute average deviation (AAD) of 0.001 and 0.320 and root mean squared error (RMSE) of 0.119 and 0.993, respectively. The RSM model was found to be more acceptable since it has the lowest RMSE and AAD compared to the ANN model. Optimum MB removal of 71.17% was obtained at pH of 2.03, contact time of 15.16 min, Ho-CaWO4 nanoparticles dose of 1.91 g/L, and MB concentration of 100.65 mg/L. Maximum adsorption capacity (q(m)) of 103.09 mg/g was obtained. The experimental data of MB adsorption on Ho-CaWO4 nanoparticles followed the Freundlich isotherm and pseudo-second-order kinetic models than the other models. It could be concluded that the prepared Ho-CaWO4 nanoparticles can be used efficiently for the removal of MB and also, the process can be optimized to maximize the removal of MB. Synthesis and characterization of Ho-CaWO4 nanoparticles. Modelling and optimization of Methylene Blue removal onto Ho-CaWO4 using Response Surface Methodology (RSM) and Artificial neural network (ANN). Evaluation of the isotherm and kinetic parameters of the adsorption process. (C) 2019 The Author(s). Published by Elsevier B.V.