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

Chinenye Adaobi Igwegbe; Leili Mohmmadi; Shahin Ahmadi; Abbas Rahdar; Danial Khadkhodaiy; Rahmin Dehghani; Somayeh Rahdar

doi:10.1016/j.mex.2019.07.016

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

MethodsX. 2019 Jul 19:6:1779-1797. doi: 10.1016/j.mex.2019.07.016. eCollection 2019.

Authors

Chinenye Adaobi Igwegbe¹, Leili Mohmmadi², Shahin Ahmadi³, Abbas Rahdar⁴, Danial Khadkhodaiy², Rahmin Dehghani⁵, Somayeh Rahdar³

Affiliations

¹ Department of Chemical Engineering, Nnamdi Azikiwe University, Awka, Nigeria.
² Department of Environmental Health, Zabol University of Medical Sciences, Zahedan, Iran.
³ Department of Environmental Health, Zabol University of Medical Sciences, Zabol, Iran.
⁴ Department of Physics, University of Zabol, Zabol, P.O. Box. 35856-98613, Iran.
⁵ Department of Environmental Health, Karman University of Medical Sciences, Karman, Iran.

Abstract

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.

Keywords: Adsorption; Artificial Neural Network; Central composite design; Methylene Blue; Nanoparticles; Response Surface Methodology.