Photocatalytic Removal of RhB by Ag and Mg Co-Doped ZnO Nanoparticles: Modeling of Operational Parameters Using ANN Based on RSM Data

Abstract

In this study, using response surface methodology (RSM), we focused on optimizing Rhodamine B (RhB) removal via Ag, Mg co-doped ZnO nanoparticles. Statistical analysis and lack of fit indicated that the model was adequate for optimizing the process. The optimum conditions were 240 mg L–1 photocatalyst dosage, 5 mg L–1 RhB concentration and 10 min irradiation time. A mathematical equation was created between RhB removal percent and effective operational parameters. The results calculated through RSM equation were used for modeling the process via artificial neural networks (ANN). Operational parameters such as photocatalyst dosage, initial RhB concentration and irradiation time were selected as the input variables. A three-layered feed forward back propagation ANN model was developed to process modeling. Data used in RSM design was simulated for testing the accuracy of the model generated by ANN. The comparison of the ANN predicted data and the experimental data revealed that the proposed method is significantly more effective in terms of the process modeling. The results obtained via ANN modeling indicated that the photocatalyst dosage with the relative importance of 49.61% can be considered as the most effective parameter for RhB photocatalytic removal.