Prediction and optimizing of methylene blue sequestration to activated charcoal/magnetic nanocomposites using artificial neutral network and response surface methodology
Uyiosa Osagie Aigbe, Thabang Calvin Lebepe, Oluwatobi S. Oluwafemi, Otolorin Adelaja Osibote
Abstract
Green synthesized magnetic nanoparticles (MNPs) linked with activated charcoal (AC) (AC/Fe3O4 NCs) were exploited for methylene blue (MB) confiscation in this study. The AC/Fe3O4 NCs produced were characterized using TEM, FTIR, UV/Vis and XRD spectrometry. The Response-Surface-Methodology (RSM) was utilized to improve the experimental data for the MB sorption to AC/Fe3O4 NCs, with 20 experimental runs implemented through a central composite design (CCD) to assess the effect of sorption factors-initial MB concentration, pH and sorbent dosage effects on the response (removal-effectiveness). The quadratic model was discovered to ideally describe the sorption process, with an R2 value of 0.9857. The theoretical prediction of the experimental data using the Artificial-Neural-Network (ANN) model showed that the Levenberg-Marquardt (LM) had a better performance criterion. Comparison between the modelled experimental and predicted data showed also that the LM algorithm had a high R2 of 0.9922, which showed NN model applicability for defining the sorption of MB to AC/Fe3O4 NCs with practical precision. The results of the non-linear fitting (NLF) of both isotherm and kinetic models, showed that the sorption of MB to AC/Fe3O4 NCs was perfectly described using the pseudo-second-order (PSOM) and Freundlich (FRHM) models. The estimated optimum sorption capacity was 455 mg.g−1. Thermodynamically, the sorption of MB to AC/Fe3O4 NCs was shown to be non-spontaneous and endothermic.