Prediction and optimizing of methylene blue sequestration to activated charcoal/magnetic nanocomposites using artificial neutral network and response surface methodology.

Green synthesized magnetic nanoparticles (MNPs) linked with activated charcoal (AC) (AC/FeO NCs) were exploited for methylene blue (MB) confiscation in this study. The AC/FeO 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/FeO 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 R 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 R of 0.9922, which showed NN model applicability for defining the sorption of MB to AC/FeO 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/FeO NCs was perfectly described using the pseudo-second-order (PSOM) and Freundlich (FRHM) models. The estimated optimum sorption capacity was 455 mg g. Thermodynamically, the sorption of MB to AC/FeO NCs was shown to be non-spontaneous and endothermic.