Application of response surface methodology and artificial neural network for the preparation of Fe-loaded biochar for enhanced Cr(VI) adsorption and its physicochemical properties and Cr(VI) adsorption characteristics - Kang, Jin Kyu; Seo, Eun Jin; Lee, Chang Gu; Jeong, Sanghyun; Park, Seong Jik (Environmental Science and Pollution Research, 2022)
In this study, we optimized and explored the effect of the conditions for synthesizing Fe-loaded food waste biochar (Fe@FWB) for Cr(VI) removal using the response surface methodology (RSM) and artificial neural network (ANN). The pyrolysis time, temperature, and Fe concentration were selected as the independent variables, and the Cr(VI) adsorption capacity of Fe@FWB was maximized. RSM analysis showed that the p-values of pyrolysis temperature and Fe concentration were less than 0.05, indicating that those variables were statically significant, while pyrolysis time was less significant due to its high p-value (0.2830). However, the ANN model results showed that the effect of pyrolysis time was more significant on Cr(VI) adsorption capacity than Fe concentration. The optimal conditions, determined by the RSM analysis with a lower sum of squared error than ANN analysis, were used to synthesize the optimized Fe@FWB (Fe@FWB-OPT) for Cr(VI) removal. From the equilibrium model fitting, the Langmuir model showed a better fit than the Freundlich model, while the Redlich–Peterson isotherm model overlapped. The Cr(VI) sorption capacity of Fe@FWB-OPT calculated from the Langmuir model was 377.71 mg/g, high enough to be competitive to other adsorbents. The kinetic Cr(VI) adsorption was well described by the pseudo-second-order and Elovich models. The XPS results showed that Cr adsorbed on the surface of Fe-FWB-OPT was present not only as Cr(VI) but also as Cr(III) by the reduction of Cr(VI). The results of Cr(VI) adsorption by varying the pH indicate that electrostatic attraction is a key adsorption mechanism.
Keywords: Adsorption; Charcoal; Chromium; Food; Kinetics; Neural Networks, Computer; Refuse Disposal; Water Pollutants, Chemical; Artificial neural network; Biochar; Food waste; Hexavalent chromium; Iron loading; Response surface methodology