Sb (Ⅲ) Removal from Aqueous Solutions by the Mesoporous Fe_3O_4/GO Nanocomposites: Modeling and Optimization Using Artificial Intelligence

摘要

The mesoporous graphene oxide-supported ferroferric oxide (Fe_3O_4/GO) nanocomposites (the average size of 30.08 nm) were controllably synthesized in the present study. The successful in situ growth of Fe_3O_4 nanoparticles on GO surface was ascribed to the oxygen-containing groups on GO. The magnetic separation was employed for Sb(Ⅲ) removal from aqueous solutions and artificial intelligence techniques were adopted to reduce the number and cost of experiments, in order to render these nanocomposites of a practical value. The three methods, including response surface methodology (RSM), artificial neural network-genetic algorithm (ANN-GA) and artificial neural network-particle swarm optimization (ANN-PSO), were used to model and optimize the removal of Sb(Ⅲ) from aqueous solutions. These three models were evaluated based on correlation coefficient (R~2) and mean squared error (MSE). The higher R~2 value and lower MSE of ANN-GA demonstrated the superiority of ANN-GA model over ANN-PSO and RSM models. Analysis of variance, gradient boosted regression trees (GBRT) and Garson method exhibited that contact time was the most influential variable for the Sb(Ⅲ) removal. Fitting of isotherm data showed that the removal process was controlled by the monolayer adsorption on a homogeneous surface based on the values of R~2, x~2, sum of absolute errors (SAE) and average percentage errors (APE). The adsorption process followed the pseudo-second-order model, which was spontaneous and entropy-driven. It was observed that the adsorption process was accompanied with the redox reaction based on the XPS analysis. The regeneration experiments showed that the mesoporous Fe_3O_4/GO nanocomposites are an effective and reusable adsorbent within four regeneration cycles.