Synthesis of nickel ferrite nanoparticles as an efficient magnetic sorbent for removal of an azo-dye: Response surface methodology and neural network modeling

Abstract

In this research, nickel ferrite (NiFe2O4) nanoparticles (NFNs) are prepared through coprecipitation method, and applied for adsorption removal of a model organic pollutant, methyl orange (MO). The characterization of the prepared NFNs was performed using scanning electron microscopy (SEM), X-ray diffraction (XRD), vibrating sample magnetometer (VSM) and transmission electron microscopy (TEM). Optimization and modeling of the removal of MO applying NFNs were performed via central composite design (CCD) and the influential parameters including nano-sorbent amount, dye initial concentration, contact time and pH were considered as input variables for CCD. A dye removal percentage of 99 % was achieved under the optimum condition established for MO removal that was in agreeing with the predicted value. Additionally, multi-layer artificial neural network (ML-ANN) was applied to acquire a predictive model of MO removal. The isothermal investigation of MO adsorption was performed by developing Langmuir, Freundlich and Temkin models, and results showed that experimental data were best fit in Freundlich model. Based on the adsorption kinetics studies, the pseudo-second-order kinetic model was the best model to describe the adsorption mechanism of MO onto NFNs.

Keywords

• nickel ferrite magnetic nanoparticles
• adsorption removal
• artificial neural network
• central composite design
• methyl orange