Mercury ion adsorption on AC@Fe3O4-NH2-COOH from saline solutions: Experimental studies and artificial neural network modeling

An efficient, novel functionalized supported magnetic nanoparticle (AC@Fe3O4-NH2-COOH) has been synthesized by co-precipitation method for removal of mercury ions from saline solutions. High dispersed supported magnetic nanoparticles with particle sizes less than 30 nm were formed over activated carbon derived from local walnut shell. Surface characterizations of supported magnetic nanoparticles were evaluated by Boehm test, Brunauer- Emmett-Teller (BET) surface area, X-ray diffraction (XRD), transmission electron microscopy (TEM), Fourier transform infrared spectroscopy (FT-IR), thermogravimetric analysis (TGA) and X-ray fluorescence (XRF). A three-layer artificial neural network (ANN) code was developed to predict the Hg (II) ions removal from aqueous solution by AC@Fe3O4-NH2-COOH. The three-layer back-propagation (BP) is configured of tangent sigmoid transfer function (tansig) at hidden layer with eight neurons for AC@Fe3O4-NH2-COOH, and linear transfer function (purelin) at output layer. According to the calculated MSEs, Levenberg-Marquardt algorithm (LMA) was the best training algorithm among others. The linear regressions between the predicted and experimental outputs were proven to be a good agreement with a correlation coefficient of about 0.9984 for five model variables. Maximum adsorption capacity was achieved 80mg/g by Langmuir isotherm at pH of 7 and salinity of 25,000 ppm. Kinetic studies illustrated that mercury adsorption follows pseudo-second-order.