Adaptive Neuro-fuzzy Inference System Prediction of Zn Metal Ions Adsorption by γ-Fe2o3/Polyrhodanine Nanocomposite in a Fixed Bed Column

This study investigates the potential of an intelligence model namely, Adaptive Neuro-Fuzzy Inference System (ANFIS) in prediction of the Zn metal ions adsorption in comparision with two well known empirical models included Thomas and Yoon methods. For this purpose, an organic-inorganic core/shell structure, γ-Fe2O3/polyrhodanine nanocomposite with γ-Fe2O3 nanoparticle as core with average diameter of 15 nm and polyrhodanine as shell with thickness of 3 nm, was synthesized via chemical oxidation polymerization. The properties of adsorbent were characterized with transmission electron microscope (TEM) and Fourier transform infrared (FT-IR) spectroscopy. Sixty seven experimental data sets including the treatment time (t), the initial concentration of Zn (Co), column height (h) and flow rate (Q) were used as input data to predict the ratios of effluent-to-influent concentrations of Zn (Ct/C0). The results showed that ANFIS model with the R coefficient of 0.99 can predict Ct/C0 more accurately than empirical models. Also it was found that the result of the Thomas and Yoon methods with R coefficient of 0.828 and 0.829, respectively were so close to each other. Finally, performance of our ANFIS model was compare to Thomas and Yoon methods in two different conditions, i.e. variable initial influent concentration and variable column height. High performance of ANFIS model was proved by the comparitive results.