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


1 Faculty of Engineering Modern Technologies, Amol University of Special Modern Technologies, Amol, Iran

2 Faculty of Water and Envirommental Engineering, Shahid Beheshti University, Tehran, Iran

3 Faculty of Chemical, Gas and Petroleum Engineering, Semnan University, Semnan, Iran

4 Department of Chemical Engineering, Babol Noshirvani University of Technolgy, Babol, Iran


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.


1.     Monteiro, C.M., Castro, P.M. and Malcata, F.X., Microalga-mediated bioremediation of heavy metal-contaminated surface waters, in Biomanagement of metal-contaminated soils. 2011, Springer.365-385.

2.     Ghorbani, F., Sanati, A., Younesi, H. and Ghoreyshi, A., "The potential of date-palm leaf ash as low-cost adsorbent for the removal of pb (ii) ion from aqueous solution", International Journal of Engineering-Transactions B: Applications, Vol. 25, No. 4, (2012), 269-278.

3.     Fenjan, S.A., Bonakdari, H., Gholami, A. and Akhtari, A., "Flow variables prediction using experimental, computational fluid dynamic and artificial neural network models in a sharp bend", International Journal of Engineering-Transactions A: Basics, Vol. 29, No. 1, (2016), 14-22.

4.     Zareie, C. and Najafpour, G., "Preparation of nanochitosan as an effective sorbent for the removal of copper ions from aqueous solutions", International Journal of Engineering-Transactions B: Applications, Vol. 26, No. 8, (2013), 829-836.

5.     Afkhami, A., Saber-Tehrani, M. and Bagheri, H., "Modified maghemite nanoparticles as an efficient adsorbent for removing some cationic dyes from aqueous solution", Desalination, Vol. 263, No. 1, (2010), 240-248.

6.     Rahimpour, A., Seyedpour, S.F., Aghapour Aktij, S., Dadashi Firouzjaei, M., Zirehpour, A., Arabi Shamsabadi, A., Khoshhal Salestan, S., Jabbari, M. and Soroush, M., "Simultaneous improvement of antimicrobial, antifouling, and transport properties of forward osmosis membranes with immobilized highly-compatible polyrhodanine nanoparticles", Environmental Science & Technology, Vol. 52, No. 9, (2018), 5246-5258.

7.     de Franco, M.A.E., de Carvalho, C.B., Bonetto, M.M., de Pelegrini Soares, R. and Féris, L.A., "Diclofenac removal from water by adsorption using activated carbon in batch mode and fixed-bed column: Isotherms, thermodynamic study and breakthrough curves modeling", Journal of Cleaner Production, Vol. 181, (2018), 145-154.

8.     Volesky, B. and Holan, Z., "Biosorption of heavy metals", Biotechnology Progress, Vol. 11, No. 3, (1995), 235-250.

9.     Khalili, R. and Eisazadeh, H., "Preparation and characterization of polyaniline/sb2o3 nanocomposite and its application to removal of pb (іі) from aqueous media", International Journal of Engineering-Transactions B: Applications, Vol. 27, No. 2, (2013), 239-246.

10.   Safari, A., Hosseini, R. and Mazinani, M., "A novel type-2 adaptive neuro fuzzy inference system classifier for modelling uncertainty in prediction of air pollution disaster (research note)", International Journal of Engineering-Transactions B: Applications, Vol. 30, No. 11, (2017), 1746-1751.

11.   Yurtsever, U., Yurtsever, M., Şengil, İ.A. and Kıratlı Yılmazçoban, N., "Fast artificial neural network (fann) modeling of cd (ii) ions removal by valonia resin", Desalination and Water Treatment, Vol. 56, No. 1, (2015), 83-96.

12.   Elemen, S., Kumbasar, E.P.A.a. and Yapar, S., "Modeling the adsorption of textile dye on organoclay using an artificial neural network", Dyes and Pigments, Vol. 95, No. 1, (2012), 102-111.

13.   Soetaredjo, F.E., Kurniawan, A., Ong, L., Widagdyo, D.R. and Ismadji, S., "Investigation of the continuous flow sorption of heavy metals in a biomass-packed column: Revisiting the thomas design model for correlation of binary component systems", RSC Advances, Vol. 4, No. 95, (2014), 52856-52870.

14.   Callery, O., Healy, M.G., Rognard, F., Barthelemy, L. and Brennan, R.B., "Evaluating the long-term performance of low-cost adsorbents using small-scale adsorption column experiments", Water Research, Vol. 101, (2016), 429-440.

15.   Jang, J.S.R., Sun, C.T., Mizutani, E. and Ho, Y., "Neuro-fuzzy and soft computing--a computational approach to learning and machine intelligence", Proceedings of the IEEE, Vol. 86, No. 3, (1998), 600-603.

16.   Ghasemi, S., Ghorbani, M. and Ghazi, M.M., "Synthesis and characterization of organic–inorganic core–shell structure nanocomposite and application for zn ions removal from aqueous solution in a fixed-bed column", Applied Surface Science, Vol. 359, (2015), 602-608.