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Language: English

Conflict of Interest: In relation to this article, we declare that there is no conflict of interest.

Publication history: Received June 17, 2017
Accepted October 15, 2017

This is an Open-Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/bync/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

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

Mohammad Pazouki^1† Mohammad Zabihi^{2 3} Jalal Shayegan⁴ Mohammad Hossein Fatehi⁴

¹Laboratory of Electrochemistry, Materials and Energy Research Center, P. O. Box 14155-4777, Tehran, Iran ²Chemical Engineering Faculty, Sahand University of Technology, P. O. Box 51335-1996, Sahand New Town, Tabriz, Iran ³Environmental Engineering Research Center, Sahand University of Technology, Tabriz, Iran ⁴Department of Chemical and Petroleum Engineering, Sharif University of Technology, Azadi Avenue, P. O. Box 11155-9465, Tehran, Iran

mpazouki@merc.ac.ir

Korean Journal of Chemical Engineering, March 2018, 35(3), 671-683(13), 10.1007/s11814-017-0293-9

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Abstract

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.

Keywords

Adsorption Mercury-magnetic Salinity Activated Carbon Co Precipitation Artificial Neural Network

References

Johari K, Saman N, Song ST, Chin CS, Kong H, Mat H, Int. Biodeterior. Biodegrad., 109, 45 (2016)
Zabihi M, Asl AH, Ahmadpour A, J. Hazard. Mater., 174(1-3), 251 (2010)
Zabihi M, Ahmadpour A, Asl AH, J. Hazard. Mater., 167(1-3), 230 (2009)
Zhang X, Wang Y, Yang S, Polymer, 114, 521 (2014)
Liu Y, Chen M, Hao YM, Chem. Eng. J., 218, 46 (2013)
Wang Y, Wei Q, Huang Y, Mater. Lett., 157, 67 (2015)
Wasti A, Awan MA, J. of the Association of Arab Universities for Basic and Applied Sciences, 20, 26 (2016)
Papas BN, Whitten JL, Surf. Sci., 651, 22 (2016)
Uzunova EL, Mikosch H, Microporous Mesoporous Mater., 232, 119 (2016)
Mthombeni NH, Mbakop S, Ochieng A, Onyango MS, J. Taiwan Inst. Chem. Engineers, 9 (2016).
Andrejkovicova S, Sudagar A, Rocha J, Patinha C, Hajjaji W, da Silva EF, Velosa A, Rocha F, Appl. Clay Sci., 126, 141 (2016)
Moritz MG, Moritz M, Mater. Sci. Eng., 69, 815 (2016)
Russo V., Masiello D., Trifuoggi M., Di Serio M., Tesser R., Chem. Eng. J., 302, 287 (2016)
Srivastava N, Thakur AK, Shahi VK, Carbohydr. Polym., 136, 1315 (2016)
Zabihi M, Khorasheh F, Shayegan J, RSC Adv, 5, 5107 (2015)
Nethaji S, Sivasamy A, Mandal AB, Bioresour. Technol., 134, 94 (2013)
Xu JH, Gao NY, Zhao DY, Yin DQ, Zhang H, Gao YQ, Shi W, Sep. Purif. Technol., 147, 9 (2015)
Yetilmezsoy K, Demirel S, J. Hazard. Mater., 153(3), 1288 (2008)
Agarwal S, Tyagi I, Gupta VK, Ghaedi M, Masoomzade M, Ghaedi AM, Mirtamizdoust B, J. Mol. Liq., 218, 354 (2016)
Khandanloua R, Fard-Masoumi HR, Ahmad MB, Shameli K, Basri M, Kalantari K, Ecological Engineering, 91, 249 (2016)
Dimopoulos I, Chronopoulos-Serelia JA, Lek S, Ecol. Model., 120, 157 (1999)
Aber S, Daneshvar N, Soroureddin SM, Chabok A, Asadpour-Zeynali K, Desalination, 211(1-3), 87 (2007)
Daneshvar N, Khataee AR, Djafarzadeh N, J. Hazard. Mater., B137, 1788 (2006)
Fatehi MH, Shayegan J, Zabihi M, Goodarznia I, J. Environ. Chem. Eng., 5, 1754 (2017)
Hasar H, J. Hazard. Mater., B97, 49 (2003)
Bao SY, Tang LH, Li K, Ning P, Peng JH, Guo HB, Zhu TT, Liu Y, J. Colloid Interface Sci., 462, 235 (2016)
Xu Y, Dang Q, Liu C, Yan J, Fan B, Cai J, Li J, Colloids Surf. A: Physicochem. Eng. Asp., 482, 353 (2015)
Wang SG, Wang KK, Dai C, Shi HZ, Li JL, Chem. Eng. J., 262, 897 (2015)
Huang SH, Chen DH, J. Hazard. Mater., 163(1), 174 (2009)
Wang Y, Zhan Y, Hou C, He X, Liu M, J. Taiwan Institute Chem. Engineers, 7, 1 (2015)
Alijani H, Shariatinia Z, Mashhadi AA, Chem. Eng. J., 281, 468 (2015)
Anagnostopoulos VA, Manariotis ID, Karapanagioti HK, Chrysikopoulos CV, Chem. Eng. J., 213, 135 (2012)