ANN Modeling for Cadmium Biosorption from Potable Water Using a Packed-Bed Column Process
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ANN Modeling for Cadmium Biosorption from Potable Water Using a Packed-Bed Column Process

Authors: Dariush Jafari, Seyed Ali Jafari

Abstract:

The recommended limit for cadmium concentration in potable water is less than 0.005 mg/L. A continuous biosorption process using indigenous red seaweed, Gracilaria corticata, was performed to remove cadmium from the potable water. The process was conducted under fixed conditions and the breakthrough curves were achieved for three consecutive sorption-desorption cycles. A modeling based on Artificial Neural Network (ANN) was employed to fit the experimental breakthrough data. In addition, a simplified semi empirical model, Thomas, was employed for this purpose. It was found that ANN well described the experimental data (R2>0.99) while the Thomas prediction were a bit less successful with R2>0.97. The adjusted design parameters using the nonlinear form of Thomas model was in a good agreement with the experimentally obtained ones. The results approve the capability of ANN to predict the cadmium concentration in potable water.

Keywords: ANN, biosorption, cadmium, packed-bed, potable water.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1338030

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References:


[1] B. Volesky, and I. Prasetyo, “Cadmium removal in a biosorption column,” Biotechnology and Bioengineering, vol. 43, pp. 1010-1015, 1994.
[2] B. Volesky, J. Weber, and J. M. Park, “Continuous-flow metal biosorption in a regenerable Sargassum column,” Water Research, vol. 37, pp. 297-306, 2003.
[3] M. Mukhopadhyay, S. B. Noronha, and G. K. Suraishkumar, “Copper biosorption in a column of pretreated Aspergillus niger biomass,” Chemical Engineering Journal, vol. 144, pp. 386-390, 2008.
[4] P. Lodeiro, R. Herrero, and M. E. Vicente, “The use of protonated Sargassum muticum as biosorbent for cadmium removal in a fixed-bed column,” Journal of hazardous materials, vol. 137, pp. 244-253, 2006.
[5] K. H. Chu, and M. A. Hashim, “Copper biosorption on immobilized seaweed biomass: Column breakthrough characteristics,” Journal of Environmental Sciences, vol. 19, pp. 928-932, 2007.
[6] G. S. Shephard, S. Stockenstroma, D. Villiers, W.J. Engelbrecht, and G.F.S. Wessels, “Degradation of microcystin toxins in a falling film photocatalytic reactor with immobilized titanium dioxide catalyst,” Water Res., vol. 36, no. 1, pp. 140-146, 2002.
[7] A.R. Soleymani, J. Saiena, and H. Bayat, “Artificial neural networks developed for prediction of dye decolorization efficiency with UV/K2S2O8 process,” Chem. Eng. J., vol. 170, no. 1, pp. 29-35, 2011.
[8] Aleboyeh, M.B. Kasiri, M.E. Olya, and H. Aleboyeh, "Prediction of azo dye decolorization by UV/H2O2 using artificial neural networks", Dyes. Pigments, vol.77, no. 2, pp. 288-294, 2008.
[9] D. Park, Y. S.Yun, D. S. Lee, S. R. Lim, and J. M. Park, “Column study on Cr (VI)-reduction using the brown seaweed Ecklonia biomass,” Journal of hazardous materials, vol. 137, pp. 1377-1384, 2006.
[10] D. Saha, A. Bhowal, and S. Datta, “Artificial neural network modeling of fixed bed biosorption using radial basis approach,” Heat and mass transfer, vol. 46, pp. 431-436, 2010.
[11] S. Chowdhury, and P. D. Saha, “Artificial neural network (ANN) modeling of adsorption of methylene blue by NaOH-modified rice husk in a fixed-bed column system,” Environmental Science and Pollution Research, vol. 20, pp. 1050-1058, 2013.
[12] E. Malkoc, Y. Nuhoglu, and Y. Abali, Cr (VI) “adsorption by waste acorn of Quercus ithaburensis in fixed beds: Prediction of breakthrough curves,” Chemical Engineering Journal, vol. 119, pp. 61-68, 2006.
[13] K. Naddafi, R. Nabizadeh, R. Saeedi, A. H. Mahvi, F. Vaezi, K. Yaghmaeian, and S. Nazmara, “Biosorption of lead (II) and cadmium (II) by protonated Sargassum glaucescens biomass in a continuous packed bed column,” Journal of hazardous materials, vol. 147, pp. 785- 791, 2007.
[14] S. Aminossadati, A. Kargar, and B. Ghasemi, “Adaptive network-based fuzzy inference system analysis of mixed convection in a two-sided liddriven cavity filled with a nanofluid,” Int. J. Therm. Sci. vol. 52, pp. 102-111, 2012.
[15] K. Devabhaktuni, M. Yagoub, Y. Fang, J. Xu, and Q. Zhang, “Neural networks for microwave modeling: Model development issues and nonlinear modeling techniques,” J. RF Microwave Comput. Aided Eng., vol. 11, no. 1, pp. 4-21, 2001.
[16] R. Abedini, M. Esfandyari, A. Nezhadmoghadam, and B. Rahmanian, “The prediction of undersaturated crude oil viscosity: An artificial neural network and fuzzy model approach,” Pet. Sci. Technol., vol. 30, no. 19, pp. 2008-2021, 2012.
[17] C. Zhu, Z. Luan, Y. Wang, and X. Shan, “Removal of cadmium from aqueous solutions by adsorption on granular red mud (GRM),” Separation and Purification Technology, vol. 57, pp. 161-169, 2007.
[18] K. Vijayaraghavan, and D. Prabu, “Potential of Sargassum wightii biomass for copper (II) removal from aqueous solutions: Application of different mathematical models to batch and continuous biosorption data,” Journal of hazardous materials, vol. 137, pp. 558-564, 2006.