ANN Modeling for Cadmium Biosorption from Potable Water Using a Packed-Bed Column Process
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.
[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.
[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.
@article{"International Journal of Chemical, Materials and Biomolecular Sciences:68815", author = "Dariush Jafari and Seyed Ali Jafari", title = "ANN Modeling for Cadmium Biosorption from Potable Water Using a Packed-Bed Column Process", 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.", volume = "9", number = "1", pages = "75-5", }
