103
Views
22
CrossRef citations to date
0
Altmetric
Articles

Artificial neural network modelling for removal of chromium (VI) from wastewater using physisorption onto powdered activated carbon

, , &
Pages 3632-3641 | Received 03 Mar 2014, Accepted 07 Nov 2014, Published online: 03 Dec 2014

References

  • K.Z. Setshedi, M. Bhaumik, S. Songwane, M.S. Onyango, A. Maity, Exfoliated polypyrrole-organically modified montmorillonite clay nanocomposite as a potential adsorbent for Cr(VI) removal, Chem. Eng. J. 222 (2013) 186–197.10.1016/j.cej.2013.02.061
  • K.Y. Foo, B.H. Hameed, Insights into the modeling of adsorption isotherm systems, Chem. Eng. J. 156 (2010) 2–10.10.1016/j.cej.2009.09.013
  • D. Aguado, J. Ribes, T. Montoya, J. Ferrer, A. Seco, A methodology for sequencing batch reactor identification with artificial neural networks: A case study, Comput. Chem. Eng. 33 (2009) 465–472.10.1016/j.compchemeng.2008.10.018
  • K. Anupam, S. Dutta, C. Bhattacharjee, S. Datta, Optimisation of adsorption efficiency for reactive red 198 removal from wastewater over TiO2 using response surface methodology, Can. J. Chem. Eng. 89(5) (2011) 1274–1280.10.1002/cjce.v89.5
  • C.W. Baxter, S.J. Stanley, Q. Zhang, D.W. Smith, Developing artificial neural network models of water treatment processes: A guide for utilities, J. Environ. Eng. Sci. 1 (2002) 201–211.10.1139/s02-014
  • M.M. Hamed, M.G. Khalafallah, E.A. Hassanien, Prediction of wastewater treatment plant performance using artificial neural networks, Environ. Modell. Softw. 19(10) (2004) 919–928.10.1016/j.envsoft.2003.10.005
  • B.V. Babu, V. Ramakrishna, K.K. Chakravarthy, Artificial neural networks for modeling of adsorption (2008). Accessed 25 June, 2011. Available from: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.110.1612.
  • Y. Zhang, J. Xu, Z. Yuan, H. Xu, Q. Yu, Artificial neural network-genetic algorithm based optimization for the immobilization of cellulase on the smart polymer Eudragit L-100, Bioresour. Technol. 101 (2010) 3153–3158.10.1016/j.biortech.2009.12.080
  • A.B. Chelani, C.V.C. Rao, K.M. Phadke, M.Z. Hasan, Prediction of sulphur dioxide concentration using artificial neural networks, Environ. Modell. Softw. 17 (2002) 161–168.
  • B.V. Babu, V. Ramakrishna, Applicability of regression technique for physical modeling: A case study on adsorption in wastewater treatment, Proceedings of International Symposium & 55th Annual Session of IIChE (CHEMCON-2002), Osmania Univeristy, Hyderabad, India, 2002.
  • X. Du, Q. Yuan, J. Zhao, Y. Li, Comparison of general rate model with a new model—Artificial neural network model in describing chromatographic kinetics of solanesol adsorption in packed column by macroporous resins, J. Chromatogr. A 1145 (2007) 165–174.10.1016/j.chroma.2007.01.065
  • W. Gao, S. Engell, Estimation of general nonlinear adsorption isotherms from chromatograms, Comput. Chem. Eng. 29 (2005) 2242–2255.10.1016/j.compchemeng.2005.08.006
  • P.C. Nayak, Y.R.S. Rao, K.P. Sudheer, Groundwater level forecasting in a shallow aquifer using artificial neural network approach, Water Resour. Manage. 20 (2006) 77–90.10.1007/s11269-006-4007-z
  • K. Pramanik, Use of ANN for predication of cell mass and ethanol concentration in batch fermentation using Saccbaromyces cerevisiae yeast, J. Inst. Eng. (India) 85 (2004) 31–35.
  • S. Lingireddy, G.M. Brion, Artificial Neural Networks in Water Supply Engineering, ASCE Publications, Reston, VA, 2005.
  • C.M. Zealand, D.H. Burn, S.P. Simonovic, Short term streamflow forecasting using artificial neural networks, J. Hydrol. 214 (1999) 32–48.10.1016/S0022-1694(98)00242-X
  • R. Hecht-Nielsen, Kolmogorov’s mapping neural network existence theorem, Proceedings of the First IEEE International Joint Conference on Neural Networks, Institute of Electrical and Electronic Engineering, San Diego, CA, 1987.
  • H.R. Maier, G.C. Dandy, Neural networks for the prediction and forecasting of water resources variables: A review of modelling issues and applications, Environ. Modell. Softw. 15(1) (2000) 101–124.10.1016/S1364-8152(99)00007-9
  • M. Alp, H.K. Cigizoglu, Suspended sediment load simulation by two artificial neural network methods using hydrometeorological data, Environ. Modell. Softw. 22 (2007) 2–13.10.1016/j.envsoft.2005.09.009
  • S. Birikundavyi, R. Labib, H.T. Trung, J. Rousselle, Performance of neural networks in daily streamflow forecasting, J. Hydrol. Eng. 7(5) (2002) 392–398.10.1061/(ASCE)1084-0699(2002)7:5(392)
  • H.K. Cigizoglu, Incorporation of ARMA models into flow forecasting by artificial neural networks, Environmetrics 14(4) (2003) 417–427.10.1002/(ISSN)1099-095X
  • D.E. Rummelhart, G.E. Hinton, R.J. Wiliams, Learning internal representations by error propagation, Report 8506, Institute for Cognitive Science, University of California, San Diego, CA, 1985.
  • D. Mohan, C.U. Pittman Jr., Activated carbons and low cost adsorbents for remediation of tri- and hexavalent chromium from water, J. Hazard. Mater. 137 (2006) 762–811.10.1016/j.jhazmat.2006.06.060
  • K. Anupam, S. Dutta, C. Bhattacharjee, S. Datta, Adsorptive removal of chromium (VI) from aqueous solution over powdered activated carbon: Optimisation through response surface methodology, Chem. Eng. J. 173(1) (2011) 135–143.10.1016/j.cej.2011.07.049
  • N. Zhao, N. Wei, J. Li, Z. Qiao, J. Cui, F. He, Surface properties of chemically modified activated carbons for adsorption rate of Cr (VI), Chem. Eng. J. 115 (2005) 133–138.10.1016/j.cej.2005.09.017
  • G. Jing, Z. Zhou, L. Song, M. Dong, Ultrasound enhanced adsorption and desorption of chromium (VI) on activated carbon and polymeric resin, Desalination 279 (2011) 423–427.10.1016/j.desal.2011.06.001
  • L.S. de Lima, M.D.M. Araujo, S.P. Quináia, D.W. Migliorine, J.R. Garcia, Adsorption modeling of Cr, Cd and Cu on activated carbon of different origins by using fractional factorial design, Chem. Eng. J. 166 (2011) 881–889.10.1016/j.cej.2010.11.062
  • O. Ahmed, M. Nordin, S. Sulaiman, W. Fatimah, Study of genetic algorithm to fully-automate the design and training of artificial neural network, Int. J. Comput. Sci. Network Secur. 9(1) (2009) 217–226.
  • H.R. Godini, M. Ghadrdan, M.R. Omidkhah, S.S. Madaeni, Part II: Prediction of the dialysis process performance using Artificial Neural Network (ANN), Desalination 265 (2011) 11–21.10.1016/j.desal.2010.04.039
  • S. Lawrence, C.L. Giles, A.C. Tsoi, What size neural network gives optimal generalization? Convergence properties of backpropagation, Technical Report Umiacs-Tr-96-22 and CS-TR-3617, Institute for Advanced Computer Studies, University of Maryland, College Park, MD, 1996.
  • K.V. Kumar, K. Porkodi, Modelling the solid–liquid adsorption processes using artificial neural networks trained by pseudo second order kinetics, Chem. Eng. J. 148(1) (2009) 20–25.10.1016/j.cej.2008.07.026
  • H.R. Maier, N. Morgan, C.W.K. Chow, Use of artificial neural networks for predicting optimal alum doses and treated water quality parameters, Environ. Modell. Softw. 19 (2004) 485–494.10.1016/S1364-8152(03)00163-4
  • K. Hornik, M. Stinchcombe, H. White, Multilayer feedforward networks are universal approximators, Neural Networks 2 (1989) 359–366.10.1016/0893-6080(89)90020-8
  • S. Dutta, S.A. Parsons, C. Bhattacharjee, S. Bandhyopadhyay, S. Datta, Development of an artificial neural network model for adsorption and photocatalysis of reactive dye on TiO2 surface, Expert Syst. Appl. 37 (2010) 8634–8638.10.1016/j.eswa.2010.06.090

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.