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The Scientific World Journal
Volume 2013 (2013), Article ID 240158, 5 pages
http://dx.doi.org/10.1155/2013/240158
Research Article

Prediction of Heavy Metal Removal by Different Liner Materials from Landfill Leachate: Modeling of Experimental Results Using Artificial Intelligence Technique

1Department of Environmental Engineering, Engineering Faculty, Ondokuz Mays University, Kurupelit, 55139 Samsun, Turkey
2Department of Electric and Electronic Engineering, Engineering Faculty, Ondokuz Mays University, Kurupelit, 55139 Samsun, Turkey

Received 3 April 2013; Accepted 23 May 2013

Academic Editors: G. Brunetti, I. Ortiz, and C. K. Yoo

Copyright © 2013 Nurdan Gamze Turan et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. M. Alkan, B. Kalay, M. Doǧan, and Ö. Demirbaş, “Removal of copper ions from aqueous solutions by kaolinite and batch design,” Journal of Hazardous Materials, vol. 153, no. 1-2, pp. 867–876, 2008. View at Publisher · View at Google Scholar · View at Scopus
  2. F. Claret, C. Tournassat, C. Crouzet et al., “Metal speciation in landfill leachates with a focus on the influence of organic matter,” Waste Management, vol. 31, no. 9-10, pp. 2036–2045, 2011. View at Publisher · View at Google Scholar · View at Scopus
  3. T. Aman, A. A. Kazi, M. U. Sabri, and Q. Bano, “Potato peels as solid waste for the removal of heavy metal copper(II) from waste water/industrial effluent,” Colloids and Surfaces B, vol. 63, no. 1, pp. 116–121, 2008. View at Publisher · View at Google Scholar · View at Scopus
  4. P. H. Brunner and J. Fellner, “Setting priorities for waste management strategies in developing countries,” Waste Management and Research, vol. 25, no. 3, pp. 234–240, 2007. View at Publisher · View at Google Scholar · View at Scopus
  5. D. Laner, M. Crest, H. Scharff, J. W. F. Morris, and M. A. Barlaz, “A review of approaches for the long-term management of municipal solid waste landfills,” Waste Management, vol. 32, no. 3, pp. 498–512, 2012. View at Publisher · View at Google Scholar · View at Scopus
  6. S. Renou, J. G. Givaudan, S. Poulain, F. Dirassouyan, and P. Moulin, “Landfill leachate treatment: review and opportunity,” Journal of Hazardous Materials, vol. 150, no. 3, pp. 468–493, 2008. View at Publisher · View at Google Scholar · View at Scopus
  7. T. Chalermyanont, S. Arrykul, and N. Charoenthaisong, “Potential use of lateritic and marine clay soils as landfill liners to retain heavy metals,” Waste Management, vol. 29, no. 1, pp. 117–127, 2009. View at Publisher · View at Google Scholar · View at Scopus
  8. G. di Bella, D. di Trapani, G. Mannina, and G. Viviani, “Modeling of perched leachate zone formation in municipal solid waste landfills,” Waste Management, vol. 32, no. 3, pp. 456–462, 2012. View at Publisher · View at Google Scholar · View at Scopus
  9. N. Yusof, A. Haraguchi, M. A. Hassan, M. R. Othman, M. Wakisaka, and Y. Shirai, “Measuring organic carbon, nutrients and heavy metals in rivers receiving leachate from controlled and uncontrolled municipal solid waste (MSW) landfills,” Waste Management, vol. 29, no. 10, pp. 2666–2680, 2009. View at Publisher · View at Google Scholar · View at Scopus
  10. K. Czurda, “Encapsulation parameters in waste deposit technology: geologic barriers and liner systems,” Geo.Alp, vol. 3, pp. 207–214, 2006. View at Google Scholar
  11. L. H. Mollins, D. I. Stewart, and T. W. Cousens, “Predicting the properties of bentonite-sand mixtures,” Clay Minerals, vol. 31, no. 2, pp. 243–252, 1996. View at Google Scholar · View at Scopus
  12. T. B. Musso, K. E. Roehl, G. Pettinari, and J. M. Vallés, “Assessment of smectite-rich claystones from Northpatagonia for their use as liner materials in landfills,” Applied Clay Science, vol. 48, no. 3, pp. 438–445, 2010. View at Publisher · View at Google Scholar · View at Scopus
  13. T. C. Kenney, W. A. van Veen, M. A. Swallow, and M. A. Sungaila, “Hydraulic conductivity of compacted bentonite-sand mixtures,” Canadian Geotechnical Journal, vol. 29, no. 3, pp. 364–374, 1992. View at Google Scholar · View at Scopus
  14. T. Abichou, C. H. Benson, and T. B. Edil, “Micro-structure and hydraulic conductivity of simulated sand-bentonite mixtures,” Clays and Clay Minerals, vol. 50, no. 5, pp. 537–545, 2002. View at Publisher · View at Google Scholar · View at Scopus
  15. M. R. Fagundes-Klen, L. G. L. Vaz, M. T. Veit, C. E. Borba, E. A. Silva, and A. D. Kroumov, “Biosorption of the copper and cadmium ions-a study through adsorption isotherm analysis,” Bioautomation, vol. 7, no. 1, pp. 23–33, 2007. View at Google Scholar
  16. D. Salari, N. Daneshvar, F. Aghazadeh, and A. R. Khataee, “Application of artificial neural networks for modeling of the treatment of wastewater contaminated with methyl tert-butyl ether (MTBE) by UV/H2O2 process,” Journal of Hazardous Materials, vol. 125, no. 1–3, pp. 205–210, 2005. View at Publisher · View at Google Scholar · View at Scopus
  17. K. Yetilmezsoy and S. Demirel, “Artificial neural network (ANN) approach for modeling of Pb(II) adsorption from aqueous solution by Antep pistachio (Pistacia Vera L.) shells,” Journal of Hazardous Materials, vol. 153, no. 3, pp. 1288–1300, 2008. View at Publisher · View at Google Scholar · View at Scopus
  18. S. Aber, A. R. Amani-Ghadim, and V. Mirzajani, “Removal of Cr(VI) from polluted solutions by electrocoagulation: modeling of experimental results using artificial neural network,” Journal of Hazardous Materials, vol. 171, no. 1–3, pp. 484–490, 2009. View at Publisher · View at Google Scholar · View at Scopus
  19. N. G. Turan, B. Mesci, and O. Ozgonenel, “The use of artificial neural networks (ANN) for modeling of adsorption of Cu(II) from industrial leachate by pumice,” Chemical Engineering Journal, vol. 171, no. 3, pp. 1091–1097, 2011. View at Publisher · View at Google Scholar · View at Scopus
  20. D.-J. Choi and H. Park, “A hybrid artificial neural network as a software sensor for optimal control of a wastewater treatment process,” Water Research, vol. 35, no. 16, pp. 3959–3967, 2001. View at Publisher · View at Google Scholar · View at Scopus
  21. Ö. Çinar, H. Hasar, and C. Kinaci, “Modeling of submerged membrane bioreactor treating cheese whey wastewater by artificial neural network,” Journal of Biotechnology, vol. 123, no. 2, pp. 204–209, 2006. View at Publisher · View at Google Scholar · View at Scopus
  22. A. Wang, C. Liu, H. Han, N. Ren, and D.-J. Lee, “Modeling denitrifying sulfide removal process using artificial neural networks,” Journal of Hazardous Materials, vol. 168, no. 2-3, pp. 1274–1279, 2009. View at Publisher · View at Google Scholar · View at Scopus
  23. R. M. Aghav, S. Kumar, and S. N. Mukherjee, “Artificial neural network modeling in competitive adsorption of phenol and resorcinol from water environment using some carbonaceous adsorbents,” Journal of Hazardous Materials, vol. 188, no. 1–3, pp. 67–77, 2011. View at Publisher · View at Google Scholar · View at Scopus
  24. 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, no. 4, pp. 431–436, 2010. View at Publisher · View at Google Scholar · View at Scopus
  25. R. A. Chayjan and M. Esna-Ashari, “Comparison between artificial neural networks and mathematical models for estimating equilibrium moisture content in raisin,” Agricultural Engineering International: The CIGRE Journal, vol. 12, no. 1, p. 158, 2010. View at Google Scholar
  26. R. Singh, R. S. Bhoopal, and S. Kumar, “Prediction of effective thermal conductivity of moist porous materials using artificial neural network approach,” Building and Environment, vol. 46, no. 12, pp. 2603–2608, 2011. View at Publisher · View at Google Scholar · View at Scopus