Modeling and Prediction of Zinc Extraction Efficiency from Concentrate by Operating Condition and Using Artificial Neural Networks
Authors: S. Mousavian, D. Ashouri, F. Mousavian, V. Nikkhah Rashidabad, N. Ghazinia
Abstract:
PH, temperature and time of extraction of each stage, agitation speed and delay time between stages effect on efficiency of zinc extraction from concentrate. In this research, efficiency of zinc extraction was predicted as a function of mentioned variable by artificial neural networks (ANN). ANN with different layer was employed and the result show that the networks with 8 neurons in hidden layer has good agreement with experimental data.
Keywords: Zinc extraction, Efficiency, Neural networks, Operating condition.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1336146
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[1] A.D. Souza, P.S. Pina, F.M.F. Santos, C.A. da Silva, V.A. Leão,” Effect of iron in zinc silicate concentrate on leaching with sulphuric acid”, Hydrometallurgy 95 (2009) 207–214.
[2] Çopur, M., Özmetin, C., Özmetin, E., Kocakerim, M.M, "Optimization study of the leaching of roasted zinc sulphide concentrate with sulphuric acid solutions”, Chemical Engineering and Processing 43 (8), 2004, 1007–1014.
[3] Youcai, Z., Stanforth, R,” Extraction of zinc from zinc ferrites by fusion with caustic soda”, Minerals Engineering 13 (13) , 2000, 1417–1421.
[4] Pappu, A., Saxena, M., Asolekar, S.R, "Jarosite characteristics and its utilization potentials”, Science of the Total Environment 359 (1), 2006, 232–243.
[5] Raghavan, R., Mohanan, P.K., Patnaik, S.C, "Innovative processing technique to produce zinc concentrate from zinc leach residue with simultaneous recovery of lead”, Hydrometallurgy 48 (2), 1998, 225– 237.
[6] Souza, A.D, "Integration Process of the Treatment of Concentrates or Zinc Silicates Ore and Roasted Concentrate of Zinc Sulphides” , 2000.
[7] Brook-Hunt. In: Brook Hunt (Ed.), Mining and Metal Consultants, "Zinc Smelter Study” , 2005, B.H.a.A. Ltd.
[8] Md. Raisul Islam, S. S. Sablani & A. S. Mujumdar, "An Artificial Neural Network Model for Prediction of Drying Rates”, Drying Technology, Vol. 21, No. 9, pp. 1867–1884, 2003.
[9] Hornik, K.; Stinchombe, M.; White, H, "Multilayer feed forward network are universal approximator”, Neural Network 1989, 2, 359–366.
[10] NeuralWorks Reference Guide, Software Reference for Professional II/Plus and NeuralWorks Explorer, Neural Ware Inc.: Pittsburgh, PA, 1993.
[11] Atashy.H, J. Rahnama-Rad, M. Fallahnejad., "An Investigation on the Improvement of Zinc Extraction From Siliceous Concentrates”, International Journal of Engineering, 2008, pp. 9-16.