Modeling and Prediction of Zinc Extraction Efficiency from Concentrate by Operating Condition and Using Artificial Neural Networks
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.
[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.
[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.
@article{"International Journal of Chemical, Materials and Biomolecular Sciences:66053", author = "S. Mousavian and D. Ashouri and F. Mousavian and V. Nikkhah Rashidabad and N. Ghazinia", title = "Modeling and Prediction of Zinc Extraction Efficiency from Concentrate by Operating Condition and Using Artificial Neural Networks ", 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.", volume = "8", number = "1", pages = "5-4", }
