Study of parameters affecting on copper recovery from oxide ores by column leaching using Artificial Neural Network

Document Type : Research Article

Authors

1 M.Sc. student, Department of Mining and Metallurgical Eng., Amirkabir University of Technology

2 Associate Professor, Department of Mining and Metallurgical Eng., Amirkabir University of Technology

3 M.Sc., Iran Mineral Processing Research Center (IMPRC), Karaj

4 Assistant Professor, Department of Mining Eng., Kashan University, Esfahan

Abstract

In this study, Artificial Neural Network was used for predicting optimized conditions of column leaching on copper oxide ore. Optimization, control and analysis of heap leaching implicate an accurate, proper and comprehensive modeling. Providing such models need to identify all the effective parameters in process and the impact of these factors simultaneously on the output of a process. Important parameters such as height of column, particle’s sizes, acid flow rate and leaching duration were studied and it was investigated their impacts on recovery of copper. Experiments were performed in three columns with the heights of 2, 4 and 6 meters and in the particle size distributions of 25.4 and 50.8 mm. The results showed that the copper recovery has an inverse relation with the column height and particle sizes. This relation is direct with leaching duration and acid rate. The copper recovery obtained in the columns with heights of 2, 4 and 6m were 78.63%, 66.27%, and 52.89% respectively. According to the results, the Trained ANN modeling predicts the copper recovery based on operation conditions.

Keywords

References

[1]خدادادی، احمد؛ کلینی، سید جواد؛ ربیعه، علیرضا؛ ”مدل سازی ریاضی و طراحی نرم افزار مناسب جهت هیپ لیچینگ کانی های اکسیده مس“،.، نشریه علمی پژوهشی مهندسی معدن، شماره هفتم، 43-23، 1388
[2] Wu, A., Yin, S., Yang, B., Wang, J. and Qiu, G., “Study on preferential flow in dump leaching of low-grade
ores”, Hydrometallurgy, vol. 87, pp. 124- 132, 2007.
[3] Yorio, C., Betancourt, E., Vivas, R. and Rus, J., “Ni, Co recovery study and Fe by acid leaching in columns”,
Revista de Metalurgia, vol. 42, pp. 41- 48, 2006.
[4] Petersen, J. and Dixon, D.G., “Modeling zinc heap bioleaching”, Hydrometallurgy, vol. 85, pp. 127- 143, 2007.
[5] Leahy, M.J., Davidson, M.R. and Schwarz M.P., “A model for heap bioleaching of chalcocite with heat
balance, mesophiles and moderate thermophiles”,Hydrometallurgy, vol. 85, pp. 24- 41. 2007.
[6] Mellado, M. E., Gálvez, E. D. and Cisternas, L. A.,“On the optimization of flow rates on copper haep
leaching operations”, Int. J. of Mineral processing, vol. 101, pp. 75- 80, 2011.
[7] Padilla, G. A., Cisternas, L. A. and Cueto, J. Y., “On the optimization of heap leaching”, Minerals processing,
vol. 21, pp. 673- 678, 2008.
[8] Mellado, M. E., Gálvez, E. D., and Cisternas, L. A.,“Stochastic analysis of heap leaching process via
analytical models”, Mineral Engineering, vol. 33, pp.93- 98, 2012.
[9] Wadswort, M.E. and Miller, J.D., “Rate processes of extractive metallurgy In: Hydrometallurgical
processes: section 3”, Plenum Press, New York, pp.133– 153, 1979.
[10] Ekmekyapar, A., Oya, R. and Künkül, A., “Dissolution kinetics of an oxidized copper ore in ammonium
chloride solution”, Chemical and Biochemical Engineering Quarterly, vol. 17, pp. 261–266, 2003.
[11] Koleini, S.M. J. and Khodadadi, A., “A Study on Leaching Behaviour of Copper Oxide Ore of Sarcheshmeh Mine”, International seminar on Mineral Processing Technology MPT, 2007.
[12] Bouffard, S. C. and Dixon, D. G., “Investigative study into the hydrodynamics of heap leaching processes”,
Metallurgical and Material Transactions, vol. 32, pp.763– 776, 2001.
[13] Dehghani, H. and Ataee-pour, M., “Development of a model to predict peak particle velocity in a blasting
operation”, International Journal of Rock Mechanics and Mining Sciences, vol. 48, pp. 51– 58, 2011.
[14] Demir, F., Türkmen M. and Tekeli, H., “A new way for prediction of elastic modulus of normal and high
strength concrete : artificial neural networks”, Intelligent Manufacturing Systems, pp. 208- 215, 2006.
[15] Cilek, E. C., “Application of Neural Networks to Predict Locked Cycle Flotation Test Results”, Minerals
Engineering, 15, pp. 1095- 1104, 2002.
[16] Massinaei, M. and Doostmohammadi R., “Modeling of Bubble Surface Area Flux in an Industrial Rougher
Column using Artificial Neural Network and Statistical Techniques”, Minerals Engineering, vol. 23, pp. 83-
90, 2010.
[17] Sawmliana, C., Roy, P. P., Singh, R. K. and Singh, T.N., “Blast induced air overpressure and its prediction
using artificial neural network” International Journal of Mining Technology, vo. 116, no. 2, pp. 41– 48, 2007.
[18] Monjezi, M., Ghafurikalajahim, M. and Bahrami, A., “Prediction of blast-induced ground vibration using
artificial neural networks”, Tunnelling and Underground Space Technology, vol. 26, pp. 46– 50, 2011.
[19] Bakhshandeh Amnieh, H., Mozdianfard, M.R. and Siamaki, A., “Predicting of blasting vibrations in
Sarcheshme copper mine by neural network”, Safety Science, vol. 48, pp. 319– 325. 2010.
[20] Khandelwal, M. and Singh, T.N., “Prediction of blast-induced ground vibration using artificial neural
network”, International Journal of Rock Mechanics and Mining Sciences, vol. 46, pp. 1214– 1222, 2009.
[21] Bakhshandeh Amnieh, H., Siamaki A. and Soltani S., “Design of blasting pattern in proportion to the
peak particle velocity (PPV): Artificial neural networks approach”, Safety Science, vol. 50, pp. 1913– 1916, 2012.
[22] Petr, V., Simoes, M.G. and Rozgonoyi, T.G., “Future Development of Neural Network Prediction for
Blasting Design Parameter of Production Blasting”,Explosive and Blasting Technique, Holmberg, pp.
625– 630, 2003.

Files

Share

How to cite

Statistics