تاثیر عوامل موثر بر بازیابی فروشویی ستونی کانسنگ اکسیدی مس با استفاده از شبکه عصبی مصنوعی

[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.