Prediction of Final Concentrate Grade Using Artificial Neural Networks from Gol-E-Gohar Iron Ore Plant

Seyed Hamid Hosseini^{1, 2,} and Mahdi Samanipour¹

¹Mining Engineering Department, Islamic Azad University, Tehran South Branch, Tehran, Iran

²Department of Mining, Metallurgical and Materials Engineering, Laval University, Pavillon Adrien-Pouliot, Quebec, QC G1K 7P4 Canada

Cite this paper:
Seyed Hamid Hosseini and Mahdi Samanipour. Prediction of Final Concentrate Grade Using Artificial Neural Networks from Gol-E-Gohar Iron Ore Plant. American Journal of Mining and Metallurgy. 2015; 3(3):58-62. doi: 10.12691/ajmm-3-3-1

Abstract

In this study, the artificial neural networks methods were used to predict the iron, phosphor, sulfur and iron oxide content of final concentrate from the Gol-E-Gohar iron plant, Kerman province, Iran. The particle size (d₈₀), iron, phosphor, sulfur and iron oxide percentages of run of mine (R.O.M) were used as the inputs for the network. Feed-forward artificial neural networks (FANNs), with 5-8-7-7-4 and 5-8-8-6-4 arrangements were used to estimate the final concentrate grade in both wet and dry magnetic separation processes. The outputs of the models were the iron, iron oxide, phosphor and sulfur content of the final concentrate. Satisfactory correlations of R² = 0.98 were achieved in training and testing stages for the wet magnetic process prediction. The proposed neural network model, as an alternative to the simulation method, can be used accurately to determine the effects of changes in feed and concentrate grade of Gol-E-Gohar iron ore plant in dry and wet magnetic processes.

Keywords:
prediction FANN phosphor sulfur Gol-E-Gohar

This work is licensed under a Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

Figures

References:

[1]	A.P Maxwell, B. Denby, W. Pitts, The Application of Neural Networks to Size Analysis of Minerals on Conveyors. 25th International Symposium on the Application of Computers and Operations Research in the Minerals Industries (APCOM), Brisbane, Australia, 1995.
[2]	M. Oja, and L. Nystöm, The Use of Self-Organising Maps in Particle Shape Quantification. In: Hoberg, H., and von Blottnitz, H., (eds), Proceedings of the XX International Mineral Processing Congress, Vol. 1, Aachen, Germany, 1997, pp. 141-150.
[3]	J.S.J. Van Deventer, M. Bezuidenhout, D.W. Moolman, On-Line Visualisation of Flotation Performance Using Neural Computer Vision of the Froth Texture. In: Hoberg, H., and von Blottnitz, H., (eds), Proceedings of the XX International Mineral Processing Congress, Vol. 1, Aachen, Germany, 1997, pp. 315-326.
[4]	E.C. Cilek, Application of neural networks to predict locked cycle flotation test results. Minerals Engineering. 15(2002), pp. 1095-1104.
[5]	J. Labidi, M.A. Pelach, X. Turon, P. Mutje, Predicting flotation efficiency using neural networks. Chem. Eng. Process. 46(2007), pp. 314-322.
[6]	K.R.P. Petersen, L. Lorenzen, Gold Liberation Modelling Using Neural Network Analysis of Diagnostic Leaching Data. In: Hoberg, H., and von Blottnitz, H., (eds), Proceedings of the XX International Mineral Processing Congress, Vol. 1, Aachen, Germany, 1997, pp. 391-400.
[7]	K. Ghasemi, Dry magnetic separator performance reviews in Gol-E-Gohar Iron Ore Processing Circuit. Master Thesis, School of Mining Engineering, Amirkabir University of Technology, (2004), pp.24-28.
[8]	S. Haykin, Neural Networks, a comprehensive foundation, USA. 2nd edition. USA: Prentice Hall; 1999.
[9]	W. Zhao, D. Chen, S. Hu, Optimizing operating conditions based on ANN and modified Gas. Computers and Chemical Engineering 24(2000), pp 61–65.
[10]	R .R. Trippi, E. Turban, Neural networks in finance and investing-using artificial intelligence to improve real-world performance. Revised edition. New York: McGraw-Hill, 1996.
[11]	M. M. Gupta, L. Jin, N. Homma, Static and dynamic neural networks: From fundamental to advanced theory, John Wiley & Sons Ltd., USA, 2003.
[12]	M. T. Hagan, H. B. Demuth, M.H. Beale, Neural network design, Thomson Leaning, Singapore, 2002.
[13]	Q. Zhu, J. M. Jones, A. Williams, and K. M. Thomas, The predictions of coal/char combustion rate using an artificial neural network approach, Fuel, 78(1999), p.1755.