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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.

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Article

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

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


American Journal of Mining and Metallurgy. 2015, Vol. 3 No. 3, 58-62
DOI: 10.12691/ajmm-3-3-1
Copyright © 2015 Science and Education Publishing

Cite this paper:
Seyed Hamid Hosseini, 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.

Correspondence to: Seyed  Hamid Hosseini, Mining Engineering Department, Islamic Azad University, Tehran South Branch, Tehran, Iran. Email: hoseini@azad.ac.ir

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.

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