Prediction of coal response to froth flotation based on coal analysis using regression and artificial neural network

被引：57

作者：

Jorjani, E. ^{[1
]}

Poorali, H. Asadollahi ^{[1
]}

Sam, A. ^{[2
]}

Chelgani, S. Chehreh ^{[1
]}

Mesroghli, Sh. ^{[1
]}

Shayestehfar, M. R. ^{[2
]}

机构：

[1] Islamic Azad Univ, Dept Min Engn, Sci & Res Branch, Tehran, Iran

[2] Shahid Bahonar Univ Kerman, Dept Min Engn, Kerman, Iran

来源：

MINERALS ENGINEERING | 2009年 / 22卷 / 11期

关键词：

Coal; Neural networks; Froth flotation; Modeling; DESULFURIZATION;

D O I：

10.1016/j.mineng.2009.03.003

中图分类号：

TQ [化学工业];

学科分类号：

081705 [工业催化];

摘要：

In this paper, the combustible value (i.e. 100-Ash) and combustible recovery of coal flotation concentrate were predicted by regression and artificial neural network based on proximate and group macerals analysis. The regression method shows that the relationships between (a) In (ash), volatile matter and moisture (b) In (ash), In (liptinite), fusinite and vitrinite with combustible value can achieve the correlation coefficients (R-2) of 0.8 and 0.79, respectively. In addition, the input sets of (c) ash, volatile matter and moisture (d) ash, liptinite and fusinite can predict the combustible recovery with the correlation coefficients of 0.84 and 0.63, respectively. Feed-forward artificial neural network with 6-8-12-11-2-1 arrangement for moisture, ash and volatile matter input set was capable to estimate both combustible value and combustible recovery with correlation of 0.95. It was shown that the proposed neural network model could accurately reproduce all the effects of proximate and group macerals analysis on coal flotation system. (C) 2009 Elsevier Ltd. All rights reserved.

引用

页码：970 / 976

页数：7

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