کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
234164 | 465390 | 2009 | 7 صفحه PDF | دانلود رایگان |
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) ln (ash), volatile matter and moisture (b) ln (ash), ln (liptinite), fusinite and vitrinite with combustible value can achieve the correlation coefficients (R2) 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.
Journal: Minerals Engineering - Volume 22, Issue 11, October 2009, Pages 970–976