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Prediction of coal response to froth flotation based on coal analysis using regression and artificial neural network / E. Jorjani in Minerals engineering, Vol. 22 N° 11 (Octobre 2009) 

Public ISBD [article]  in Minerals engineering > Vol. 22 N° 11 (Octobre 2009) . - pp. 970–976 Titre : Prediction of coal response to froth flotation based on coal analysis using regression and artificial neural network Type de document : texte imprimé Auteurs : E. Jorjani, Auteur ; H. Asadollahi Poorali, Auteur ; A. Sam, Auteur Année de publication : 2009 Article en page(s) : pp. 970–976 Note générale : Génie Minier Langues : Anglais (eng) Mots-clés : Coal  Neural  networks  Froth  flotation  Modeling Résumé : 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. DEWEY : 622 ISSN : 0892-6875 En ligne : http://www.sciencedirect.com/science/article/pii/S0892687509000776 [article] Prediction of coal response to froth flotation based on coal analysis using regression and artificial neural network [texte imprimé] / E. Jorjani, Auteur ; H. Asadollahi Poorali, Auteur ; A. Sam, Auteur . - 2009 . - pp. 970–976. Génie Minier Langues : Anglais (eng) in Minerals engineering > Vol. 22 N° 11 (Octobre 2009) . - pp. 970–976 Mots-clés : Coal  Neural  networks  Froth  flotation  Modeling Résumé : 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. DEWEY : 622 ISSN : 0892-6875 En ligne : http://www.sciencedirect.com/science/article/pii/S0892687509000776