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Prediction of operational parameters effect on coal flotation using artificial neural network
Department of Mining Engineering, Science and Research Branch, Islamic Azad University, Iran.
Department of Mining Engineering, Science and Research Branch, Islamic Azad University, Iran.
Department of Mining Engineering, Science and Research Branch, Islamic Azad University, Iran.ORCID iD: 0000-0002-2265-6321
2008 (English)In: Journal of University of Science and Technology Beijing: Mineral Metallurgy Materials (Eng Ed), ISSN 1005-8850, Vol. 15, no 5, p. 528-533Article in journal (Refereed) Published
Abstract [en]

Artificial neural network procedures were used to predict the combustible value (i.e. 100-Ash) and combustible recovery of coal flotation concentrate in different operational conditions. The pulp density, pH, rotation rate, coal particle size, dosage of collector, frother and conditioner were used as inputs to the network. Feed-forward artificial neural networks with 5-30-2-1 and 7-10-3-1 arrangements were capable to estimate the combustible value and combustible recovery of coal flotation concentrate respectively as the outputs. Quite satisfactory correlations of 1 and 0.91 in training and testing stages for combustible value and of 1 and 0.95 in training and testing stages for combustible recovery prediction were achieved. The proposed neural network models can be used to determine the most advantageous operational conditions for the expected concentrate assay and recovery in the coal flotation process.

Place, publisher, year, edition, pages
2008. Vol. 15, no 5, p. 528-533
Keywords [en]
coal flotation, operational parameters, artificial neural networks, combustible recovery
National Category
Mineral and Mine Engineering
Identifiers
URN: urn:nbn:se:ltu:diva-72304DOI: 10.1016/S1005-8850(08)60099-7Scopus ID: 2-s2.0-58149277313OAI: oai:DiVA.org:ltu-72304DiVA, id: diva2:1272015
Available from: 2018-12-18 Created: 2018-12-18 Last updated: 2019-02-25

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Chelgani, Saeed Chehreh

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CiteExportLink to record
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  • apa
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  • nn-NB
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Output format
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