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Variable importance assessments of an innovative industrial-scale magnetic separator for processing of iron ore tailings
Mining and Metallurgical Engineering Department, Yazd University, Yazd, Iran. Rahbar Farayand Arya Company (RFACo), Tehran, Iran.
School of Mining Engineering, University of Tehran, Tehran, Iran.
Department of Computer Engineering, Islamic Azad University North Tehran Branch, Tehran, Iran.
Electrical and Electronics Engineering Department, Shahed University, Tehran, Iran.
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2020 (English)In: Mineral Processing and Extractive Metallurgy: Transactions of the Institute of Mining and Metallurgy, ISSN 2572-6641, E-ISSN 2572-665X, Vol. 131, no 2, p. 122-129Article in journal (Refereed) Published
Abstract [en]

Reprocessing of iron ore tailings (IOTs) and extracting recoverable valuable iron oxides will become increasingly financially attractive for mining companies and also may reduce environmental problems. Using databases built based on long term monitoring of units installed on plants to control the operational conditions to generate artificial intelligence models can decrease the cost of reprocessing operations Although some investigations have been focused on the reprocessing of IOTs, several challenges still remain which need to be addressed, especially for fine particles. SLon®, has developed a pulsating high gradient magnetic separator for the processing of fine iron oxides. However, there has been no systematic optimisation and variable assessments for SLon® operating variables to examine their effects on metallurgical responses (separation efficiency) on the industrial scale. This study addressed these drawbacks by linear (Pearson correlation) and non-linear (random forest) variable importance measurements (VIM) through an industrial SLon® installation.

Place, publisher, year, edition, pages
Taylor & Francis, 2020. Vol. 131, no 2, p. 122-129
Keywords [en]
Iron ore tailings, random forest, Pearson correlation, reprocessing
National Category
Metallurgy and Metallic Materials
Research subject
Mineral Processing
Identifiers
URN: urn:nbn:se:ltu:diva-80976DOI: 10.1080/25726641.2020.1827674ISI: 000574483400001Scopus ID: 2-s2.0-85091680809OAI: oai:DiVA.org:ltu-80976DiVA, id: diva2:1471640
Note

Validerad;2022;Nivå 2;2022-06-29 (sofila)

Available from: 2020-09-29 Created: 2020-09-29 Last updated: 2023-09-05Bibliographically approved

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Chelgani, Saeed Chehreh

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