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ArticleName Modeling of dry magnetic separation results for weakly magnetic minerals processed in roll separators
DOI 10.17580/or.2024.01.04
ArticleAuthor Pelevin A. E.

Ural State Mining University (Ekaterinburg, Russia)

Pelevin A. E., Professor, Doctor of Engineering Sciences, Associate Professor,


This paper studies the effects of the concentration of weakly magnetic minerals in the initial product on the separation performance in a roll magnetic separator. Higher ilmenite concentrations in the magnetic separator feed lead to higher magnetic product yields and higher ilmenite concentrations in the magnetic product while simultaneously increasing the ilmenite content in the non-magnetic product. Regression models were developed that allow calculating the process indicators for a single magnetic separation operation depending on the ilmenite content in the feed. Models were also designed to calculate the magnetic product yield and the ilmenite content in the non-magnetic product. The average absolute errors in predicting the process indicators by models were between 0.49 and 1.30 %. Mass balance equations were used to calculate other indicators. The paper presents the results of mathematical modeling of linear and closed-cycle processes to produce an ilmenite concentrate with a mass fraction of TiO2 exceeding 46 %. A process with one rougher and two scavenger operations should be used for the processing of high-grade initial products with a mass fraction of ilmenite of 12–75 %. For a low-grade initial product with a mass fraction of ilmenite of 1.5–12 %, a closed processing cycle should be used, with the non-magnetic product of the cleaner operation fed into the rougher operation. An iterative calculation method should be used to model closed-cycle processes. Modeling allows calculating the separation indicators for various process flows and selecting the most promising options for further experimental research and trade-off studies.

keywords Dry magnetic separation, weakly magnetic minerals, ilmenite content in the initial product, concentrate yield, processing tailings, regression model, process flow modeling

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