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<p>The beneficiation of low-grade iron ore (39.5% Fe<sub>(T)</sub> grade) using a dry-roll magnetic separator was investigated. The ore was characterized using Mineral Liberation Analysis (MLA). It was determined that the ore was composed of iron oxide (goethite and hematite), quartz, chlorite, muscovite, plagioclase, and other minerals. The effect of particle size (PS, − 1 + 0.500&#xa0;mm, − 0.500 + 0.300&#xa0;mm, and − 0.300 + 0.125&#xa0;mm), splitter position (SP, 43° and 58°), cleaning stage (CS, 1 and 2), conveyor speed (CoS, 3, 5, and 7&#xa0;Hz), magnetic field strength (MFS, 0.2&#xa0;T and 0.4&#xa0;T) on the recovery of the magnetic product was investigated. Experimental results show that the product (− 1 + 0.500&#xa0;mm) with the Fe<sub>(T)</sub> grade of 67.67% can be obtained, but its recovery was not at an acceptable value (&lt; 30%). Furthermore, the Fe<sub>(T)</sub> grade of the product (− 0.500 + 0.300 and − 0.300 + 0.125&#xa0;mm) could not reach satisfactory levels<b>.</b> The artificial neural network (ANN) method was conducted on the results of experimental studies. Three different training algorithms were employed for modeling, and their performance was assessed using statistical evaluation criteria. The results demonstrate that Bayesian Regularization (BR) algorithm exhibited better performance compared to others in predicting both Fe(T) grade and recovery rate during the testing phase. These findings support the notion that ANN algorithms can be a powerful modeling and prediction tool in the field of mineral processing.</p> Graphical Abstract <p></p>

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Beneficiation of Low-Grade Iron Ore Using a Dry-Roll Magnetic Separator and its Modeling via Artificial Neural Network

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年2月24日

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  • Abdourahman Hassan Brahim Fariss,
  • Ahmedaljaali Ibrahim Idrees Ibrahim,
  • Ali Can Ozdemir,
  • Soner Top,
  • Sait Kursunoglu,
  • Mahmut Altiner

摘要

The beneficiation of low-grade iron ore (39.5% Fe(T) grade) using a dry-roll magnetic separator was investigated. The ore was characterized using Mineral Liberation Analysis (MLA). It was determined that the ore was composed of iron oxide (goethite and hematite), quartz, chlorite, muscovite, plagioclase, and other minerals. The effect of particle size (PS, − 1 + 0.500 mm, − 0.500 + 0.300 mm, and − 0.300 + 0.125 mm), splitter position (SP, 43° and 58°), cleaning stage (CS, 1 and 2), conveyor speed (CoS, 3, 5, and 7 Hz), magnetic field strength (MFS, 0.2 T and 0.4 T) on the recovery of the magnetic product was investigated. Experimental results show that the product (− 1 + 0.500 mm) with the Fe(T) grade of 67.67% can be obtained, but its recovery was not at an acceptable value (< 30%). Furthermore, the Fe(T) grade of the product (− 0.500 + 0.300 and − 0.300 + 0.125 mm) could not reach satisfactory levels. The artificial neural network (ANN) method was conducted on the results of experimental studies. Three different training algorithms were employed for modeling, and their performance was assessed using statistical evaluation criteria. The results demonstrate that Bayesian Regularization (BR) algorithm exhibited better performance compared to others in predicting both Fe(T) grade and recovery rate during the testing phase. These findings support the notion that ANN algorithms can be a powerful modeling and prediction tool in the field of mineral processing.

Graphical Abstract