WoS İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/394
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Article Citation - WoS: 6Citation - Scopus: 6Concentration Study of a Specularite Ore via Shaking Table, Reverse Flotation, and Microwave-Assisted Magnetic Separation(Taylor & Francis inc, 2022-11-03) Al-Dhubaibi, Ammar Mahdi Ahmed; Vapur, Huseyin; Top, Soner; Sivrikaya, OsmanDespite the difficulties in pelletizing specularite-type refractory iron ores, the utilization of these resources is indispensable for the steel industry due to the increasing need for iron. This study investigated Fe recovery from a refractory iron ore using gravity separation, reverse flotation, and two-stage magnetic separation. Tilt angle and particle size had a significant effect on the grade and recovery of concentrates in shaking table tests. Gravity concentration at optimum conditions resulted in an iron concentrate with 64.47% Fe grade and 90.73% Fe recovery. In the reverse flotation tests, the frother and depressant substantially affected the Fe grade of concentrates while the collector influenced the Fe recovery. A 90% Fe recovery with 64.69% Fe grade was obtained within optimum flotation conditions. The Fe grades were raised to >67.5% in products after the first magnetic separation. The tailings of the first magnetic separation were subjected to the second magnetic separation after microwave-assisted roasting to increase the magnetic susceptibility. In the second magnetic separation, a concentrate containing 66.06% Fe was separated from the microwave-roasted non-magnetic material with 82.23% Fe recovery. To the best of our knowledge, the microwave-roasting method has been applied to a specularite-type refractory iron ore for the first time.Article Citation - WoS: 4Citation - Scopus: 4Beneficiation of Low-Grade Iron Ore Using a Dry-Roll Magnetic Separator and Its Modeling via Artificial Neural Network(Springer, 2025-02-24) Fariss, Abdourahman Hassan Brahim; Ibrahim, Ahmedaljaali Ibrahim Idrees; Ozdemir, Ali Can; Top, Soner; Kursunoglu, Sait; Altiner, MahmutThe 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 degrees and 58 degrees), 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<bold>.</bold> 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.