Optimization of Line of Magnetite Recovery from Wet Tailings by Creating Second Medium Intensity Magnetic Field (Case Study: Processing Plant of Gol-e-Gohar Hematite)
Why this work is in the frame
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Bibliographic record
Abstract
The primary raw material of the steel industry is iron. This paper aims to optimize magnetite recovery from wet tailings by increasing the iron content in the concentrate of the line. To manage tailings, a Wet Tailing Processing (WTP) line constructed at Gol-e-Gohar Iron Ore Company to recover the magnetite. The dominant crystalline phases in these tailings were quartz, albite, talc, hematite, and calcite. The line feed is 45 microns, which is not suitable for the gravity method. Thus, separation can achieve using only the magnetic method. Because of the high iron content in the tailings, a wet magnetic separator is used. According to the results, the proposed medium-intensity separator and the associated circuit modifications increase iron recovery from 7 to 30 percent; resulting in 150 tons of annual production; preventing loss of iron through concentrator plant tailings, and increasing the Blain number by 50 to 100 units in the hematite plant. Furthermore, water consumption is significantly reduced by replacing old wet tailings of the concentrator plant with new wet tailings as the feed, which is another significant achievement of this research. Instead of fresh water, saline water with flow rate of 250 cubic meters per hour are used.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it