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)
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| 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) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".