Iron recovery from zinc mine tailings by magnetic separation followed by carbothermal reduction of self‐reducing briquettes
Bibliographic record
Abstract
Abstract The volume of tailings produced by the extractive industry has been increasing due to the processing of the low‐grade ore. This issue can cause environmental accidents and require significant investment to control the disposal of tailings. Therefore, this study aims to recover iron from zinc mine tailings by wet magnetic separation followed by the carbothermal reduction of self‐reducing briquettes. Two magnetic separation routes were investigated to concentrate iron. Zinc mine tailings were processed by route I, in a rougher stage followed by a scavenger stage; and route II, in a rougher stage followed by a cleaner stage. The carbothermal reductions were performed using self‐reducing briquettes composed of Fe concentrate from the route with high Fe content and charcoal. The products were analyzed by scanning electron microscopy with energy dispersive spectroscopy (SEM‐EDS), x‐ray diffraction (XRD), inductively coupled plasma optical emission spectrometry (ICP‐OES), and volumetric chemical analysis. Magnetic separation route II provided the highest‐grade Fe concentrate, 52% Fe, while route I provided 33% Fe. In the carbothermal reductions, a metallization degree of 98% in the Fe concentrate briquette, 97% in the briquette with a 10% replacement of its raw material by Fe concentrate, and 99% in the hematite briquette was reached. The replacement of raw material by Fe concentrate showed no significant change in Fe recovery. Considering the whole process, magnetic separation and carbothermal reduction, the recovery of Fe from the zinc mine tailings was 67%. Therefore, the process route suggested in this study will not only reduce tailings disposal and consequently the risk of environmental accidents, but it will also provide profitable raw material for the steel industry.
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How this classification was reachedexpand
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) | 0.000 | 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 teacher head, 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".