Gold Recovery from Sulfide Concentrates Produced by Environmental Desulfurization of Mine Tailings
Bibliographic record
Abstract
Environmental desulfurization is gaining attention as an efficient approach to manage sulfidic mine tailings by separating sulfide minerals from tailings. While extensive research has been done to reuse desulfurized tailings in mine site reclamation, the responsible management or potential of valorization of desulfurization concentrates did not receive as much attention. The objective of this study was to evaluate the potential to recover Au from desulfurization concentrates originating from active gold mine sites. Desulfurization concentrates were produced by froth flotation of tailings and characterized. Cyanidation tests and gravity separation experiments were performed on the concentrates. Desulfurization concentrates, mostly composed of fine particles, contained high amounts of S (between 23.0 and 34.5% S) and variable contents of Au (between 0.7 and 1.9 g/t). Mineralogical characterization by SEM showed that 99% of Au-bearing particles were locked, mainly in pyrite (94%). Cyanidation allowed the recovery of 13 to 78% of Au, depending on the desulfurization concentrate. Low Au-bearing particles liberation, and possibly the presence of Cu, were identified as parameters negatively affecting the Au recovery. Gravity separation was poorly efficient (<50%) in recovering Au, which is quite consistent with physical (small particle size) and mineralogical characterizations. A desulfurization concentrate reprocessing flowsheet is proposed.
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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) | 0.000 | 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 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".