Optimizing Zinc Recovery in Sulfide Mineral Flotation for Geometallurgical Characterization
Bibliographic record
Abstract
This study aims to optimize zinc recovery from sulfide mineral flotation in the province of Oyón, Peru, by integrating detailed mineralogical characterization with a rigorous experimental design.Optical microscopy and TIMA electron microscopy identified marmatitic sphalerites with 17.24% distribution and 99.51% free species, along with gangue minerals such as epidote, garnet, and quartz.A three-factor experimental design evaluated ZnSO₄, NaCN dosage, and flotation pH to minimize zinc displacement during Pb-Cu bulk flotation, achieving a minimum Zn displacement of 5.14% with a 1.24% grade.Zn flotation tests optimized CuSO₄ and Xanthate Z-11 dosages, resulting in a zinc recovery of 97.61% and a concentrate grade of 48.06%.This comprehensive approach demonstrates significant improvements in zinc recovery and offers a replicable methodology for other mining regions with complex mineralogies, contributing to more sustainable and efficient mining practices.
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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 | 0.001 | 0.001 |
| 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".