Challenges associated with the recovery of Co– and As-bearing minerals from aged mine tailings
Bibliographic record
Abstract
• Gravity separation and flotation were used to recover Co– and As–bearing minerals. • Co and As recovery from aged mine tailings is challenging due to secondary minerals. • Knelson and Mozley table concentrated Co and As efficiently but had low recovery. • Flotation using KAX and hydroxamic acid is efficient in recovering Co and As. • Sonication pre-treatment slightly enhanced flotation performance. The global demand for cobalt (Co), essential for “clean” energy technologies, has raised interest in identifying secondary sources, including mine tailings. This study evaluates the potential of historic silver (Ag) mine tailings in Ontario, Canada, as a secondary Co source and for arsenic (As) mitigation, offering economic and environmental benefits. Physico–chemical and mineralogical characterization revealed promising Co (1 310 mg/kg) and As (5 245 mg/kg) contents in fine silty tailings (D 80 = 55 μm), with key Co-As-bearing minerals (e.g., safflorite, skutterudite, cobaltite, erythrite) exhibiting significant alteration and association with silicates (i.e., albite, quartz, chlorite). The complex mineralogy and fine particle size are challenging for conventional processing methods. Tests using gravity separation achieved limited Co and As recoveries (4.2% and 7.3%, respectively), despite effective preconcentration (x24.8 and x38, respectively). Flotation experiments, performed in Denver cell with xanthate and hydroxamic acid collectors, achieved concentration factors of 2.5 for Co (70% recovery) and 3.0 for As (80% recovery). Pre-treatment with sonication further enhanced flotation efficiency. Analysis of entrainment index and particle size distribution emphasized the role of hydroxamate in particle recovery. The study highlights the need for innovative processing strategies to overcome challenges posed by fine particle size, mineral alteration, and complex associations. However, Co grades comparable to global smelter concentrates were achieved, suggesting the potential for sustainable reprocessing of aged mine tailings. Future research should focus on optimizing reprocessing techniques to enhance resource efficiency and sustainability.
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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".