Investigations of Monomineralic Flotation of Galena, Sphalerite, and Pyrite at Different Temperatures
Bibliographic record
Abstract
Temperature variations could potentially impact flotation performance, leading to operational and economic issues. Several historical and currently operating plants across Canada have reported seasonal metal losses as one of their major challenges. Improvements in flotation efficiency at cold temperatures are important for the sustainable development of the industry, especially in view of expanding mining operations to the north and the increasing impact of climate change-originated weather extremes on mining operations. To better understand the driving mechanisms of the seasonal metallurgical variation, the degree of vulnerability of froth flotation to fluctuations in temperature, and to develop recommendations addressing seasonally driven losses, a series of mono-mineral flotation tests were conducted. The observed variations in yield and recovery kinetics for different sulfide minerals follow a similar pattern: higher recovery and slower kinetics at lower temperatures, and lower recoveries and faster kinetics at higher temperature conditions. Following the results of the flotation tests, foam height and stability were investigated at different temperatures to explain the observed variations. A high correlation between changes in mono-mineral flotation behavior and foam properties with temperature was revealed.
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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".