Effects of Collectors and Frothers on Copper and Molybdenum Coarse Particle Recoveries—A Statistical Approach
Bibliographic record
Abstract
Typically, coarse dense mineral particles greater than 150 μm are difficult to float, and the recovery decreases progressively. Various physical parameters can be manipulated in an attempt to increase the recovery. These physical parameters are the following: liberation, turbulence in the flotation cell, pH, collector, frother type and dosage. The testwork discussed in this paper was performed for a copper-molybdenum operation that is experiencing coarse particle (>150 μm) losses in the tails. This operation uses Diesel No. 2 fuel and sodium ethyl xanthate for molybdenum and copper flotation, respectively and X-133 frother. In an attempt to increase coarse particle recovery, stronger collectors (potassium amyl xanthate, Aero 249 and Aero 3501) and frothers (FrothPro 618, FrothPro 630 and FrothPro 706) were used. The analysis was performed using the Analysis of Variance (ANOVA) approach. The conditions required by the ANOVA method were met. The results showed that the collector potassium amyl xanthate (PAX) with frothers X-133 and FrothPro 630 resulted in approximately 3% increase in copper rougher recovery relative to the baseline (sodium ethyl xanthate and X-133). The collectors and frothers did not have a significant effect on molybdenum recovery within the dosage limits investigated.
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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 | 0.011 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".