Flotation separation of copper and nickel sulphides: Status and research needs
Bibliographic record
Abstract
Polymetallic Ni-Cu sulfide ores are a major source of Ni, Cu and platinum group metals (PGM), with pentlandite ((Fe,Ni) 9 S 8 ) as the main Ni-bearing mineral, chalcopyrite (CuFeS 2 ) as the Cu-bearing mineral, and pyrrhotite (Fe (1-x) S) as the main sulphide gangue. Flotation separation of chalcopyrite from pentlandite has been accomplished commercially by depressing pentlandite using lime at high pH, sulphur dioxide combined with diethylenetriamine, or sodium cyanide. Dextrin (a polysaccharide) was used as a selective depressant for Ni sulphides in the differential flotation of Cu-Ni bulk concentrates at the Kotalahti Mine in Finland until the mine was closed in 1987. In the massive Cu-Ni sulphide reserves in the Sudbury region, the occurrence of another Ni sulphide mineral, millerite (NiS), has been frequently observed. Millerite is readily floatable and not depressed by the typical pentlandite depressants. Recent laboratory tests on high-purity single minerals have shown that dextrin can selectively depress millerite, whereas chalcopyrite remains unaffected. However, tests on real ores have been inconclusive. Since polysaccharide depressants adsorb on mineral surfaces through acid-base interaction with metal hydroxyl species on the mineral surfaces, cross-contamination of the mineral surfaces could nullify the selectivity of adsorption. Also, fine and ultrafine mineral particles can enter flotation concentrates by mechanical entrainment. Therefore, future research should focus on addressing the selectivity problem of polysaccharides in Cu-Ni sulphide flotation, as well as leveraging the functions of polysaccharides with different molecular weights to reduce the entrainment of ultrafine nickel sulphides in the copper concentrate.
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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 | 0.008 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".