A review of the physicochemical properties and flotation of pyrrhotite superstructures (4C – Fe<sub>7</sub>S<sub>8</sub>/ 5C – Fe<sub>9</sub>S<sub>10</sub>) in Ni‐Cu sulphide mineral processing
Bibliographic record
Abstract
Abstract Pyrrhotite is one of the most abundant sulphide gangue minerals in base metal mining operations. It has little economic value and is a significant contributor of SO2 emissions at the smelting stage, thus it is rejected in most concentrators, requiring a concerted effort to ensure it is well separated from valuable minerals. The mineral processing of pyrrhotite is generally understood, however it has many complex crystallographic structures (superstructures) that behave differently in the flotation pulp for which the information is sparse and dispersed throughout the literature. Traditionally, the superstructures were largely isolated into magnetic/non–magnetic circuits and floated separately. Many operations have now transitioned away from magnetic separators, and treat the superstructures in the same flotation circuit where their flotation responses are quite different. Historically, it was perceived that the superstructures behaved virtually the same; whereas, recent work has clearly demonstrated that there are quantifiable differences in their flotation responses. The available information is scattered in the literature and is also plagued with many contradictions, making it very difficult for researchers to retrieve relevant information pertaining to the superstructures. This review aims to reduce the confusion by summarizing the superstructure nomenclature, characterization methods, aqueous stability, surface charges and superstructure‐reagent interactions, and available flotation data.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".