The mineralogy of pyrrhotite from Sudbury CCN and Phoenix nickel ores and its effect on flotation performance
Bibliographic record
Abstract
The non‐stoichiometric sulphide pyrrhotite (Fe1−xS), common to many nickel ores, occurs in a variety of crystallographic forms and compositions. In order to manipulate its performance in nickel processing operations either to target the recovery or rejection or pyrrhotite, one needs an understanding of pyrrhotite mineralogy, reactivity and the effect this may have on its flotation performance. In this study, a non‐magnetic Fe9S10 pyrrhotite from Sudbury CCN in Canada and a magnetic Fe7S8 pyrrhotite from Phoenix in Botswana were selected to explore the relationship between mineralogy, reactivity and microflotation. Non‐magnetic Sudbury pyrrhotite was less reactive in terms of its oxygen uptake and showed the best collectorless flotation recovery. Magnetic Phoenix pyrrhotite was more reactive and showed poor collectorless flotation, which was significantly improved with the addition of xanthate and copper activation. These differences in reactivity and flotation performance are interpreted to be a result of the pyrrhotite mineralogy, the implications of which may aid in the manipulation of flotation performance.La pyrrhotine sulfureuse non stoechiométrique (Fe1−xS), commune à plusieurs minerais de nickel, se retrouve sous une variété de formes cristallographiques et de compositions. Afin de manipuler son rendement lors des opérations de traitement du nickel, soit afin de viser la récupération ou bien la rejection de la pyrrhotine, on a besoin de comprendre la minéralogie de la pyrrhotine, sa réactivité et leur effet sur le rendement de sa flottation. Dans cette étude, on a choisi une pyrrhotine non magnétique, Fe9S10, de Sudbury CCN au Canada et une pyrrhotine magnétique, Fe7S8, de Phoenix au Botswana, pour explorer la relation entre la minéralogie, la réactivité et la microflottation. La pyrrhotine non magnétique de Sudbury était moins réactive par rapport à son absorption d'oxygène et a montré la meilleure récupération par flottation sans collecteur. La pyrrhotine magnétique de Phoenix était plus réactive et a montré une mauvaise flottation sans collecteur, que l'on a améliorée significativement avec l'addition de xanthate et l'activation au cuivre. Ces différences de réactivité et de rendement de la flottation sont interprétées comme étant un résultat de la minéralogie de la pyrrhotine, dont les implications peuvent aider à manipuler le rendement de la flottation.
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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) | 0.001 | 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 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".