Nickel, copper and cobalt coalescence in copper cliff converter slag
Bibliographic record
Abstract
The aim of this investigation is to assess the effect of various additives on coalescence of nickel, copper and cobalt from slags generated during nickel extraction. The analyzed fluxes were silica and lime while examined reductants were pig iron, ferrosilicon and copper-silicon compound. Slag was settled at the different holding temperatures for various times in conditions that simulated the industrial environment. The newly formed matte and slag were characterized by their chemical composition and morphology. Silica flux generated higher partition coefficients for nickel and copper than the addition of lime. Additives used as reducing agents had higher valuable metal recovery rates and corresponding partition coefficients than fluxes. Microstructural studies showed that slag formed after adding reductants consisted of primarily fayalite, with some minute traces of magnetite as the secondary phase. Addition of 5 wt% of pig iron, ferrosilicon and copper-silicon alloys favored the formation of a metallized matte which increased Cu, Ni and Co recoveries. Addition of copper-silicon alloys with low silicon content was efficient in copper recovery but coalescence of the other metals was low. Slag treated with the ferrosilicon facilitated the highest cobalt recovery while copper-silicon alloys with silicon content above 10 wt% resulted in high coalescence of nickel and copper, 87 % and 72 % respectively.
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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".