Separation and Recovery of Valuable Metals from Nickel Slags Disposed in Landfills
Bibliographic record
Abstract
With the increased requests for more sustainable extraction processes feedstocks with low metal content are becoming more attractive. In this research, an additional refining step is investigated in order to recover valuable metals from slag generated during nickel extraction process, particularly copper, nickel, and cobalt. Slag was settled at the different temperatures for various times in conditions that simulated the industrial environment. The chemical composition and morphology of newly formed matte and slag were determined. Kinetic parameters of matte formation, valuable metal recovery rates and partition coefficients were deduced. Metals separation and settling rate was found to be strongly dependent on temperature. The highest recovery rates were found to occur at 1598 K (1325°C) for two hour settling while the most economical combination of parameters was found when settling at 1573 K (1300°C) for one hour. Silica additions generated higher partition coefficients for copper and nickel than the addition of lime. It is concluded that an additional refining step involving SiO2 and CaO fluxes is an economical way to recover more than 60% of valuable metals from slag that is disposed in landfills.
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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".