Separation and Recovery of Valuable Metals from Nickel Slags Disposed in Landfills
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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) | 0.000 | 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 it