Ferric Sulfate Leaching of Pyrrhotite Tailings between 30 to 55 °C
Bibliographic record
Abstract
Mine tailings present major environmental issues in the mining industry. However due to the depletion of high-grade sulfide ores for metal recovery, tailings could also be a potential resource for certain valuable metals. The present study investigates the potential to recover nickel from pyrrhotite tailings. Leaching tests were performed in acidic ferric sulfate media with 0.14 wt % solids to keep the ferric concentration essentially constant. The temperature was varied between 30 and 55 °C, and the ferric concentration was in a range 0.02–0.3 M. The results showed that both temperature and ferric sulfate concentration had significant effects on the nickel extraction kinetics. The shrinking core model (SCM) was applied to the nickel extraction data. The rate controlling step was found to be product layer diffusion. The Arrhenius plot yielded an activation energy of Ea = 62.12 kJ/mol based on apparent reaction rates obtained by the SCM. The reaction order with respect to ferric ion was found to be 1 at the high concentration range. SEM images of partially leached tailings confirmed the presence of elemental sulfur around the pyrrhotite particles, which was responsible for the observed non-linear leaching kinetics (diffusion control).
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.
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".