A Thermodynamic Study of the Reduction of a Limonitic Laterite Ore by Methane
Bibliographic record
Abstract
Abstract The recovery of nickel from the oxidic nickeliferous laterite ores is receiving increasing attention due to the difficulty of recovering this metal from the sulphide ore deposits. One possible solution is to selectively reduce the nickel oxide in the ore, which could then be upgraded by, for example, magnetic concentration. In this article, a thermodynamic study was performed on the reduction of a limonitic laterite ore by methane. Methane was selected as the reducing agent as it has a lower environmental impact than carbon due to the reduced carbon dioxide emissions. The effects of temperatures and methane additions on the nickel recovery and nickel grade were investigated. High recoveries of over 95 % were predicted, but the grades were limited to about 2.5 % due to the formation of magnetite. The thermodynamic simulations for reduction by methane were in agreement with the experimental results in the literature for other reducing agents, reflecting the fact that the nickel oxide in the limonitic ore is relatively unstable. Thus, high recoveries could be achieved irrespective of the reducing agent involved.
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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) | 0.002 | 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".