Solid state reduction and magnetic separation of nickeliferous laterite ores: Review and analysis
Bibliographic record
Abstract
As the global demand for nickel transitions from ferronickel to nickel sulfate for batteries, the nickel sulfide ore reserves are becoming increasingly more difficult to mine. Although, the mining of the nickeliferous laterite ores is much simpler, the extraction of the metal is more challenging, with limited process options. Furthermore, the current processing techniques are costly and have environmental issues, resulting in the need to develop alternative technologies through laboratory research and pilot plant testing. In this paper, firstly, an overview is provided of the composition plus mineralogy of both the limonitic and the saprolitic nickeliferous laterite ores and of the current commercial techniques that are employed to process these ores. Secondly, the pyrometallurgical research work reported in the literature on the production of a nickel concentrate by selective reduction followed by magnetic separation is reviewed. The main objectives are to achieve a high nickel recovery and to produce a concentrate with a high nickel grade. Thirdly, the role of additives, in particular the sulfur-containing species, is evaluated. Fourthly, the main issues involved in the processing of these ores and of particular importance, the areas that require further research and development are discussed. Finally, the potential of these new developing processes to replace the current commercial operations is assessed.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".