Direct Production of Ferrochrome by Segregation Reduction of Chromite in the Presence of Calcium Chloride
Bibliographic record
Abstract
A solid reduction process is described whereby chromite is reduced with the help of calcium chloride to produce ferrochrome alloy powders with high metal recovery. The process involves segregation reduction of chromite using graphite as the reductant and calcium chloride as the segregation catalyst. Experiments were performed in the temperature range of 1200–1400 °C to evaluate the influences of various design parameters using both a thermogravimetric analyzer and an electric tube furnace with continuous off-gas analysis. The reduced products were characterized by scanning electron microscopy, X-ray powder diffraction, synchrotron X-ray absorption spectroscopy, and were subjected to wet chemical analysis. It was concluded that the addition of calcium chloride not only accelerated the carbothermic reduction of chromite but also promoted the formation and growth of individual ferrochrome alloy particles. The alloy formation within chromite particles was minimized, enabling the effective separation of ferrochrome alloy particles from the unwanted gangue without the need for fine grinding. Majority of the calcium chloride remained in a recoverable form, with a small percentage (<10 wt %) consumed by reacting with the siliceous gangue forming wadalite. Pure ferrochrome alloy powders were successfully produced with high metal recovery using elutriating separation.
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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".