Influence of Na2CO3 and K2CO3 Addition on Iron Grain Growth during Carbothermic Reduction of Red Mud
Bibliographic record
Abstract
Red mud is a by-product of alumina production from bauxite ore by the Bayer method, which contains considerable amounts of valuable components such as iron, aluminum, titanium, and scandium. In this study, an approach was applied to extract iron, i.e., carbothermic reduction roasting of red mud with sodium and potassium carbonates followed by magnetic separation. The thermodynamic analysis of iron and iron-free components’ behavior during carbothermic reduction was carried out by HSC Chemistry 9.98 (Outotec, Pori, Finland) and FactSage 7.1 (Thermfact, Montreal, Canada; GTT-Technologies, Herzogenrath, Germany) software. The effects of the alkaline carbonates’ addition, as well as duration and temperature of roasting on the iron metallization degree, iron grains’ size, and magnetic separation process were investigated experimentally. The best conditions for the reduction roasting were found to be as follows: 22.01% of K2CO3 addition, 1250 °C, and 180 min of duration. As a generalization of the obtained data, the mechanism of alkaline carbonates’ influence on iron grain growth was proposed.
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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 | 0.001 |
| 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".