Fundamental study of red mud based fluxes for desulphurization and dephosphorization of hot metal
Bibliographic record
Abstract
Bauxite residue, also known as red mud, is generated during alumina production and is an abundant industrial waste material. Continuously increasing environmental concerns, together with scarcity of traditional mineral resources, have created a thrust to re-use the material. Red mud contains significant amounts of iron oxide and sodium hydroxide, hence a highly basic (pH > 10) slurry. In this research, the use of red mud as starting material for preparation of iron refining fluxes was evaluated. Red mud based fluxes and hot metal were equilibrated in graphite crucibles at the temperature range of 1300 ºC to 1400 °C and oxygen partial pressures range of 10-2 atm to 10-6 atm. It was found that the sulphide capacity increases with lime addition to a maximum 32 wt% CaO and decreases with increasing A12O3, TiO2 and SiO2 content in the fluxes saturated with lime. An iron foil equilibrium technique was employed to obtain precise measurements of phosphorus distribution between carbon saturated iron and red mud based fluxes. The measurements indicate that the equilibrium phosphorus distribution ratio initially increases with rise in FeO or CaO concentration of the fluxes and then drops. The melting behavior of the fluxes was also studied by visualizing the deformation of flux pellets as they were heated using a high temperature microscopy technique. Measurements of characteristic temperature for different fluxes indicated the melting property is a function of slag basicity. Therefore, optical basicity was used to establish a correlation between basicity of the red mud based fluxes and their melting properties.
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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".