Chromium Oxide Reduction from Slag by Silicon and Magnesium
Bibliographic record
Abstract
Experimental and theoretical studies were performed in order to estimate the effect of the basicity of the slag as well as the amount of reducing agents on the reduction of chromium oxide from a slag in contact with molten steel at 1600 °C. The aim of this work was to compare the efficiency of magnesium and silicon as reducing agents of Cr2O3. The slag system contained CaO, MgO, SiO2, CaF2 and Cr2O3 together with Fe‐alloys (Fe‐Si or Fe‐Si‐Mg). The maximum Cr yield was about 81% with Fe‐44%Si‐9%Mg and 88% with Fe‐75%Si. That means that the reducing effect of Mg was much better than that of Si. Some experiments were conducted to study the effect of initial Cr in steel on the chromium oxide reduction reaction. Three initial Cr contents were tested (0.15, 2 and 4 mass%). It was found that chrome was partially bound into stable spinel phase MgO·Cr2O3 in the final slag. Thermodynamic estimations were made to determine the effect of process temperature, slag basicity and the amount of reducing agents in the slag on the Cr recovery. The efficiency of Cr reducing agents increased in the order: Si, Al, Ca and Mg. The Cr yield increased with slag basicity and amount of reducing agents.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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".