Tracking Inclusions during Ladle Refining Using a Kinetic Model for the Compositions of Metal, Slag, and Inclusions
Bibliographic record
Abstract
A mathematical kinetic model to predict the trajectory of chemical compositions of the molten steel, slag, and inclusions in the ladle metallurgy furnace was presented in a recent study. The model considered the following chemical processes: 1) the rate of slag–steel reaction; 2) the rate of supply of calcium during calcium treatment; and 3) the rate of steel–inclusion reactions, including oxides and sulfides. Herein, the model is further validated by running the model in varying conditions and comparing the predicted and measured composition of inclusions and dissolved species in the steel for additional plant heats. A sensitivity analysis is also carried out to investigate the effect of different parameters, including sulfur content of steel, total oxygen, slag composition, and reoxidation of steel on the trajectory of composition of inclusions during the ladle process. The model developed by the authors offers potential for control and optimization of the operation of a ladle furnace.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".