AISI/DOE Technology Roadmap Program: Removal of Residual Elements in The Steel Ladle by a Combination of Top Slag and Deep Injection Practice
Bibliographic record
Abstract
The objective of this work was to determine if tin could be removed from liquid steel by a combination of deep injection of calcium and a reducing top-slag practice. The work was carried out in three stages: injection of Ca wire into 35 Kg heats in an induction furnace under laboratory condition; a fundamental study of the solubility of Sn in the slag as a function of oxygen potential, temperature and slag composition; and, two full-scale plant trials. During the first stage, it was found that 7 to 50% of the Sn was removed from initial Sn contents of 0.1%, using 8 to 16 Kg of calcium per tonne of steel. The Sn solubility study suggested that low oxygen potential, high basicity of the slag and lower temperature would aid Sn removal by deep injection of Ca in the bath. However, two full-scale trials at the LMF station in Dofasco's plant showed virtually no Sn removal, mainly because of very low Ca consumption rates used (0.5 to 1.1 Kg/tonne vs. 8 to 16 Kg/tonne used during the induction furnace study in the laboratory). Based on the current price of Ca, addition of 8 to 16 Kg/tonne of steel to remove Sn is too cost prohibitive, and therefore, it is not worthwhile to pursue this process further, even though it may be technically feasible.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 teacher head, 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".