Nitrogen removal from steel by DRI fines injection
Bibliographic record
Abstract
Nitrogen even in small quantities is detrimental to the quality of steel, and it is difficult to remove from steel. The goal of this work was to develop a technique for nitrogen removal from liquid steel by injection of DRI fines. DRI fines, generated either directly from a DRI process or by attrition in transport or handling, contain significant quantities of carbon and oxygen. Studies have shown that upon heating these elements react rapidly inside DRI particles to form fine CO bubbles. The present study examines the generation of CO by the injection of DRI fines; these fines are generated inevitably from the handling and transportation of DRI pellets and briquettes, but such fines could be specifically prepared by crushing DRI pellets. It should be noted that some EAF shops already inject DRI fines to gain some value from this degraded product. To our knowledge there have been no studies to isolate the effect of fines injection, and to optimize this process for nitrogen removal.
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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 | 0.001 |
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".