Correlation of BOS process variables with dust mass formation and zinc content
Bibliographic record
Abstract
The basic oxygen steelmaking (BOS) process typically produces a dust rich in valuable iron units and often contaminated with zinc. This paper takes a look at statistical correlation and multiple regressions of process variables with the quantity of dust and the zinc mass contained in the dust. A robust inline sampling system was designed and installed to isokinetically sample the primary BOS dust slurry from a 248 m3 capacity BOS converter at Tata Steelworks Port Talbot (UK). This system was used to measure the dust mass and composition changes against time for 12 large scale trial heats and to compare with the process information data for a statistical evaluation of the variables. Statistically significant Pearson linear correlations were measured for the total dust mass produced with the iron ore and for the zinc mass contained in the dust with the addition of waste oxide briquettes (WOBs). A multiple regression analysis model showed strong associated correlations between the zinc mass contained in the dust with the galvanised scrap and WOB additions and explained 73% of the zinc mass variance.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".