Real-Time, Optical Measurement of Gas Temperature and Particle Emissivity in a Full-Scale Steelmaking Furnace
Bibliographic record
Abstract
This article summarizes the successful implementation of a novel technique for measuring gas temperature and particle emissivity in real time at the mouth of a full-scale basic oxygen furnace (BOF). Both the technique and the data presented here may be useful to both process-control professionals interested in energy balances and computational fluid dynamic (CFD) modelers seeking in-situ data for their specific radiation heat-transfer submodels and temperature-boundary conditions. A description of the sensor, the retrieval algorithms, and the assumptions associated with each is included. The technique is based on midinfrared-emission spectroscopy. Results from a campaign spanning seven heats at a 168-tonne converter with data points every 2 seconds have been reported. During decarburization, the average off-gas temperature and particle emissivity were 1471 K and 0.55, respectively, for low-carbon heats (aim carbon <0.08 pct), and 1517 K and 0.36, respectively, for high-carbon heats (aim carbon >0.30 pct). Practical issues, validation of the assumptions, and measurement uncertainty are discussed. This technique may be applicable to other metallurgical batch processes in which large columns of high-temperature off-gases containing CO, CO 2 , and particles are present.
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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) | 0.000 | 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 | 0.001 | 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".