A low-cost non-invasive slag detection system for continuous casting
Bibliographic record
Abstract
The majority of steel produced today is made by the technology known in the steel making industry as continuous casting. Deciding when to stop the flow of molten steel from the ladle is not trivial, since terminating the process too early affects yield negatively, while closing the outflow valve too late lets slag enter the casting process. There is a variety of automatic slag detection systems available now, but numerous casting operations still rely on the decision of a human operator. In this paper, we propose a cost-effective non-invasive slag detection system that is based on the vibration signal measured during the casting procedure. In this method, the vibration acceleration data is analyzed by a cumulative sum (CUSUM) control chart in real time, providing a violation signal that can be used to close the ladle outflow valve. The proposed algorithm is implemented in an embedded microcontroller unit and is verified through a simulation study and laboratory experiments. These trials suggest that the technique may perform similarly to the human operator, however, just as in the case of the human operator, the disadvantage is that it only identifies the change when a small amount of slag already enters the tundish. Its advantage lies in its simplicity, low-cost, portable and non-invasive nature; possibly aiding the decision of the operator or, it may be used to create a completely automated ladle outflow valve closing system.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".