Water detection framework for industrial electric arc furnaces: Boundary modelling and formulation
Bibliographic record
Abstract
Abstract This paper describes the development of a boundary model for the off‐gas water vapour in an industrial steelmaking electric arc furnace (EAF). The solution addresses the mechanistic components of a complete EAF water detection framework. The boundary model has been implemented on an industrial alternating current (AC) EAF. The model specifies upper and lower limits in real‐time of the expected EAF off‐gas water vapour leaving the furnace, and it provides a valuable on‐line monitoring tool to the operator on what boundary to expect for the off‐gas water vapour in different circumstances. An essential data required for the framework is the EAF off‐gas composition. So, in this work, an off‐gas analyzer with a human machine interface (HMI) and a supervisory control and data acquisition (SCADA) system was installed in the first step. Then, in order to evaluate the developed water leak detection framework and verify the obtained results, industrial trials were designed in which a certain amount of water was intentionally added into the furnace by increasing the electrode spray water flow rate. Moreover, we have shown how the presented framework can be used to appropriately adjust the alarm setting values in control/emergency shutdown systems of industrial EAF to enhance the safety and availability of the plant.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".