DETECTION OF WATER LEAKS USING EFSOP WATER DETECTION TECHONOLOGY®
Bibliographic record
Abstract
Tenova Goodfellow Inc. has successfully installed the proprietary EFSOP (Expert Furnace System Optimization Process) system in over 80 installations worldwide. For over 15 years, the EFSOP System has successfully achieved its main objective of improving performance and operating costs through the reduction of electricity, oxygen, methane, injected and charge carbon use. Using the off-gas information provided by the EFSOP System, Tenova Goodfellow Inc. has now developed a solution for detecting abnormal water events within the EAF steelmaking process. When water form enters the EAF, it immediately forms into water vapor (H 2 O gas ) and a proportion of the resulting water vapour will further react and dissociate to H 2 . Effective water detection technology must be capable of detecting BOTH forms of water; H 2 O gas & H 2 . Tenova Goodfellow has the technology to measure in real time and simultaneously H 2 O gas & H 2 . This paper will provide a summary on how the EFSOP Water Detection Technology is used during the EAF operation. Details regarding the technology, software, alarm rate and information on sensibility water trial tests will be provided within this paper.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 teacher head, 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".