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 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".