Determination of thickness and quality of refractory linings in industrial furnaces using acousto ultrasonic echo
Bibliographic record
Abstract
Refractory linings in metallurgical furnaces undergo deterioration and wearing with time. The deterioration is caused mainly by thermomechanical mechanisms leading to cracks, chemical degradation and loss of heat transfer capability. Any sudden failure of the lining is dangerous and could affect the structural integrity of the furnace, leading to production loss and costly refurbishment. Non-destructive testing (NDT) and monitoring of refractory lining would lead to better safety, longer use of the vessel, production optimization, controlled maintenance and increased production. Thickness measurements and monitoring of the refractory lining in operating furnaces is possible using Acousto Ultrasonic -Echo (AU-E) technique. This technique uses stress waves of both acoustic and ultrasonic ranges in order to determine thickness and integrity. This thesis presents the details of AU-E technique in addition to laboratory measurements to determine parameters leading to the in-situ measurements. Finally, three case studies are presented to substantiate the theoretical and laboratory measurements.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".