EXPERIENCE AND DEVELOPMENT OF METHODS TO ESTIMATE BLAST FURNACE REFRACTORY LINING CONDITIONS
Bibliographic record
Abstract
Acousto – Ultrasonic – Echo (AU-E) method of non-distractive testing of refractory lining conditions is developed by Hatch (Canada) to estimate refractory wear of blast furnaces and electrical smelters in non-ferrous and ferro-alloys industries. This system compliments the traditional modeling of heat transfer of blast furnace lining based on imbedded thermocouples data and additionally allows to determine location of cracks/anomalies and boundary between refractory lining and accretion. The limitations and accuracy of AU-E method are discussed and confirmed by comparison with physical measurements on cold furnaces. Improvement of the method allowed to take into account the influence of high temperatures, profile of the furnace and its dimensions and difference in the acoustic resistance of various layers of multilayer refractory lining on the regularity of wave propagation. The AU-E method is a reliable and non-destructive method for controlling the state of refractory masonry of smelting furnaces. The hardware and software of the AU-E system underwent a significant improvement, which made it possible to obtain measurement results with sufficient accuracy. Examples of AU-E method application to numerous furnaces in Russian Federation and around the Globe as well as some technological measures to prolong blast furnace campaign are presented and discussed. It was shown that results of several consecutive measurements allow estimation of the rate of refractory wear and prediction of the end point of blast furnace campaign. AU-E method is successfully applied for more than 70 blast furnaces around the World including blast furnaces of NLMK. CherMK, NTMK, ZapSib and MMK in Russian Federation and also for numerous copper, platinum, nickel and ferro-alloy smelters etc.
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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".