Prediction of the ledge thickness inside a high-temperature metallurgical reactor using a virtual sensor
Bibliographic record
Abstract
A non-intrusive inverse heat transfer procedure for predicting the time-varying thickness of the phase-change ledge on the inner surface of the walls of a high-temperature metallurgical reactor is presented. An extended Kalman filter with augmented state is coupled with a nonlinear state-space model of the reactor in order to estimate on-line the position of the phase front. The data are collected by a heat flux sensor located inside or outside of the reactor wall. This non-intrusive method can be seen as a virtual sensor which is defined as the combination of an estimation algorithm with measurements for the estimation of 'hard to measure' on-line process variables. The inverse prediction of the ledge thickness with the virtual sensor is thoroughly tested for typical operating conditions that prevail inside an industrial facility. Due to the fact that the melting/solidification process inside the reactor is highly nonlinear, results show that the accuracy of the state-space identification and the virtual sensor estimation is far superior when a nonlinear state-space model and the extended Kalman filter are employed, as opposed to a linear state-space model and the classic Kalman filter. In the former, it is shown that the discrepancy between the exact and the estimated ledge thickness remains smaller than 10% at all times.
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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) | 0.001 | 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 | 0.001 |
| 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 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".