Control of the ledge thickness in high-temperature metallurgical reactors 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 inside surface of the walls of a high-temperature metallurgical reactor is presented. A Kalman filter, based on a state-space representation of the reactor, is coupled with a recursive least-square estimator in order to estimate online the position of the phase front. The data are collected by a heat flux sensor located inside or outside of the reactor wall. The inverse method, used here as a virtual sensor, is coupled to a classic proportional–integral controller in order to control the ledge thickness by regulating the air cooling applied on the outside surface of the reactor wall. The virtual sensor and the control strategy are thoroughly tested for typical phase change conditions that prevail inside industrial facilities. Results show that a virtual sensor that relies on a heat flux sensor embedded inside the reactor wall provides more accurate and stable information, but at a price of a more complicated installation. In that case, it is shown that the discrepancy between the exact and the estimated ledge thicknesses remains smaller than 3% at all times, and that the control strategy ensures a null steady-state tracking error, a maximum tracking error less than 10%, no overshoot and no significant time lag.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".