Modeling of Creep in Refractory Lining in Anode Baking Furnaces
Bibliographic record
Abstract
Refractory flue walls in anode baking furnaces are exposed to harsh conditions during operation, affecting the structural properties of the material. The flue walls in industrial furnaces degrade over time to the point where they no longer perform as intended and must be replaced. Earlier studies of spent refractory lining from anode baking furnaces have shown considerable densification of the flue wall bricks, where the densification varies significantly from the anode side to the flue side of the brick. The observed densification is proposed to be caused by high-temperature creep, and the aim of this work was to determine whether the uneven densification across the brick could be modeled using a finite element method (FEM) implementing high-temperature steady-state creep. Finite element modeling was used to model steady-state creep for a material similar to that used in the baking furnace. Thermal and physical parameters and boundary conditions were chosen to simulate the conditions in an anode baking furnace. Refractory samples of pristine and spent lining from the baking furnace were also analyzed with X-ray computed tomography (CT), with a reduction in the porosity confirming the densification during operation. The FEM modeling demonstrated that high-temperature creep could explain the observed densification in the spent flue walls. The present findings may be useful in relation to increasing the lifetime of industrial flue walls.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".