Mathematical modelling of an industrial furnace for high‐temperature heat treatment of wood
Bibliographic record
Abstract
Abstract There are various high‐temperature treatment methods for wood. In the “Bois Perdure” process, the thermal treatment of wood is carried out in a furnace by contacting it with hot combustion gases over 200°C without the addition of any chemicals in order to improve its dimensional stability and durability. The treatment eliminates free and bound water in the wood and modifies its molecular structure. In this study, a mathematical model describing the industrial furnace has been developed. The overall model consists of a 3‐D unsteady‐state sub‐model which solves for the flow, heat, and mass transfer in the gas coupled with a 1‐D unsteady‐state sub‐model which calculates the heat and mass transfer in the wood. The 3‐D gas sub‐model was developed using the commercial CFD code CFX. The 1‐D wood sub‐model is based on the solution of simultaneous heat and mass transfer equations (Luikov equations) using the implicit finite difference formulation. The model predicts the temperature and moisture distributions in the wood as well as the flow, heat, and moisture profiles in the gas. The model results are compared with the data obtained from the industrial furnace, and a good agreement was found between them.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".