Through air drying of paper—the effect of dryer fabric
Bibliographic record
Abstract
A custom experimental apparatus is designed to perform through air drying under well-controlled drying conditions such as air temperature and mass-flowrate. Using a novel optical measurement technique, the spatial distribution of moisture content in paper during through air drying is quantified as a function of time. The technique is capable of measuring the moisture content distribution with high spatiotemporal resolution while air flows through a paper mat sitting on a permeable dryer fabric. Four commercially available fabrics with different structural design and properties are used in the investigations. The effect of the fabrics’ structural properties, which are characterized using optical coherence tomography (OCT), is studied under various drying conditions. It is shown that the geometry of the contact spots of the fabrics has a significant impact on the drying time at high drying intensities. However, at low rates of drying (i.e., low air temperature and flowrate), no correlation between drying time and fabric properties is observed. After a cycle of through air drying, the permeability of paper increases irreversibly. This increased permeability is observed to be a function of the fabric structure. It is shown that the increase in permeability is larger for coarse fabric structures although no monotonic correlation with the fabric permeability can be observed. Comparing the spatial maps of moisture content with the paper grammage distribution reveals that there is a correlation between the local grammage and the spatial pattern of drying in a paper sheet.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".