Moisture Nonuniformity in Drying Paper: Measurement and Relation to Process Parameters
Bibliographic record
Abstract
Local nonuniformity of moisture content during drying is doubly disadvantageous. Moisture nonuniformity reduces drying effectiveness, increasing drying cost or reducing drying capacity, and may decrease product quality for printing through effects such as cockling and curl. These problems can occur with the now dominant process of cylinder drying, but become much more acute with the various higher intensity drying techniques which are competing to become elements of the higher speed hybrid dryer sections of the future. For quantitative characterization of moisture nonuniformity a novel method applicable to paper being dried by various processes was developed, then demonstrated for one high intensity air convection process, through air drying. During drying, the local moisture content was determined for many 2.5 mm diameter sensing areas by very rapid scanning with a custom design 3-wavelength infrared paper moisture instrument. For local moisture nonuniformity thus determined, the coefficient of variation provides a standard basis for comparison between paper types and drying conditions. For kraft paper from 20 to 120 g/m2, the effects on local moisture nonuniformity were evaluated for drying intensity and for several paper parameters—formation, grammage, and initial moisture content. The drying nonuniformity index was found to parallel both formation quality and drying intensity. An unexpected finding was that over the 20–120 g/m2 range, local moisture nonuniformity passes through a maximum at 30 g/m2. Sheet initial moisture content produces the largest effect, with moisture nonuniformity index being both high and very sensitive to this parameter for initial moisture content above the fibre saturation point. Another finding from these unique measurements is that, for the same 2.5 mm diameter sensing areas, there is a clear correlation between moisture nonuniformity index and local grammage.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".