Forces on bars in high-consistency mill-scale refiners: Effect of consistency
Bibliographic record
Abstract
The contact mechanics that arise during bar-crossing impacts are responsible for inducing the stresses, and corresponding strains, that develop raw wood fibre into useful papermaking pulp. Enhancing our knowledge of the interactions that make up these impacts, and how they are affected by process variables, is therefore a necessary step to advancing refiner technology. Experiments by Senger and Ouellet [1] involving individual fibre bundles, or flocs, in a single-bar refiner have indicated that bar forces are dependent on floc consistency, floc grammage and bar edge sharpness. These results offered insight into previously unexplainable behavior, but investigation of these relationships in mill-scale refiners was restricted by lack of available measurement technology. This investigation is now possible with the development of a refiner force sensor (RFS) that replaces a small segment of refiner bar, and is capable of measuring normal and shear forces experienced during individual bar-crossing impacts. Bar forces from RFS tests in two mill-scale refiners under nominal operation have already been reported [2]. During the same installations, consistency within the refining zone was also varied in individual experiments. The principal goal of this work is to report on the effects of refining consistency on recorded bar forces in these two trials.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".