Indications of the onset of fiber cutting in low consistency refining using a refiner force sensor: The effect of pulp furnish
Bibliographic record
Abstract
Abstract Detection of the onset of fiber cutting is beneficial in low consistency refining as it may prevent reduction of average fiber length, optimize fiber quality improvements by operating at gaps just wider than the critical gap, avoid decreasing the strength properties of paper, and increase energy efficiency. The objective of this study is to understand the effect of pulp furnish on measured bar forces and, more specifically, on the detection of fiber cutting. Bar forces, i. e. forces applied to pulp fibers by the refiner bars, are measured with a custom-designed piezoelectric force sensor. Trials were conducted with an AIKAWA 16-in. single-disc refiner using hemlock/balsam softwood thermomechanical pulp, SPF softwood thermomechanical pulp, northern bleached softwood kraft pulp, and aspen hardwood thermomechanical pulp at 3.0 to 3.5 % consistency at rotational speeds of 1200 and 1400 rpm. The power of the time domain signal of the measured forces is introduced as an indicator of the onset of fiber cutting. Our results show that this new fiber cutting metric is a sensitive and reliable metric for determination of fibre cutting for a range of pulp furnishes. The study suggests that the refiner force sensor has potential to be exploited for in-process detection of fiber cutting.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".