Fines Deposition on Pulp Fibers and Fines Flocculation in a Turbulent-Flow Loop
Bibliographic record
Abstract
Fines retention is a combination of fines deposition on fibers and fines flocculation, followed by entrapment of fines flocs in a forming sheet. In the laboratory, fines flocculation is often studied in a dynamic drainage jar (DDJ) to mimic the hydrodynamic shear on a paper machine. However, the shear in a DDJ is very different from the shear on a machine. A flow geometry that might approximate shear in a headbox of a paper machine better is high Reynolds number flow through a tube because many headboxes contain a series of parallel pipes. We studied the deposition of fines and the flocculation of fines in a flow loop, with flow velocities on the order of a few meters per sceond, using a poly(ethylene oxide)−cofactor retention aid system. We found that fines deposition and flocculation follow the predictions of kinetic theories of Langmuir and Smoluchowski rather well despite the fact that fines are highly polydisperse. Fines were found to be flocculated even in the absence of a retention aid probably because of mechanical entanglements of fibrillar fines. Adding retention aids resulted in further aggregation. The detachment and floc breakup rates were found to be rather high, and extrapolation to papermaking conditions leads to the conclusion that fines deposition and flocculation are negligible in a headbox, at least for the retention aid system considered. This contradicts findings from DDJ experiments, which usually show appreciable fines retention. Perhaps a flow loop better represents flow conditions in a headbox, and a DDJ better represents flow conditions during drainage and formation.
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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 | 0.000 |
| 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 | 0.001 | 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".