Fibre floc morphology and dewaterability of a pulp suspension: Role of flocculations kinetics and characteristics of flocculation agents
Bibliographic record
Abstract
The aim of this investigation was to find out how properties of a flocculation agent and flocculation kinetics affect the morphology of fibre flocs and how dewatering of a fibre suspension may be attributed to floc morphology. Fibre flocculation, analysed in terms of floc size, mass fractal dimension, floc strength, and the kinetic constant of flocculation, was measured with a digital image analysis system using cationic polyacrylamides as flocculants and NaCl as a coagulant. The results suggest that the kinetics of fibre flocculation is not a key factor determining floc properties but follows from the high bonding ability of the flocculation agent, which also lies behind the high floc density and size, i.e., the factors that lead to fast flocculation and improved floc properties are partly the same. In addition to interfibre bonding strength, the structure of the bonding layer of polymeric flocculants was found to be a significant factor describing floc morphology. Dewaterability of the fibre suspension was improved by increased floc density, which promotes fast water flow through the large voids around the dense flocs, while large, irregular flocs induced loose floc packing, which further improved dewatering by creating larger voids. Increased floc strength seemed to reduce the sealing of fluid passageways, particularly on the surface of the wire, where fibre squeezing could blind the filter fabric.
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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 | 0.001 |
| 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 | 0.001 |
| 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".