On low consistency refining of mechanical pulps
Bibliographic record
Abstract
The aim of this thesis is to develop comprehensive knowledge to fill the gaps in the understanding of three key aspects of low consistency refining of mechanical pulps. Firstly, the fibre shortening mechanisms are formally studied by using a comminution model. Fibre length distribution data from before and after refining with a variety of pulp types, net-powers, feed flow rates, angular velocities and plate geometries was analyzed. Fibres' cutting rate and cutting location were found to be highly correlated with refiner gap. Plate geometry was also demonstrated to have a role in the fibre cutting location. Secondly, the relationship between net-power and gap was described using a correlation built entirely from pilot-scale refining data. Results showed that a properly defined dimensionless net-power number is crucial to compare different refiner sizes under the same grounds. The developed correlation was compared to industrial-scale data showing that the correlation is well suited for predictions. Key assumptions of the correlation were validated using bar-force sensor measurements data. Finally, the framework developed in the first two parts of this thesis were used together with pressure screening models available in literature to theoretically analyze refining systems typically found in TMP lines. Fibre length was used to assess each system performance in terms of refiner gap, reject ratio and refiner power. Moreover, the impact of some design aspects such as refiner size, recirculation and split-ratios was also described.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".