Theoretical analysis of LC-refining – pressure screening systems in TMP
Bibliographic record
Abstract
LC refining of mechanical pulps has proven to save energy in the production of TMP pulps. However, the specific role of LC refining as part of a TMP system has not been thoroughly studied since it is difficult to conceive any particular system at industrial-scales and impractical at pilot-scales. In this study, pressure screening and LC refining models that describe fibre length distributions, together with correlations to predict refining power were used to model three basic refining systems. From the simulation results, the impact of important variables such as reject ratio, refiner gap and refining net-power was studied. Performance curves of length-weighed average fibre length were generated from simulation results and were used to assess each system behaviour and also to make comparisons between systems. Data from an industrial scale TMP mill sub-system was gathered and compared to simulation results showing relative errors between 0-18 % on the predicted variables.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 teacher head, 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".