Energy saving potential of high yield pulp (HYP) application by addition of small amounts of bleached wheat straw pulp
Bibliographic record
Abstract
Abstract High-yield pulp (HYP) has found wide applications in many paper grades. Usually, the strength properties of HYP must be improved and its freeness fine-tuned before sending it for paper machining, by means of refining at a low consistency, which requires energy. In this study, the possibility of avoiding refining of HYP was investigated by adding low percentages of refined and bleached wheat straw pulp (BWSP) to a HYP-containing mixture. The results show that the strength properties of a HYP and a hardwood kraft (HWKP) mixture can be improved with approximately 10% refined BWSP. In this manner, refining energy of 20 kWh t-1is needed, and the pulp quality is improved to a similar level to that obtained from the same pulp mixture refined with an energy input of 70 kWh t-1. This approach also works for 100% HYP. The practical implication is that only a small percentage of refined BWSP is needed to improve the strength property of a HYP or HYP/HWKP mixtures, so that less refining energy is required in the low-consistency refining process.
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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 | 0.000 | 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".