Pilot-Scale Investigation into the Effects of Alkaline Peroxide Pre-Treatments on Low-Consistency Refining of Primary Refined Softwood TMP
Bibliographic record
Abstract
Primary refined coarse softwood thermomechanical pulp was treated with alkaline peroxide prior to low-consistency (LC) refining. The effects of the pre-treatments on pulp quality, refinability, and electrical energy consumption were assessed. Four pre-treatments were conducted with alkali charges of 2.5 and 6% and peroxide charges of 3 and 4%. The pulps were refined to specific energies up to 600 kWh/t by multiple passes through an LC refiner at intensities of 90 and 150 kWh/t. It was found that alkaline peroxide treatments increased tear strength and protected the fibre from cutting, especially during high intensity refining below a specific energy of 300 kWh/t. Treatment with 6% NaOH and 4% or 3% H2O2 led to lower brightness gains and scattering coefficients but increased the tensile strength index by 31%, potentially lowering the total electrical energy required to achieve strong pulp. The enhancement of tensile strength caused by the highly alkaline peroxide mostly resulted from increased bonding, which was attributable to acid group generation rather than the promotion of further fibrillation during LC refining.
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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) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".