Improved Fiber Separation and Energy Reduction in Thermomechanical Pulp Refining Using Enzyme-Pretreated Wood
Bibliographic record
Abstract
Black spruce (Picea mariana) wood chips were subjected to enzymatic pretreatments, using an enzyme formulation containing cellulolytic and hemicellulolytic enzymes, combined with light mechanical treatments including downsizing and/or compression/decompression. These pretreatments were followed by 3 stages of mechanical refining in a lab-scale disc refiner. Depending on the treatment, the overall energy savings obtained could be as high as 15%. Most of the savings were obtained during the first stage of refining. Pulps collected after this stage were imaged using TEM and SEM. Differences were observed between differing treatment types. The most significant differences were seen when macerated chips were compressed and allowed to decompress in enzyme solutions. Enzyme treatments are able to change the rupture pattern during refining, thus lowering the overall specific energy consumption (SEC) as evidenced by improved pulp freeness and direct energy measurements. Increasing enzyme penetration helps to improve the overall SEC savings and also improves the distribution of energy savings throughout the refining stages by moving initial fiber separation from the middle lamella into the secondary wall.
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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".