Enzyme‐based approaches for pitch control in thermomechanical pulping of softwood and pitch removal in process water
Bibliographic record
Abstract
Abstract BACKGROUND: In the pulp and paper manufacturing process, pitch colloidal particles have a tendency to agglomerate and deposit on pulp fibres and equipments. They reduce the efficiency of the washer, increase the dirt count and bleach chemical consumption, reduce pulp brightness thus leading to paper defects. Triglycerides are considered to be the most problematic compounds during the manufacturing of mechanical and acidic sulfite pulps from various softwood species. RESULTS: Using enzyme‐based approaches, a pitch control method was developed for use with thermomechanical pulping of softwood and for pitch removal from process water. Results showed that with combination of a novel biodegradable surfactant and a lipase resin acids, sterols and triglyceride groups were reduced by 48%, 32% and 78%, respectively, compared with untreated samples. Using laccase treatment of the process water the fatty and resin acids were reduced by 42% and the lignans by 60%. CONCLUSION: By combining the proposed pulp treatment with laccase decontamination of the process water, this new method offers an efficient alternative to control the various pitch‐associated problems. Copyright © 2008 Society of Chemical Industry
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".