Effect of wet storage on alkali-oxygen pulping of bagasse
Bibliographic record
Abstract
Carbohydrate degradation is a serious problem in alkali-oxygen pulping due to the inherently oxidative reaction conditions. Hemicellulose and short-chain cellulose are particularly susceptible to degradation by oxidation, and thus have a great effect on the pulping yield, as well as the viscosity and crystallinity of resultant pulp in alkali-oxygen pulping. Removal of the low molecular weight substances by wet storage prior to alkali-oxygen pulping may increase the pulping yield and improve pulp properties, in addition to savings in pulping chemicals and energy. This paper investigated alkali-oxygen pulping of bagasse pretreated by wet storage, and results show that wet storage of bagasse had a significant effect on its alkali-oxygen pulping, in terms of pulping yield, pulp viscosity and crystallinity. The pH and time were found to be the two most important factors in wet storage on bagasse. Pulp crystallinity increased from 31.78% to 42.06% when the wet storage of bagasse was performed at pH 6.0 for 16 days. In addition, the screened pulp yield increased from 58 to 60%, and pulp viscosity increased from 650 to 700 mL/g. The improved pulping performance was attributed to increased pore volumes in bagasse due to dissolution of low molecular weight lignin and carbohydrates during wet storage, which in turn improved the selectivity of delignification in the alkali-oxygen pulping 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 | 0.001 |
| 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".