Enhancing Kraft based dissolving pulp production by integrating green liquor neutralization
Bibliographic record
Abstract
A pre-hydrolysis Kraft pulping (PHK) process that was used to make dissolving pulp was enhanced by replacing conventional white liquor (WL) neutralization with green liquor (GL) neutralization prior to Kraft pulping. This resulted in a 10% increase in dissolving pulp production, and significant chemical savings, without compromising pulp reactivity. When the possible influence of the alkaline charge on fibre properties was assessed using methods such as viscosity, Simon's stain, Size Exclusion Chromatography (SEC) and SEM, it was apparent that stronger alkaline treatments (WL) resulted increased cellulose degradation, a lower cellulose DP and a slightly larger surface area. When these methods were complemented with an assay based on the selective binding of site-specific carbohydrate-binding modules (CBMs), it was apparent that green liquor (GL) neutralization resulted in an increase in less-ordered cellulose being exposed. This likely contributed to its higher reactivity despite its lower overall surface area.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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 teacher head, 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".