Viscosity of Prehydrolysis Liquor of a Hardwood Kraft-Based Dissolving Pulp Production Process
Bibliographic record
Abstract
In this study, experimental viscosity results of hardwood prehydrolysis liquor (PHL) from the kraft-based dissolving pulp production process were obtained and used to develop empirical models describing the effect of temperature, solid content, the interaction between solute and solvent, and the concentration of lignin and hemicelluloses. The concentration, molar mass, and molecular weight of lignin and polysaccharide of PHL, all are the factors that affect the rheological behavior of the PHL. The results showed that, on the one side, Zaman and Fricke’s model gave better fitting when the viscosity is lower, on the other hand, a much better fitting of the Moosavifar’s model could be obtained by taking interaction between solute and solvent, solids content, concentration of lignin and hemicelluloses into account when the viscosity is higher, because of the interaction between solute lignin/hemicelluloses and solvent water, and the aggregation of lignin with itself, hemicelluloses with itself, and lignin with hemicelluloses. Consequently, using two different correlations in covering different viscosity value regions (η > 2 mPa s or η < 2 mPa s) of PHL led to better fitting of the data than those using just one single correlation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".