Revisiting acidulation for tall oil and lignin manufacturing
Bibliographic record
Abstract
Abstract Tall oil is a byproduct of the kraft pulping process when softwood is used as raw material. As the production of softwood‐based pulp is in high demand, the optimization of the tall oil production process needs to be revisited to ensure the highest quality and quantity of tall oil manufacturing. In this work, the process for tall oil production was optimized in terms of tall oil yield, acid number, and tall oil components (i.e., fatty acid, rosin acid, unsaponifiable, and moisture contents) by considering acidulation reaction time, pH, water content, and settling additive. It was found that a reaction pH range of 2.5–3.0, 100 wt.% water addition, a reaction time of 20 min, a temperature of 90–100°C, and a 2‐h settling time yielded 52.9 wt.% of crude tall oil with the acid number of 137.3 mg KOH/g oil. Furthermore, the addition of anionic polymer pulp processing aid at 0.018 wt.% (dry basis) resulted in the largest crude tall oil yield of 57.1 wt.% and acid number of 142 mg KOH/g oil. Lignin from the tall oil production process was found to have an anionic charge density of 0.2–0.4 mmol/g and a solubility of approximately 0.7–2.0 g/L, both of which were higher than those of kraft lignin. However, the molecular weight of tall oil lignin was 1700 g/mol, which was smaller than kraft lignin, indicating that lignin underwent slight degradation during the acidulation 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| 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".