Hardwood Lignin Recovery Using Generator Waste Acid. Statistical Analysis and Simulation
Bibliographic record
Abstract
A method to recover hardwood kraft lignin by acidification of black liquor using waste acid from a Mathieson chlorine dioxide generator is proposed. Optimum reaction conditions to maximize the lignin yield and minimize acidification costs were determined. To analyze the effects of the major variables, a 2 3 factorial model describing the effects of acidification temperature, degree of agitation, and rate of waste acid addition was developed. Increasing the acidification temperature improved the lignin precipitation and filterability. However, the maximum practical temperature was 70 °C because the lignin precipitate starts to form a tarlike substance at approximately 80 °C. Also, the rate of acid addition should be minimized. In practice, this will be determined by the mill reaction vessel size, which depends on the black liquor flow rate to be acidified. Last, the stirring rate should be kept as low as possible, although some agitation is still required to uniformly mix the acid and black liquor. A steady-state computer simulation of incorporating a proposed 21 ton/day hardwood lignin recovery plant to the kraft liquor cycle showed no adverse effects in the chemical balance of the mill.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".