Effect of Black Liquor Burning on the Settling and Filtering Behaviour of Green Liquor Dregs
Bibliographic record
Abstract
In kraft pulp mills, the burning of black liquor in recovery boilers results in unburned carbon, or char particles, that along with other types of particles form the suspended solids in green liquor called dregs. Poor dregs settling and filterability are a persistent problem at many mills that can result in substantial production losses. A systematic study was conducted to investigate the effect of black liquor burning conditions on the settling and filtering behaviour of dregs using a combination of experimental work and multivariate data analysis (MVDA), with a focus on the char component of dregs. The experimental results show that char is easier to settle and filter when i) black liquor is burned at higher temperatures or for longer amounts of time, ii) char concentration is low, and iii) lime mud is added to char. The results also imply that larger char particles tend to settle faster. MVDA was carried out on operating data from three kraft pulp mills to examine the correlations between recovery boiler operation and the dregs behaviour observed at each mill. The results suggest that low firing load to the recovery boiler, a low extent of char burning, and an unstable or cold char bed could lead to larger amounts of char (dregs) in green liquor.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".