Effectiveness of magnesium oxide additives in mitigating fouling problems in kraft recovery boilers
Bibliographic record
Abstract
A systematic study was performed in the laboratory and in the field to examine the effect of magnesium oxide additive on deposit accumulation and removal. Laboratory results show that the additive has little effect on the amount of deposits collected on a probe, but it can make deposits easy to remove if a coating layer thicker than 30 μm can be effectively applied on the probe surface. The results also show that mixing the additive with black liquor or injecting it separately has no significant effect on deposit accumulation and removal. Tests performed in a recovery boiler where the additive was continuously injected show no evidence for the existence of the coating layer. The magnesium oxide content in the boiler deposits varied between 0.1 and 0.4 wt%, which is several times lower than the amount that was found to be effective in laboratory tests. Nonetheless, the additive was shown to be effective in facilitating deposit removal if it can form a coating layer on deposit/tube surfaces. This may be possible by periodically injecting a large amount of additive into the boiler for a short period and reducing the black liquor firing rate at the same time.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".