Bibliographic record
Abstract
An industrial FBC boiler operated by a paper products company encountered significant fouling problems that required boiler shutdown and maintenance to remove deposits. CETC investigated the fouling problems. Samples from the boiler were analyzed and the results indicated that fouling was associated with near-quantitative conversion of CaO in the deposits to CaSO4, with particle expansion and possible chemical reaction sintering. There was no evidence, with possibly the exception of deposits on the distributor tuyeres, of any significant contribution from the elements traditionally associated with ash softening, e.g., Na and K. Vanadium was not considered an issue, because earlier work showed that V quantitatively combines with CaO (or MgO if present), to form high melting point vanadates. Extended sulphation tests on both bed ash and limestone were conducted in the laboratory. The results confirmed that sulphation alone was sufficient to cause strength development in the bed solids. There was also a clear difference in the strength development between bed and fly ash. Fly ash agglomerates were much weaker than those of bed ash. The long-term sulphation tests also demonstrated that the presence of “inert” ash components helps to reduce the degree of conversion of CaO to CaSO4 and to reduce the strength of the deposits. Strength development tests showed that the current limestone used in this particular boiler formed exceptionally strong deposits after being exposed to sulphation conditions for extended periods of time at 850°C. Thus it appeared that if this limestone was replaced it might benefit boiler operation. Kaolin is used at the plant to reduce distributor tuyere fouling. Chemical analysis of the deposits suggested that kaolin may help to reduce fouling, but this may well be due to physical rather than chemical behaviour of the kaolin. This work suggests that the kaolin could be replaced by another refractory material, suitably low in Na and K, since the sulphation levels in the deposits were sufficient by themselves to lead to strength development, and so any benefit of the kaolin action as an alkali metal “getter” is probably offset by that fact. Attempts to find some significant physical parameters to differentiate the tendency of bed ashes to agglomerate were not successful in this work, although previous work has suggested that bulk density may be important, with materials having a higher bulk density showing a greater tendency to agglomerate.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".