The Fouling Behavior of Rice Husk Ash in Fluidized-Bed Combustion. 2. Pilot-Scale and Full-Scale Measurements
Bibliographic record
Abstract
This paper is the second in a series of two on the slagging and fouling behavior of rice husk when fired alone or in combination with other fuels in a fluidized-bed boiler. The first paper involved the fuel properties of rice husk, as investigated by a variety of laboratory methods. In this second paper, we report the results of fireside fouling measurements when burning rice husk alone and together with eucalyptus bark in various ratios. This study is based on short-term (3−10 h) deposit samples taken with air-cooled deposit probes in the superheater region of a large-scale (157 MW th ) bubbling fluidized-bed (BFB) boiler burning rice husk and eucalyptus bark. Using an entrained-flow type of pilot furnace, we further made more, systematic measurements of the influence of the fuel mixture ratio on the fouling tendency of the fly ash formed. Burning of rice husk alone did not result in any detectable fouling, neither in the pilot furnace nor on the deposit probes in the superheater area of the fluidized-bed boiler. After deposit samplings with durations of up to 10 h during 100% rice husk firing, the deposit sampling probe had not collected more than ∼95 mg of deposit material. The combustion of eucalyptus bark alone caused significant fouling. Here, the corresponding amount of deposit was ∼90 mg after 10 h of sampling. The fouling tendency of mixtures of rice husk and bark showed a nonlinear dependence on the fuel mixture ratio. The results suggest that the rice husk ash acted as an erosive, cleaning agent in the fly ash mix.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".