Bibliographic record
Abstract
This study investigated mercury emission at various combustion conditions and analyzed mercury species in flue gas from coal combustion at a laboratory scale furnace in coal. The results of this study can be used to predict and to assess mercury emission at coal boilers and power plants. The coal used in the plants generally contains about 0.02~0.28 ㎎ of mercury per ㎏. Bituminous and anthracite coal used for the experiment contained 0.049 and 0.297 ㎎/㎏ of mercury, respectively. Mercury emissions during coal combustion at temperatures range of 600℃ to 1,400℃ was measured and analysed using Ontario Hydro method; the speciation changes were also observed in mercury emissions. The results showed higher fraction of elemental mercury than that of oxidised mercury at most temperatures tested in this experiment. The fraction of elemental mercury was lower in combustion of anthracite coal than in bituminous combustion. As expected, equilibrium calculations and real power plants data showed good similarity. The distribution of particle size in flue gas had the higher peak in size above 2.5 ㎛. However the peak of mercury enrichment in dust was at 0.3 ㎛, which could be easily emitted into atmosphere without filtration in combustion system. When the CEA (Chemical equilibrium and Application) code was used for combustion equilibrium calculation, Cl was found to be the important component effecting mercury oxidation, especially at the lower temperatures under 900℃.
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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) | 0.001 | 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".