Mercury transformation across various air pollution control devices in a 200 MW coal‐fired boiler of China
Bibliographic record
Abstract
Abstract An onsite investigation of the mercury emission from a Chinese 200 MW pulverized coal (PC) boiler equipped with various air pollution control devices (APCDs) was conducted by using the Ontario Hydro method (OHM). The mercury mass balance was + 4.6% of the input coal mercury for the whole system. Small amounts of mercury were detected in the bottom ash; nearly 90% of the mercury in PC was removed by the existing APCDs, i.e. selective catalytic reduction unit (SCR) and electrostatic precipitator (ESP) followed by fabric filters (FFs) baghouse, and flue gas desulfurization system (FGD). The concentration of oxidized mercury (converted from the elemental form) in the flue gas increased from 14% before SCR to 75% after SCR. Hence, the mercury removal efficiency of ESP and FGD was significantly improved when compared to the removal rates found in previous field measurement. This study demonstrates that the conversion of elemental mercury into the oxidized forms significantly improves the overall mercury removal efficiency of conventional APCDs, and most of the mercury emitted after FGD was in the elemental form. Copyright © 2009 Curtin University of Technology and John Wiley & Sons, Ltd.
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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 | 0.000 | 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".