Mercury Emission Experimental Comparisons for a Full-scale Coal-fired Power Plant
Bibliographic record
Abstract
Mercury(Hg) from combustion of fossil fuels and waste is the dominant source of anthropogenic Hg emissions with increasing environmental impacts.Nearly half of the Hg emissions reaches the Arctic.Coal-fired utility boilers are currently the largest known source of mercury emissions in the world.Mercury in coal-flue gas survives in three species,i.e.,elemental,oxidized and particulate.Mercury emission tests were done at the air preheater(APH) inlet and the electrostatic precipitator(ESP) outlet for a tangentially fired furnace with capacity of 250MW in Massachusetts,USA.The comparisons were presented for the mercury emission measurements among the Ontario Hydro Method(OHM) and Semi-Continuous Emission Monitors(SCEM).The results indicate the good agreements between the OHM and SCEM measurements on the total mercury species.Some SCEM results were not well consisted,especially the elemental species,with the OHM data due to the different port-installation of probes even through at the same sampling locations and they were more strongly influenced by the uneven distribution of mercury concentration in the turbulent flue gas across the flue duct section.The reasons were putatively addressed for the data deviations between the two methods and the more investigations should be done for the in situ samplings of SCEM for the more accurate species measurements.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".