Bibliographic record
Abstract
The Ontario Hydro Method(OHM) was used to sample and analyze the mercury concentration in flue gas before and after ESP and WFGD in a 300 MW power plant.Mercury content in coal,bottom slag,fly ash of ESP,adsorbent(limestone) and desulfurization product(gypsum) was detected by DMA80.Mercury mass balance was calculated based on the online measurements through the boiler system.Factors affecting the distribution,transformation and removal of mercury in flue gas were discussed.The results show that the gaseous mercury(Hg0 and Hg2+) in flue gas accounts for about 95% of total mercury,while mercury in the bottom ash can be neglected.More than 95% of Hgp and a little gaseous phase mercury(Hg0 and Hg2+)were removed by ESP.The efficiencies to remove total mercury by ESP range from 12.77% to 17.38%.A removal efficiency for Hg2+(g) reaches up to 79.93%~90.53% by WFGD,however,the content of Hg0 after WFGD increases because part of oxidized mercury is reduced to elemental mercury during WFGD.The efficiencies to remove total mercury by WFGD range from 9.68% to 29.36%.ESP and WFGD can remove all of the Hgp and a majority of Hg2+ with a total mercury removal efficiency of 25.38%~38.38%.In general,the demercuration in the conventional devices of ESP and WFGD is not high,which perhaps is owing to the lower concentration of Cl in feed coal.
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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 | 0.001 |
| 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".