Effect of Flue-gas Cleaning Devices on Mercury Emission From Coal-fired Boiler
Bibliographic record
Abstract
In order to study the effect of flue-gas cleaning devices on mercury emission from coal-fired boiler, Ontario-Hydro method had been applied to determine the mercury concentration and speciation in the flue-gas emitted from a 300MW coal-fired boiler, which was equipped with various pollution control devices, including selective catalyst reduction (SCR) De-NOx system, electrostatic precipitator (ESP), and flue-gas seawater De-SO2 system (FGD). Mercury concentration in raw coal, bottom ash and fly ash of the boiler, seawater at the inlet and outlet of SO2 absorption reactor and the drainage of aeration sink, were also analyzed. The results indicate that the percentage of gaseous mercury in total mercury discharged is more than 79.1%. De-NOx catalyst strongly affects the mercury speciation transformation, showing a conversion rate of 83.4% for Hg0 to Hg2+. The removal efficiency of particulate mercury by ESP is close to 100%. With seawater FGD, the removal efficiency of mercury is as high as 73.6%. The mercury concentration in the seawater of drainage from aeration sink is 5.5 times higher than that in fresh seawater. The study shows that the flue-gas cleaning devices in coal-fired power plant play an important role on mercury emission characterization.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".