Partitioning behavior of mercury during coal combustion: the influence of low‐NOx burners and operation load of boiler
Bibliographic record
Abstract
Abstract Two 200 MW, pulverized bituminous coal‐fired electric utility boilers each equipped with a five‐field cold‐side electrostatic precipitator (ESP) as the only air pollution control device (APCD) were investigated on mercury distribution and speciation under various conditions. With the same fuel consumption, both facilities are the same but with different burners, low‐NOx type and conventional type. Sampling points of gaseous mercury and chlorine species were at the inlet and outlet of the ESP. The mercury concentrations in various solids, including parent coal, bottom slag, economizer bottom ash and fly ash in different hoppers of ESP, were sampled and analyzed. The Ontario Hydro Method was employed to detect mercury in flue gas, and HCl and Cl2 were detected by the EPA method 26. A series of tests were conducted on the boiler with low‐NOx burners under various loads (70, 85 and 100% of full load) and on the other boiler with conventional burners under full load. Results showed that small amount of mercury remained in the solid combustion products and most of mercury was released into the atmosphere. The majority of the released mercury was in oxidized form. Burner types and load variation had effects on Cl species concentration in flue gas. Furthermore, particle diameter and carbon content and specific surface area of fly ash affected speciation and distribution of mercury and mercury removal efficiency of ESP. Copyright © 2009 Curtin University of Technology and John Wiley & Sons, Ltd.
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".