Mercury Emission from Co-combustion of Coal and Sludge in a Circulating Fluidized-Bed Incinerator<sup>†</sup>
Bibliographic record
Abstract
Co-combustion of coal and sewage sludge is known as one of the most effective thermal treatments for sludge use and disposal. However, multiple pollutants emitted from this process, especially heavy metal mercury emission, have become a worldwide concern on the environment and public health. An experimental study on mercury emission and its speciation from co-combustion of sludge and coal was conducted in a circulating fluidized-bed incinerator with a fluidized-bed cross-section of 0.23 × 0.23 m and a total height of 7 m. Mercury speciation in flue gas and mercury contents in fly and bottom ashes were measured on the basis of the Ontario Hydro method. The mercury mass balance of the co-combustion process was calculated. Effects of some major factors, such as Ca/S molar ratio, desulfurization sorbent categories, excess air coefficient, and SO 2 and NO x concentrations, on the distribution of mercury speciation were investigated. Results showed that most of the mercury from the mixed fuel of the coal and sludge went into the flue gas, in which elemental mercury was the major species. A small amount of mercury remained in the fly ash, and none of the mercury was detected in the bottom ash. As desulfurization sorbents, both CaO and CaCO 3 can remove Hg 2+ in flue gas effectively, but CaO had a bigger capacity than CaCO 3 . The percentage of Hg 2+ in flue gas was found added with an increase of SO 2 and NO x concentrations. It can be concluded that an excess air coefficient exerted dominant influences on the distribution of mercury species among flue gas, fly ash, and bottom ash.
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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 | 0.000 | 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".