Emission and Migration Characteristics of Mercury in a 0.3 MWth CFB Boiler with Ammonium Bromide-Modified Rice Husk Char Injection into Flue
Bibliographic record
Abstract
The emission and migration characteristics of flue gas mercury in a pilot-scale 0.3 MWth circulating fluidized bed combustion boiler system by injecting the adsorbent of ammonium bromide (NH 4 Br)-modified rice husk char (NBr-RHC) into the flue duct before a fabric filter (FF) were investigated in this study. Mercury concentration in the flue gas was sampled by the Ontario Hydro Method. Meanwhile, the feeding coal, lime, process water, fly ashes both in the flue gas and FF hopper, bottom ash, gypsum, and wet flue gas desulfurization (WFGD) effluent were also collected during the flue gas mercury sampling. The temperature-programmed desorption (TPD) method was used to identify the mercury species in the sample of fly ashes. The results show that the combination system of the selective catalytic reduction (SCR) + adsorbent injection (AI) + FF + WFGD obtains a total flue gas mercury removal efficiency of 94.62%, which is 13.42% higher than that without the AI of NBr-RHC. A total of 93.83% of mercury exists in byproducts of the solid and liquid, and the amount of mercury collected by FF accounts for 97.28% due to the physisorption and chemisorption of gaseous mercury by NBr-RHC injection. The TPD analysis for mercury compound in the particulate matters from the AI device indicates that HgBr 2 formation is the key reason for NBr-RHC having high mercury removal efficiency. Elemental mercury is found to oxidize across the SCR, with 70.23% converted to oxidized mercury. WFGD exhibits a good oxidized mercury absorption rate of 86.67%, but elemental mercury increases from 0.19 to 0.26 μg/m 3 due to the oxidized mercury reduction, and more attention should be given on this increase.
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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".