Mercury Removal by Wet,Semi-Dry and Furnace Desulphurization Technology in Circulating Fluidized Bed
Bibliographic record
Abstract
By applying the ontario hydro method(OHM) and the US EPA standard methods,the flue gas mercury sampling was carried out before and after wet flue gas desulphurization and novel integrated desulphurization semi-desulphurization,respectively,in two coal-fired power plants.Various mercury speciations,such as Hg0,Hg2+ and HgP in flue gas,were analyzed.The flue gas mercury sampling before and after electrostatic precipitator of circulating fluidized bed combustion was also done and analyzed.The solid samples,such as coal,bottom ash,electrostatic precipitator(ESP) ash and desulphurization product,were analyzed by DMA80.According to mercury balance,mercury speciation and its distribution at different locations downstream the flue gas were obtained.The research results show that mercury mainly exist in the form of gaseous state in a pulverized coal-fired power plant,while it mainly exists in the form of particle-bound mercury in a circulating fluidized bed coal-fired power plant.Generally,mercury in the bottom ash is dissipative,while it is rich in dust collector(DC) ash,ESP ash,mixed ash and desulphurization product.The circulating fluidized bed combustion furnace desulphurization system shows the highest mercury removal efficiency,while the wet flue gas desulphurization system the lowest.
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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".