Unconventional pollutant removal from a coal-fired ultra-low emission unit
Bibliographic record
Abstract
The emission of unconventional pollutants from coal-fired power plants has not received enough attention. In order to fully characterize the removal characteristics of unconventional pollutants from coal-fired power plants, the step removal characteristics of CPM, Hg, SO3, PAHs and other abnormal pollutant from a 1 000 MW coal-fired ultra-low emission unit were systematically studied by using FPM and CPM integration sampling system, Ontario method (OHM), controlling condensate method, HJ 646-2013 unit test method. The results showed that at 100% and 75% loadings, the removal efficiencies of CPM were 87.15% and 92.20% by the low-low-temperature electric dust removal system, respectively, and wet desulfurization efficiencies were 49.65% and 45.55%, respectively. At different loadings, FPM emission concentrations were 3.6 and 4.4 mg·m−3, respectively, but CPM emission concentrations were 14.2 and 15.3 mg·m−3, the latter ones were much higher than the former ones. The Hg removal efficiency of low-low-temperature electric dust removal system was 64.81%, and the Hg removal efficiency of the whole system was 75.5%. Hgp was totally removed, and the remaining ones were Hg0 and Hg2+ being hard to remove, their removal efficiencies were 63.01% and 64.29%, respectively. The emission concentrations of Hg0 and Hg2+ were 5.4 μg·m−3 and 0.5 μg·m−3, respectively. The conversion rate of SO2 oxidized to SO3 by SCR denitration catalyst was about 0.7%. The low-low-temperature electric dust removal system could remove 88.7% SO3 in the gaseous state, the wet desulfurization could remove 29.63% SO3, the final SO3 emission concentration was 1.9 mg·m−3. The removal efficiency of 16 kinds of PAHs from the whole system reached 94.25%, of which the removal efficiencies of gas and solid phase PAHs were 91.61% and 99.27%, respectively, their corresponding emission concentrations were 2.39 and 0.11 μg·m−3, respectively. The existing ultra-low emission equipment had different levels of collaborative removal effect of unconventional pollutants, the emission concentrations of CPM, Hg, SO3 and PAHs of the unit at full loading were 14.2 mg·m−3, 5.9 μg·m−3, 1.9 mg·m−3 and 2.5 μg·m−3, respectively. The Hg emission concentration meets the standard of 30 μg·m−3 in Emission Standard for air pollutants from thermal power plants, and the national compulsory emission standard for the emission concentrations of CPM, Hg, SO3 and PAH have not yet settled. This study can provide a reference for the control of unconventional pollutants in coal-fired power plants.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".