Fine particle emission characteristics from coal-fired power plants based on field tests
Bibliographic record
Abstract
Six representative coal-fired power plants were selected and monitored for the PM10 and PM2.5 emission from these plants. The dust,PM10 and PM2.5 removal efficiencies and emission factors can be calculated based on the monitoring results,and the emission characteristics can be analyzed. The highest total dust removal efficiency among the 6 examined power plants through ESP (electrostatic precipitator) and FGD (flue gas desulfurization) is 99.88%,while the lowest efficiency is 99.75%,and the average efficiency is 99.82%. Before the ESP procedure,PM10/TSP is 20.93%~34.98%,and the average is 25.60%; PM2.5/TSP is 2.84%~4.14%,and the average is 3.39%. After the ESP and FGD procedure,PM10/TSP is 87.54%~95.90%,and the average is 91.57%; PM2.5/TSP is 41.22%~50.31%,and the average is 46.14%. PM2.5/PM10 increases from 10.74%~15.90% to 42.99%~55.14%,and the average is from 13.48% to 50.45%. The PM10 removal rate is 98.88%~99.62%,and the average is 99.29% after the ESP and FGD procedure,while the PM2.5 removal rate is 95.68%~98.47%,and the average is 97.41%. Comparing with the coal-fired power plants abroad,the 6 examined power plants have slightly larger dust emission factors than Canada and the United States in terms of kg·MWh-1 and kg·t-1. However,the PM10 and PM2.5 emission factors are much larger than in Canada and the US.
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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".