Fine particle emission characteristics from coal-fired power plants based on field tests
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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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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 it