Mercury speciation and mass distribution of coal-fired power plants in Taiwan using different air pollution control processes
Bibliographic record
Abstract
In this study, the mercury (Hg) emission, speciation, and mass distribution of four coal-fired power plants (CFPPs) located at central, southern, and northern Taiwan with various types of air pollution control devices were investigated. Gaseous Hg in the coal-combustion flue gas was sampled by using the Ontario Hydro method, and the solid and liquid samples were collected for understanding the Hg mass balance. The experimental results showed that the total Hg concentrations in flue gases at the inlets of selective catalytic reduction (SCR) varied from 2.984 to 4.692 μg Nm−3, while the total Hg concentrations in the flue gases at the stacks ranged from 0.240 to 0.675 μg Nm−3. These four CFPPs showed similar Hg speciation results at the stacks. The average Hg removal efficiencies of Plants 1 (SCR + electrostatic precipitator [ESP] + wet flue gas desulfurization [WFGD]), 2 (SCR + ESP + WFGD), 3 (SCR + bag filter (BF) + seawater flue gas desulfurization [SWFGD]) and 4 (SCR + BF + SWFGD) were 92.4%, 90.1%, 85.9%, and 84.8%, respectively. Coal was the major raw material in Hg input of CFPPs with a mass flow rate ranging 5.87–12.05 g hr−1. Elemental Hg (Hg0), accounting for 66.4%−97.1% of the total Hg, was the dominant species emitted to the atmosphere. The Hg mass balances for the four CFPPs varied from 86.0% to 117% of the Hg input, suggesting that good mass balances were obtained from the tested CFPPs. Implications: Mercury emissions from coal-fired power plant (CFPPs) have been greatly concerned and should thus be better comprehended. The present study examined the mercury speciation and mass distribution of four CFPPs located at Taiwan. Overall, these CFPPs had similar Hg speciation results at stack and Hg0 was the dominant species emitted to the atmosphere. The selective catalytic reduction (SCR) + electrostatic precipitator (ESP) + wet flue gas desulfurization (WFGD) system had the highest Hg removal efficiency and the Hg mass balances for the four CFPPs varied from 86.0 to 117%. This study helps better understanding the Hg emission inventory of CFPPs and provides useful information for selecting adequate air pollution control devices (APCDs) for Hg control.
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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 | 0.001 | 0.001 |
| 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) | 0.000 | 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".