As, Hg, and Se Flue Gas Sampling in a Coal-Fired Power Plant and Their Fate during Coal Combustion
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Bibliographic record
Abstract
As, Hg, and Se are the most volatile elements in the flue gas from a coal-fired power plant. Significant amounts of these elements cause an undesired direct gaseous emission, which leads to a serious environmental health risk. The main focus of this study is to evaluate the possibility of simultaneous sampling of these volatile elements using an accurate official method for Hg (the most volatile element). A study of As, Hg, and Se emissions from a 1400 MW coal-fired power plant equipped with electrostatic precipitators (ESPs) was carried out for the combustion of a mixture of two types of coal. Simultaneous sampling of coal, bottom ash, fly ash, flue gas, and particles associated with the gas phase has been performed. Flue gas has been sampled by the Ontario Hydro Method Sampling Train, an ASTM method for Hg speciation. This sampling method was tested for As and Se sampling. As and Se determinations have been performed by HG-AAS, and Hg has been determined by CV-AAS. The results were used to examine the following: overall mass balances, relative distribution of these elements in the coal-fired power plant; As, Hg, and Se concentrations in coal and combustion residues; and predominant oxidation state for Hg in flue gas. The mass balances obtained for As, Hg, and Se were satisfactory in all cases; nevertheless, relative enrichment values in fly ash for As and Se were low; therefore, we concluded that As sampling in flue gas can be conducted by application of the Ontario Hydro Method; nevertheless Se released in the gas phase is not completely collected by this sampling train. Application of this sampling method allowed for performance of Hg speciation. The results indicated that Hg(II) was the predominant species in flue gas. It has also been proved that 24%, more than 99.8%, and 90% for As, Hg, and Se in the stack emissions, respectively, were in the gaseous phase.
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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 | 0.001 |
| 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 it