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Record W2165560622 · doi:10.2172/793526

EFFECTS OF FLY ASH ON MERCURY OXIDATION DURING POST COMBUSTION CONDITIONS

2002· report· en· W2165560622 on OpenAlexaboutno aff
G.A. Norton, Hongqun Yang, Robert C. Brown, D.L. Laudal, Grant E. Dunham, John Erjavec, J.M. Okoh

Bibliographic record

Venuenot available
Typereport
Languageen
FieldEnvironmental Science
TopicMercury impact and mitigation studies
Canadian institutionsnot available
Fundersnot available
KeywordsFly ashFlue gasMercury (programming language)Environmental chemistryChemistryOxidizing agentCombustionCoalBottom ashCoal combustion productsMineralogyOrganic chemistry

Abstract

fetched live from OpenAlex

Tests were performed in simulated flue gas streams using fly ash from the electrostatic precipitators of two full-scale utility boilers. One fly ash was from a Powder River Basin (PRB) coal, while the other was from Blacksville coal. Elemental Hg was injected upstream from samples of fly ash loaded onto filters housed in an oven at 120 or 180 C. Concentrations of oxidized and elemental Hg downstream from the filters were determined using the Ontario Hydro method. The gas stream composition and whether or not ash was present in the gas stream were the two most important variables affecting Hg oxidation. The presence of HCl, NO, NO{sub 2}, and SO{sub 2} were all important with respect to Hg oxidation, with NO{sub 2} and HCl being the most important. The presence of NO suppressed Hg oxidation in these tests. Although the two fly ashes were chemically and mineralogically diverse, there were generally no large differences in catalytic potential (for oxidizing Hg) between them. Similarly, no ash fraction appeared to be highly catalytic relative to other ash fractions. This includes fractions enriched in unburned carbon and fractions enriched in iron oxides. Although some differences of lesser magnitude were observed in the amount of oxidized Hg formed, levels of oxidized Hg generally tracked well with the surface areas of the different ashes and ash fractions. Therefore, although the Blacksville fly ash tended to show slightly more catalytic activity than the PRB fly ash, this could be due to the relatively high surface area of that ash. Similarly, for Blacksville fly ash, using nonmagnetic ash resulted in more Hg oxidation than using magnetic ash, but this again tracked well with the relative surface areas of the two ash fractions. Test results suggest that the gas matrix may be more important in Hg oxidation chemistry than the fly ash composition. Combustion tests were performed in which Blacksville and PRB fly ashes were injected into filtered (via a baghouse with Teflon bags) flue gas obtained while firing PRB coal in a 35 kW combustor. The Ontario Hydro method was used to determine the Hg speciation after fly ash injection. Wall effects in the combustor complicated interpretation of testing data, although a number of observations could still be made. The amount of Hg collected in the Ontario Hydro impingers was lower than anticipated, and is probably due to sorption of Hg by the fly ash. While firing PRB coal without any ash injection, the percent oxidized Hg in the gas stream was fairly high (average of 63%). The high levels of vapor phase oxidized Hg in these base line tests may be due to catalytic effects from the refractory materials in the combustor. When PRB fly ash was injected into a filtered PRB flue gas stream, the percentage of oxidized Hg in the gas stream decreased dramatically. Decreases in the percentage of oxidized Hg were also observed while injecting Blacksville fly ash, but to a lesser extent. Injecting whole Blacksville fly ash into the filtered PRB flue gas appeared to result in greater concentrations of oxidized Hg relative to the tests where whole PRB fly ash was injected. However, because the Blacksville fly ash has a relatively high surface area, this may be only a surface area effect.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.019
GPT teacher head0.275
Teacher spread0.256 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations7
Published2002
Admission routes1
Has abstractyes

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