Evaluation of Mercury Emissions from Coal-Fired Facilities with SCR and FGD Systems
Bibliographic record
Abstract
evaluate the effect of catalyst degradation on mercury capture; (3) evaluate the effect of low load operation on mercury capture in an SCR-FGD system, and (4) collect data that could provide the basis for fundamental scientific insights into the nature of mercury chemistry in flue gas, the catalytic effect of SCR systems on mercury speciation and the efficacy of different FGD technologies for mercury capture. This document, the seventh in a series of topical reports, describes the results and analysis of mercury sampling performed on a 1,300 MW unit burning a bituminous coal containing three percent sulfur. The unit was equipped with an ESP and a limestone-based wet FGD to control particulate and SO2 emissions, respectively. At the time of sampling an SCR was not installed on this unit. Four sampling tests were performed in September 2003. Flue gas mercury speciation and concentrations were determined at the ESP outlet (FGD inlet), and at the stack (FGD outlet) using the Ontario Hydro method. Process stream samples for a mercury balance were collected to coincide with the flue gas measurements. The results show that the FGD inlet flue gas oxidized:elemental mercury ratio was roughly 2:1, with 66% oxidized mercury and 34% elemental mercury. Mercury removal, on a coal-to-stack basis, was 53%. The average Hg concentration in the stack flue gas was 4.09 {micro}g/m{sup 3}. The average stack mercury emission was 3.47 Ib/TBtu. The mercury material balance closures ranged from 87% to 108%, with an average of 97%. A sampling program similar to this one was performed on a similar unit (at the same plant) that was equipped with an SCR for NOx control. Comparison of the results from the two units show that the SCR increases the percentage of mercury that is in the oxidized form, which, in turn, lends to more of the total mercury being removed in the wet scrubber. The principal purpose of this work is to develop a better understanding of the potential mercury removal ''co-benefits'' achieved by NOx, and SO{sub 2} control technologies. It is expected that this data will provide the basis for fundamental scientific insights into the nature of mercury chemistry in flue gas, the catalytic effect of SCR systems on mercury speciation and the efficacy of different FGD technologies for mercury capture. Ultimately, this insight could help to design and operate SCR and FGD systems to maximize mercury removal.
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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 | 0.001 |
| 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 | 0.001 | 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".