Evaluation of Mercury Emissions from Coal-Fired Facilities with SCR and FGD Systems
Bibliographic record
Abstract
CONSOL Energy Inc., Research & Development (CONSOL), with support from the U.S. Department of Energy, National Energy Technology Laboratory (DOE) and the Electric Power Research Institute (EPRI), is evaluating the effects of selective catalytic reduction (SCR) on mercury (Hg) capture in coal-fired plants equipped with an electrostatic precipitator (ESP)--wet flue gas desulfurization (FGD) combination or a spray dyer absorber--fabric filter (SDA-FF) combination. In this program CONSOL is determining mercury speciation and removal at 10 coal-fired facilities. The principal purpose of this work is to develop a better understanding of the potential mercury removal ''co-benefits'' achieved by NO{sub x}, 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. The objectives are (1) to evaluate the effect of SCR on mercury capture in the ESP-FGD and SDA-FF combinations at coal-fired power plants, (2) evaluate the effect of SCR 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 tenth in a series of topical reports, describes the results and analysis of mercury sampling performed on two 468 MW units burning bituminous coal containing 1.3-1.7% sulfur. Unit 2 is equipped with an SCR, ESP, and wet FGD to control NO{sub x}, particulate, and SO{sub 2} emissions, respectively. Unit 1 is similar to Unit 2, except that Unit 1 has no SCR for NOx control. Four sampling tests were performed on both units in January 2005; flue gas mercury speciation and concentrations were determined at the economizer outlet, air heater outlet (ESP inlet), ESP outlet (FGD inlet), and at the stack (FGD outlet) using the Ontario Hydro method. Process samples for material balances were collected with the flue gas measurements. The results show that the SCR increased the oxidation of the mercury at the air heater outlet. At the exit of the air heater, a greater percentage of the mercury was in the oxidized and particulate forms on the unit equipped with an SCR compared to the unit without an SCR (97.4% vs 91%). This higher level of oxidation resulted in higher mercury removals in the scrubber. Total mercury removal averaged 97% on the unit with the SCR, and 87% on the unit without the SCR. The average mercury mass balance closure was 84% on Unit 1 and 103% on Unit 2.
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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 | 0.001 | 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 | 0.001 | 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".