JV Task 122 - Assessment of Mercury Control Options for the San Miguel Electric Cooperative Power Plant
Bibliographic record
Abstract
In the United States, testing has been under way at electric coal-fired power plants to find viable and economical mercury control strategies to meet pending regulations. San Miguel Electric Cooperative (SMEC) engaged the Energy & Environmental Research Center (EERC) through a request for proposal (RFP) to perform research tests to evaluate sorbent-based technologies at its coal-fired San Miguel Generating Station to identify possible technology options that could be used by SMEC to meet the mercury reduction requirements of future U.S. federal standards. The goal of the testing was to target a mercury removal of {ge}90%. The EERC has successfully field-tested several sorbent-based technologies in previous projects that offer promise and potential to achieve a target removal of {ge}90%. Based on these field test results, yet recognizing that fuel type and plant operating conditions affect mercury capture significantly, the EERC proposed research tests to evaluate potential sorbent-based technologies provided by Norit Americas and the EERC that could potentially meet SMEC's mercury control objectives. Over the period of May through mid-June 2008, the EERC tested injection of both treated and nontreated activated carbon (AC) provided by Norit Americas and sorbent enhancement additives (SEAs) provided by the EERC. Tests were performed at San Miguel Unit 1 (450 MW) and included injection at the inlet of the air heater (AH) (temperature of 720 F). The test coal was a Texas lignite fuel with an average moisture content of 31.19%, an ash content of 26.6%, a heating value of 5,094 Btu/lb, a sulfur content of 2.7%, and a mercury concentration of 0.182 ppm, all reported on an as-received basis. Pilot-scale testing results identified DARCO{reg_sign} Hg-LH, SEA2 + DARCO{reg_sign} Hg, and the ChemMod sorbents as technologies with the potential to achieve the target mercury removal of {ge}90% at the full-scale test. Mercury concentrations were tracked with continuous mercury monitors (CMMs) at the electrostatic precipitator (ESP) inlet (ESP In), scrubber inlet, and scrubber outlet of San Miguel Unit 1, and a dry sorbent trap method was used to take samples periodically to measure mercury concentrations at the each of the CMM sampling locations described above. A limited number of Ontario Hydro (OH) measurements were also conducted. Removal efficiencies were calculated from mercury-in-coal values to scrubber out CMM values. Sorbent trap samples taken at the each sampling location outlet were found to be fairly consistent with CMM values. A maximum mercury removal of 78.5% was achieved with the SEA2 + DARCO Hg sorbent combination at injection rates of 50 ppm and 4 lb/Macf, respectively. An injection rate of 4 lb/Macf for DARCO Hg-LH and DARCO Hg resulted in mercury removals of 70.0% and 64.2%, respectively. These mercury reduction values were achieved at full load and at stable plant operating conditions. Scrubber reemission was observed during sorbent injection and had a significant effect on coal to scrubber out mercury removal values. When the sorbents were injected into San Miguel Unit 1 at the AH inlet, no effects on unit operations were observed. ESP performance throughout the test period was fairly steady, with only one minor breakdown. However, it should be noted that test durations were short.
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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 | 0.009 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".