Impact of Coal Chlorine on Mercury Speciation and Emission from a 100-MW Utility Boiler with Cold-Side Electrostatic Precipitators and Low-NO<i><sub>x</sub></i> Burners
Bibliographic record
Abstract
Field tests on mercury speciation and emission while burning Kentucky and Illinois coals with different chlorine contents were performed in a 100-MWe pulverized-coal boiler with low-NO x burners. Seven coals were used during the tests and were grouped into two sets for comparison, with the baseline coal being shared between the two sets. The first set of four coals was used to investigate the effect of chlorine in coal on mercury emission and its speciation by selecting coals with similar mercury contents and different chlorine contents. The four coals in the second set were selected to investigate the effect of mercury contents in coal on mercury emission and its speciation by choosing coals with similar chlorine contents and different mercury contents. The mercury concentration and speciation in the flue gas were determined using the American Society for Testing and Materials (ASTM) standard Ontario Hydro Method (OHM) and a PS Analytical Semi-continuous Emissions Monitoring (SCEM) system. Flue gas samplings were performed at two locations: upstream before the electrostatic precipitator (ESP) inlet and downstream after the ESP outlet. A thorough comparison between the two monitoring methods was made. A sampling bias was found in the OHM sampling performed at the ESP inlet, because of the accumulation of ash with a high carbon content on the OHM filter. An ash-free sampling probe should be used with the OHM impinger train for obtaining accurate mercury information whenever ash concentrations are high. Mercury emission and speciation for the seven test coals at the ESP outlet location are described and discussed. Results indicate that the coal chlorine content has an impact on the mercury oxidation processes, which are mitigated by high concentrations of SO 2 . It is speculated that SO 2 limits Cl 2 formation.
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How this classification was reachedexpand
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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".