Mercury Removal Experiments for a Full-scale Coal-fired Plant with Activated Carbon Injection
Bibliographic record
Abstract
Mercury(Hg)emission from coal-fired power plants has attracted worldwide attentions.Main removal means have been oriented to active control methods.Literatures indicated that the fly ash from coal combustion had restricted mercury sorption capacity depending on the different coal rank.Injection of activated carbon(ACI)upstream of an electrostatic precipitator and combustion modifications and optimization(CMO)for boiler are convenient and economical retrofit control technologies that have potential applications to a large portion of all coal-fired power plants in the world that are not equipped with air pollution control devices,such as flue gas desulfurization scrubbers and nitride oxides selective catalytic reduction equipments.During CMO and ACI to remove mercury from the flue gas,experiments were done for a tangentially fired furnace with the capacity of 250MWe at a full-scale coal-fired power plant in Massachusetts,USA when the Eastern low-Hg coal and the Far East high-Hg coal were burnt.Mercury emissions were determined simultaneously by Ontario Hydro Method(OHM)and Semi-Continuous Emission Monitors(SCEM)at the air preheater(APH)inlet and the electrostatic precipitator(ESP)outlet.Removal efficiency for mercury total was calculated based on the experiments.Mercury total concentrations and removal efficiencies for the unit were also exponentially expressed with the ACI quantities.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".