PRELIMINARY FIELD EVALUATION OF MERCURY CONTROL USING COMBUSTION MODIFICATIONS
Bibliographic record
Abstract
In this project General Electric Energy and Environmental Research Corporation conducts a preliminary field evaluation of a novel technology, referred to as Hg/NO{sub x}, that can reduce emissions of both mercury (Hg) and oxides of nitrogen (NO{sub x}) from coal-fired power plants. The evaluation takes place in Green Station Unit 2 operated by Western Kentucky Energy. Reduction of Hg and NO{sub x} emissions in Unit 2 is achieved using coal reburning. Activities during first project year (January 23, 2003--January 22, 2004) included measurements of baseline Hg emissions in Unit 2 and pilot-scale testing. Baseline testing of Hg emissions in Green Unit 2 has been completed. Two fuels were tested with OFA system operating at minimum air flow. Mercury emissions were measured at ESP inlet and outlet, and at the stack using Ontario Hydro revised method. Testing demonstrated that baseline Hg reductions at ESP outlet and stack were 30-45% and 70-80%, respectively. Pilot-scale testing demonstrated good agreement with baseline measurements in Unit 2. Testing showed that fuel composition had an effect on the efficiency of Hg absorption on fly ash. Maximum achieved Hg removal in reburning was close to 90%. Maximum achieved Hg reduction at air staging conditions was 60%. Testing also demonstrated that lowering ESP temperature improved efficiency of Hg 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 | 0.001 | 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 | 0.001 | 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".