Bibliographic record
Abstract
The measurement and control of mercury emissions from utility power plants continue to be the subjects of much study. In a program funded by the Department of Energy, Physical Sciences Inc. (PSI) is developing a mercury sorbent using a zeolite material with a proprietary agent for improved capture of elemental as well as oxidized mercury. Previous research at PSI has shown the feasibility of using zeolites for mercury capture. Results of this work showed that a treated zeolite sorbent performed as well as a treated activated carbon in removing total mercury from the flue gas, and that treating the zeolite improved elemental mercury capture compared to the untreated zeolite. In the current work two types of treated zeolite sorbent and one type of activated carbon were injected into flue gases from combustion of a Pittsburgh seam bituminous coal with the purpose of evaluating sorbent efficiency under conditions that approximate full-scale utilities with electro-static precipitators. The gas temperatures and residence times were similar to those found in power plant flue gas ducts and were in the range of 130 to 200°C and 2 s, respectively. The mercury concentration varied in the range of 30 to 60 μg/m. As in the previous fixed bed work, mercury concentration and speciation were measured using the modified Ontario Hydro Method. The mercury removal efficiencies in the experiments were zeolite sorbent was used varied in the range of 45 to 92% for sorbent to Hg ratios in the range of 5,000 to 96,000. When no sorbent was injected, ash did remove some mercury, but in the presence of sorbent the role of ash in mercury removal appeared to diminish, presumably due to the higher reactivity of the sorbent with respect to the ash.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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) | 0.001 | 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".