Research on the effect of additives on mercury speciation in coal‐fired derived flue gas
Bibliographic record
Abstract
In order to study the effect of additives on mercury speciation in coal‐fired derived flue gases, the additives experiment was conducted in one 110MW coal‐fired power plant, the additives including calcium bromide and iron oxide. Taking advantage of the Ontario Water Act (OHM) and sorbent tube method, mercury at the denitration (SCR) entrance and export and desulfurization (WFGD) entrance was sampled and the variation of mercury before and during adding the additives was analyzed. The results show that the additives of calcium bromide can oxidize elemental mercury and contribute to increasing the proportion of divalent mercury, and in denitration device, the ratio of divalent mercury in flue gas shows an increasing trend along with the increasing of calcium bromide. However, there is no such trend appearing at desulfurization entrance. In addition, the additives of iron oxide have no obvious effect on the oxidation of elemental mercury in flue gas. Adding a small amount of iron oxide has little effect on the form of mercury, and may even reduce the proportion of divalent mercury. © 2016 American Institute of Chemical Engineers Environ Prog, 35: 1566–1574, 2016
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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 | 0.001 | 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 | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".