Influence and Control of Electrostatic Precipitators and Wet Flue Gas Desulfurization Systems on the Speciation of Mercury in Flue Gas
Bibliographic record
Abstract
Tests were carried out for the concentration and speciation of mercury in the flue gas before and after 6 sets of typical electrostatic precipitator(ESP) and wet flue gas desulfurization(WFGD) system of coal fired power stations by use of Ontario Hydro method and on-line mercury monitoring technology.The influence and control ability of the two devices on speciation transformation of mercury in the flue gas were also studied.Results show that collection of fly ash by ESP directly decreases the proportion of particle mercury in the flue gas.For the typical coal-fired boilers that had been tested,the average proportion of particle mercury in coal-fired flue gas before ESP is about 30%,and decreases to about 5% after ESP.The speciation of mercury in the flue gas changes greatly after being washed by WFGD system.Almost all of the bivalent mercury is captured.The higher the proportion of bivalent mercury is in the flue gas entering the WFGD system,the higher the efficiency is for the mercury removal in the flue gas by WFGD system.The coal-fired power plants equipped with disposal devices for tail flue gas,such as selective catalytic reduction(SCR) denitrator +ESP+WFGD,can well control the mercury emission from the flue gas.
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 | 0.001 |
| 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".