Understanding the effects of sulfur on mercury capture from coal-fired utility flue gases
Bibliographic record
Abstract
Coal combustion continues to be a major source of energy throughout the world and is the leading contributor to anthropogenic mercury emissions. Effective control of these emissions requires a good understanding of how other flue gas constituents such as sulfur dioxide (SO2) and sulfur trioxide (SO3) may interfere in the removal process. Most of the current literature suggests that SO2 hinders elemental mercury (Hg0) oxidation by scavenging oxidizing species such as chlorine (Cl2) and reduces the overall efficiency of mercury capture, while there is evidence to suggest that SO2 with oxygen (O2) enhances Hg0 oxidation by promoting Cl2 formation below 100 °C. However, studies in which SO2 was shown to have a positive correlation with Hg0 oxidation in full-scale utilities indicate that these interactions may be heavily dependent on operating conditions, particularly chlorine content of the coal and temperature. While bench-scale studies explicitly targeting SO3 are scarce, the general consensus among full-scale coal-fired utilities is that its presence in flue gas has a strong negative correlation with mercury capture efficiency. The exact reason behind this observed correlation is not completely clear, however. While SO3 is an inevitable product of SO2 oxidation by O2, a reaction that hinders Hg0 oxidation, it readily reacts with water vapor, forms sulfuric acid (H2SO4) at the surface of carbon, and physically blocks active sites of carbon. On the other hand, H2SO4 on carbon surfaces may increase mercury capacity either through the creation of oxidation sites on the carbon surface or through a direct reaction of mercury with the acid. However, neither of these beneficial impacts is expected to be of practical significance for an activated carbon injection system in a real coal-fired utility flue gas.
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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) | 0.000 | 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 | 0.001 |
| 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".