Heterogeneous Catalytic Elemental Mercury Oxidation in Coal Combustion Flue Gas
Bibliographic record
Abstract
The new Mercury and Air Toxics Standards issued by US EPA require the reduction of mercury emissions from coal-fired power plants by 90% starting from 2016.Oxidizing elemental mercury using the HCl that exists in the flue gas or additional halogen and catalysts, followed by oxidized mercury capture in the wet Flue Gas Desulfurizer (FGD), is a viable option for mercury removal in coal-fired power plants.The aim of this study is to develop effective and reliable mercury oxidation catalysts, advance the mechanistic understandings of heterogeneous mercury oxidation, and obtain information on heterogeneous mercury oxidation kinetics.CuCl 2 supported on γ-Al 2 O 3 showed excellent Hg(0) oxidation performance and SO 2 resistance at 140 ° C.After extensive characterizations of the CuCl 2 /γ-Al 2 O 3 catalyst, the existence of multiple copper species was identified.It was found that CuCl 2 forms inert copper aluminate on the surface of γ-Al 2 O 3 at lower loadings.At higher loadings, CuCl 2 exists in a highly dispersed amorphous form that is active for Hg(0) oxidation by working as a redox catalyst.The CuCl 2 /γ-Al 2 O 3 catalyst with high loadings has the potential to be used as a low temperature Hg(0) oxidation catalyst.RuO 2 catalyst was found to be an excellent Hg( 0) oxidation catalyst.When rutile TiO 2 was used as the catalyst support, RuO 2 formed well dispersed nano-layers due to the very similar crystal structures of RuO 2 and rutile TiO 2 , giving higher Hg(0) oxidation activity over anatase TiO 2 support.The RuO 2 /rutile TiO 2 catalyst showed good Hg(0) oxidation performance under sub-bituminous and lignite coal simulated flue gas conditions with low concentration of HCl or HBr gas.It also showed excellent resistance to SO 2 .The RuO 2 /rutile TiO 2 catalyst can be used at the tail end section of the SCR unit for Hg(0) oxidation.Chapter 6 Summary ..................
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 | 0.001 | 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".