Bibliographic record
Abstract
Mercury is a known pollutant that has detrimental effect on human health and environment. The anthropogenic emissions of mercury account for 10 to 30% of worldwide mercury emissions. There is a need to control/reduce anthropogenic mercury emissions. Many mercury control technologies are available but their effectiveness is dependent on the chemical form of mercury, because different chemical forms of mercury have different physical and chemical properties. Mercury leaves the boiler in its elemental form but goes through various transformations in the post-combustion zone. There is a need to understand how fly ash and flue gas composition affect speciation, partitioning, and reactions of mercury under the full range of post-combustion zone conditions. This knowledge can then be used to predict the chemical transformation of mercury (elemental, oxidized or particulate) in the post combustion zone and thus help with the control of mercury emissions from coal-burning power plants. To accomplish this goal present study was conducted using five coal fly ashes. These ashes were characterized and their catalytic activity was compared under selected reaction conditions in a fixed bed reactor. Based on the results from these fly ash experiments, three key components (carbon, iron oxide and calcium oxide) were chosen. These three components were then used to prepare model fly ashes. Silica/alumina was used as a base for these model fly ashes. One, two or three component model fly ashes were then prepared to investigate mercury transformation reactions. The third set of experiments was performed with CuO and CuCl2 catalysts to further understand the mercury oxidation process. Based on the results of these three studies the key components were predicted for different fly ash compositions under variety of flue gas conditions. A fixed bed reactor system was used to conduct this study. In all the experiments, the inlet concentration of Hg0(g) was maintained at 35 {micro}g/m3 using a diffusion tube as the source of Hg0(g). All experiments were conducted using 4% O2 in nitrogen mix as a reaction gas, and other reactants (HCl, H2O and SO2, NO2, Br2) were added as required. The fixed bed reactor was operated over a temperature range of 200 to 400 C. In each experiment, the reactor effluent was analyzed using the modified Ontario-Hydro method. After each experiment, fly ash particles were also analyzed for mercury. The results show that the ability of fly ash to adsorb and/or oxidize mercury is primarily dependent on its carbon, iron and calcium content. There can be either one or more than one key component at a particular temperature and flue gas condition. Surface area played a secondary role in effecting the mercury transformations when compared to the concentration of the key component in the fly ash. Amount of carbon and surface area played a key important role in the adsorption of mercury. Increased concentration of gases in the flue gas other than oxygen and nitrogen caused decreased the amount of mercury adsorbed on carbon surface. Mercury adsorption by iron oxide primarily depended on the crystalline structure of iron oxide. {alpha}-Iron oxide had no effect on mercury adsorption or oxidation under most of the flue gas conditions, but ?-iron oxide adsorbed mercury under most of the flue gas conditions. Bromine is a very good oxidizing agent for mercury. But in the presence of calcium oxide containing fly ashes, all the oxidized mercury would be reduced to elemental form. Among the catalysts, it was observed that presence of free lattice chlorine in the catalyst was very important for the oxidation of mercury. But instead of using the catalyst alone, using it along with carbon may better serve the purpose by providing the adsorption surface for mercury and also some extra surface area for the reaction to occur (especially for fly ashes with low surface area).
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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".