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Record W2065414727 · doi:10.1021/ef060547k

Investigation of Mercury Transformation by HBr Addition in a Slipstream Facility with Real Flue Gas Atmospheres of Bituminous Coal and Powder River Basin Coal

2007· article· en· W2065414727 on OpenAlexaboutno aff
Yan Cao, Quanhai Wang, Chien‐Wei Chen, Bobby Chen, Martin Cohron, Yi-Chuan Tseng, Cheng-chung Chiu, Paul Chu, Wei-Ping Pan

Bibliographic record

VenueEnergy & Fuels · 2007
Typearticle
Languageen
FieldEnvironmental Science
TopicMercury impact and mitigation studies
Canadian institutionsnot available
Fundersnot available
KeywordsFlue gasMercury (programming language)Fly ashCoalBituminous coalChemistryChlorineEnvironmental chemistryWaste managementEnvironmental scienceMineralogy

Abstract

fetched live from OpenAlex

An investigation of speciated mercury transformation with the addition of hydrogen bromide (HBr) at elevated temperatures was conducted in a slipstream reactor with real flue gas atmospheres. A real flue gas atmosphere is composed of bituminous coal (with high sulfur and high chlorine contents) and Powder River Basin (PRB) coal (with lower sulfur and chlorine contents). The average sulfur, chlorine, and mercury contents in the tested bituminous coal were 1.31% and 1328 and 0.11 ppm and 0.61% and 170 and 0.08 ppm, respectively, for tested PRB coal. The average CaO, Fe 2 O 3, and loss on ignition in collected fly ash contents of tested bituminous coal were 1.71, 17.51, and 7.13% and 22.95, 4.91, and 0.64%, respectively, for tested PRB coal. The different contents of coal chlorine, CaO, and Fe 2 O 3 in fly ash can be attributed to the different mercury speciations at baseline tests for these two coals in this study. The addition of HBr concentrations into the flue gas was controlled in the 3−15 ppm range. Semi-continuous mercury emission monitors were used to check the variation of mercury speciation at sampling locations. The Ontario Hydro Method (ASTM D6784-02) was used for data validation or comparison. For both methods, a high temperature inertial sampling probe was used to minimize the interference between vapor phase mercury and fly ash. Its temperatures were controlled consistently with flue gas temperatures at their installation locations in the slipstream reactor. Test results indicated that adding HBr into the flue gas at several parts per million strongly impacted the mercury oxidation and adsorption, which were dependent upon temperature ranges. Higher temperatures (in the range of 300−350 °C) promoted mercury oxidation by HBr addition but did not promote mercury adsorption. Lower temperatures (in a range of 150−200 °C) enhanced mercury adsorption on the fly ash by adding HBr. Test results also verified effects of flue gas atmospheres on the mercury oxidation by the addition of HBr, which included concentrations of chlorine and sulfur in the flue gas. Chlorine species seemed to be involved in the competition with bromine species in the mercury oxidation process. With the addition of HBr at 3 ppm at a temperature of about 330 °C, the additional mercury oxidation could be reached by about 55% in a flue gas atmosphere by burning PRB coal in the flue gas and by about 20% in a flue gas by burning bituminous coal. These are both greater than the maximum gaseous HgBr 2 percentage in the flue gas (35% for PRB coal and 5% for bituminous coal) by thermodynamic equilibrium analysis predictions under the same conditions. This disagreement may indicate a greater complexity of mercury oxidation mechanisms by the addition of HBr. It is possible that bromine species promote activated chlorine species generation in the flue gas, where the kinetics of elemental mercury oxidation were enhanced. However, SO 2 in the flue gas may involve the consumption of the available activated chlorine species. Thus, the higher mercury oxidation rate by adding bromine under the flue gas by burning PRB coal may be associated with its lower SO 2 concentration in 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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.139
Threshold uncertainty score0.877

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.010
GPT teacher head0.213
Teacher spread0.203 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations27
Published2007
Admission routes1
Has abstractyes

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