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Record W2383693676

Experimental Research and Microscopic Mechanism Analysis on Simultaneous Mercury Removal by Novel Integrated Semi-dry Desulfurization Systems

2009· article· en· W2383693676 on OpenAlexaboutno aff
Yunjun Wang, Duan Yu-feng, Yang Li-guo, Chengjun Wu, Qian Wang

Bibliographic record

VenueJournal of Power Engineering · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicMercury impact and mitigation studies
Canadian institutionsnot available
Fundersnot available
KeywordsFlue-gas desulfurizationMercury (programming language)LimeFlue gasElectrostatic precipitatorFly ashCoalChemistryPorosityBoiler (water heating)Waste managementScanning electron microscopeHydrateChemical engineeringMineralogyMetallurgyMaterials scienceComposite material
DOInot available

Abstract

fetched live from OpenAlex

Coal,slag,and fly ash samples were taken from a utility boiler equipped with the NID(Novel Integrated Desulfurization) system,and the mercury contents of these samples were measured.Ontario-Hydro method was applied to determine the mercury patterns in flue gas before and after NID system.The physicochemical characteristics of the ashes in NID system were analyzed by specific surface and porosity analyzer,X ray diffractometer,scanning electron microscope and spectrometer,and the microscopic mechanism of simultaneous mercury removal by the novel integrated semi-dry desulfurization system was revealed.Results indicate that the lime hydrate and quick lime can mix thoroughly in the novel integrated semi-dry desulfurization system and the circulation ratio of fly ash in the desulfurization tower is high.There is always fresh Ca(OH)_2 on the surface of the mixture ash.Furthermore calcium silicate hydrate is formed in the desulfurization tower and particle agglomeration is serious,which is very beneficial for SO_2 and mercury removal from the flue gas.The mercury removal efficiency of the novel integrated semi-dry desulfurization system for flue gas is as high as 86.6%~92.2%,and the control effect for mercury emission of the coal-fired power station is obvious.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.122
Threshold uncertainty score0.418

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.015
GPT teacher head0.285
Teacher spread0.269 · 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 designBench or experimental
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

Citations1
Published2009
Admission routes1
Has abstractyes

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