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Record W2953519564 · doi:10.1002/cjce.23587

Removal model of fine particles from the flue gas of the coal‐fired power plant in a water‐sparged aerocyclone

2019· article· en· W2953519564 on OpenAlexvenueno aff
Zhiliang Cheng, Jiang Li, Yongwei Cai, Cunfang Lu, Gang Li, Xuejun Quan

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2019
Typearticle
Languageen
FieldEngineering
TopicAerosol Filtration and Electrostatic Precipitation
Canadian institutionsnot available
FundersNational Natural Science Foundation of China
KeywordsInletFlue gasPower stationParticle (ecology)Environmental scienceCoalChemistryExhaust gasFlue-gas desulfurizationThermodynamicsEngineeringPhysicsMechanical engineeringGeology

Abstract

fetched live from OpenAlex

This paper describes a novel system of a water‐sparged aerocyclone (WSA) for fine particles (FP) removal in the air stream from a coal‐fired power plant. The effects of operating parameters, including inlet FP concentration ( C 0 (FP)), water jet velocity ( U L ), and air inlet velocity ( U G ) on the removal efficiency of FP ( RE FP ) were investigated. The change of morphology, particle size distribution, and content of heavy metals of the FP samples before and after removal in the WSA were also compared. The results show that the RE of the FP and PM2.5 can reach as high as 99.36 and 99 %, respectively, under optimal conditions. A regression removal model of RE FP = 85.08 × Re G 0.0187 × Re L 0.0037 × ( C 0 (FP)/ ρ FP ) 0.0048 was proposed to predict RE FP , and the calculated RE FP by the model is in agreement with experimental data with deviations of ±0.5 %. This new technology provides an alternative approach for FP removal from exhaust gas and exhibits significant potential for industrial application.

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 score0.057
Threshold uncertainty score0.197

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 head0.007
GPT teacher head0.160
Teacher spread0.153 · 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

Citations10
Published2019
Admission routes1
Has abstractyes

Explore more

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