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Record W2966175810 · doi:10.11159/mmme19.111

Application of a Wet Resonance Grille in a Ventilating Shaft and a Model for Calculating the Efficiency of Dust Removal

2019· article· en· W2966175810 on OpenAlexvenueno aff
Haiqiao Wang, Fangxing Chen, Shiqiang Chen

Bibliographic record

VenueProceedings of the World Congress on Mechanical, Chemical, and Material Engineering · 2019
Typearticle
Languageen
FieldEngineering
TopicCyclone Separators and Fluid Dynamics
Canadian institutionsnot available
FundersHunan University of Science and TechnologyNatural Science Foundation of Hunan ProvinceHunan UniversityNational Natural Science Foundation of China
KeywordsResonance (particle physics)Materials scienceEnvironmental scienceAcousticsComputer sciencePhysicsAtomic physics

Abstract

fetched live from OpenAlex

The dust-removal processes of a spray, wet resonance grille, and water film are considered in an investigation of factors that affect the efficiency of wet resonance-grille dust-removal technology. The dust removal efficiency of the liquid droplet group is deduced adopting limit trajectory theory. A formula for calculating the efficiency of dust removal by a single resonance grille and water film is obtained employing resonance grille filtering and liquid saturation theory. On the basis of Strauss tandem theory, a mathematical model of the total dust removal efficiency is proposed and numerically calculated. Numerical results show that the grille distance-diameter ratio ranges from 2.0 to 2.5 when the liquid-gas ratio is 0.0263l/m 3 under the condition of low filtration velocity. As the distance-diameter ratio decreases, the filling ratio increases, the grille diameter decreases, and the overall dust removal efficiency increases. The method of calculating the efficiency proposed in this paper can guide engineering practice.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.586
Threshold uncertainty score0.404

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 head0.004
GPT teacher head0.198
Teacher spread0.194 · 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

Citations0
Published2019
Admission routes1
Has abstractyes

Explore more

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