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Record W2965284255 · doi:10.1021/acsomega.9b01892

Studies on Bed Density in a Gas-Vibro Fluidized Bed for Coal Cleaning

2019· article· en· W2965284255 on OpenAlexaff
Chenyang Zhou, Liang Dong, Yuemin Zhao, Xuchen Fan

Bibliographic record

VenueACS Omega · 2019
Typearticle
Languageen
FieldEngineering
TopicGranular flow and fluidized beds
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsFluidized bedBeneficiationFluidizationCoalBubbleMaterials scienceMechanicsPetroleum engineeringWaste managementMetallurgyGeologyEngineeringPhysics

Abstract

fetched live from OpenAlex

Dry coal beneficiation has played a vital role during the initial stage of coal cleaning in recent years. Successful utilization of a gas–solid fluidized bed for >6 mm coal cleaning motivates scholars to explore the possibility of fine coal cleaning using dry beneficiation methods. In this study, pulsed flow was introduced into a fluidized bed to optimize bubble behavior, thus improving the density stability. The equation of minimum fluidization velocity (Umfp) in a gas-vibro fluidized bed for coal preparation was investigated theoretically. An equation has been proposed for predicting Umfp while considering changes in the friction coefficient (Cf) in the gas-vibro fluidized bed. Based on two-phase theory, the correlation of bed density was determined by analyzing the bubble behavior in the gas-vibro fluidized bed. The theoretical bed density was then compared with experimental data of the bed density and separation density. The predicted bed density in monodisperse and binary dense medium systems was found to be consistent with the experimental results. Overall, the equation of bed density is suitable for estimating the separation density in the gas-vibro fluidized bed.

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 score0.007
Threshold uncertainty score0.794

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 head0.019
GPT teacher head0.249
Teacher spread0.230 · 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

Citations26
Published2019
Admission routes1
Has abstractyes

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