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Record W3012306577 · doi:10.1002/cjce.23748

Investigation of fluidized bed behaviour using electrical capacitance tomography

2020· article· en· W3012306577 on OpenAlexvenueno aff
Abdelwahid Azzi, Hiba Bouyahiaoui, Abdallah S. Berrouk, A. Hunt, I.S. Lowndes

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2020
Typearticle
Languageen
FieldEngineering
TopicElectrical and Bioimpedance Tomography
Canadian institutionsnot available
Fundersnot available
KeywordsFluidizationSluggingElectrical capacitance tomographyFluidized bedMaterials scienceDistributorPorositySuperficial velocityAmplitudeMechanicsVoid (composites)ElectrodeComposite materialCapacitanceThermodynamicsChemistryPhysicsFlow (mathematics)Optics

Abstract

fetched live from OpenAlex

Abstract The temporal and cross‐sectional distributions of particles in a 127 mm diameter fluidized bed have been obtained using a new generation, high‐speed electrical capacitance tomography. Two planes of eight electrodes were used and mounted at 160 and 660 mm from the gas distributor which was a 3 mm thick porous plastic plate (maximum pore size of 50‐70 μ m). 3 mm diameter, nearly‐spherical polyethylene granules made up the bed. Experiments at sampling frequencies of 200‐2000 cross‐sections per second and gas superficial velocities from just below the minimum fluidization to 83% above minimum fluidization velocities were used. The time series of the cross‐sectional average void fractions have been examined both directly and in amplitude and frequency space. The last two used probability density functions and power spectral densities. The information gathered shows that the fluidized bed was operating in the slugging mode, which is not surprising given the size of the particles. It has been found that an increase in the excess gas velocity above the minimum fluidization velocity resulted in an increase in the mean void fraction, an increase in the length and velocity of the slug bubbles as well as the bed height, and a slight decrease in the slug frequency. The results are presented in a level of detail suitable for comparison with later numerical simulation.

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 score0.035
Threshold uncertainty score0.470

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 head0.014
GPT teacher head0.174
Teacher spread0.160 · 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

Citations14
Published2020
Admission routes1
Has abstractyes

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