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Record W2017112791 · doi:10.1002/cjce.21854

Prediction of bed fluctuation and expansion ratios for homogeneous ternary mixtures of spherical glass bead particles in a three‐phase fluidised bed

2013· article· en· W2017112791 on OpenAlexvenueno aff
D.T.K. Dora, Yashobanata Kumar Mohanty, Gopendra Kishore Roy, Bidyapati Sarangi

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2013
Typearticle
Languageen
FieldEngineering
TopicGranular flow and fluidized beds
Canadian institutionsnot available
Fundersnot available
KeywordsTernary operationHomogeneousBeadParticle (ecology)Materials scienceThermodynamicsPhase (matter)Series (stratigraphy)MechanicsParticle sizeMineralogyChemistryComposite materialPhysicsGeology

Abstract

fetched live from OpenAlex

Abstract The prediction of the fluctuation and expansion ratios in three‐phase fluidised beds of homogeneous particles is of prime importance in designing the equipments and evaluating the efficiency of a number of physical and/or chemical operations of industrial importance. A clear knowledge of the hydrodynamic behaviour in a three‐phase fluidised bed is a prerequisite for this purpose. To explore this, a series of experiments have been carried out for homogeneous well‐mixed ternary mixtures of three different sizes of glass beads of varying compositions in a three‐phase fluidised bed. The hydrodynamic characteristics determined include the bed fluctuation and expansion ratios. The dependence of these quantities on the average particle diameter, superficial gas velocity and initial static bed height has been discussed. Based on dimensional and statistical analyses, correlations have been developed with the system parameters, viz., average particle diameter, initial static bed height and superficial velocity of the fluidising medium. Experimental values of bed fluctuation and expansion ratios have been found to agree well with those calculated from developed correlations.

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 score0.297
Threshold uncertainty score0.340

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 head0.010
GPT teacher head0.188
Teacher spread0.178 · 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

Citations5
Published2013
Admission routes1
Has abstractyes

Explore more

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