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Record W2314140310 · doi:10.1021/ie301361p

Prediction of Solids Accumulation in Slurry Bubble Columns with Polydispersed Solid Loadings

2012· article· en· W2314140310 on OpenAlexaff
Ion Iliuta, Faı̈çal Larachi

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2012
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Mixing
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsSlurryBubbleTurbulenceMechanicsParticle (ecology)Dispersion (optics)Particle-size distributionAdvectionMaterials scienceThermodynamicsParticle sizeChemistryPhysics

Abstract

fetched live from OpenAlex

A core-annulus multicompartment pseudo-2D two-bubble class model accounting for gas and slurry recirculation and coupled with catalyst sedimentation/advection/dispersion/lateral exchange balance equations for monodispersed and polydispersed solid particle systems was proposed to study the hydrodynamics of slurry bubble column facing solids accumulation in the reactor. The distribution of bubbles was represented by a bimodal two-class distribution: large and small bubbles. The model was coupled with a Prandtl–Nikuradse mixing length shear turbulence model or Sato bubble-induced and shear turbulence model to obtain the radial slurry velocity profile. A criterion for setting the onset of particles accumulation in the slurry bubble column was established. In solids polydispersed feeds, the influence of operating conditions (column diameter and height, solids feed mass flux and concentration, particle size distribution, gas velocity, and liquid viscosity) on the threshold particle size corresponding to the onset of particles accumulation in the reactor was analyzed systematically.

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)0.001
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 score0.252
Threshold uncertainty score0.737

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.001
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 head0.100
GPT teacher head0.312
Teacher spread0.212 · 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
Published2012
Admission routes1
Has abstractyes

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