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Record W2561718996 · doi:10.1515/ijcre-2014-0106

Performance of a Catalytic Gas–Solid Fluidized Bed Reactor in the Presence of Interparticle Forces

2015· article· en· W2561718996 on OpenAlexafffund
Jaber Shabanian, Jamal Chaouki

Bibliographic record

VenueInternational Journal of Chemical Reactor Engineering · 2015
Typearticle
Languageen
FieldEngineering
TopicGranular flow and fluidized beds
Canadian institutionsPolytechnique Montréal
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsFluidized bedFlow (mathematics)Materials sciencePhase (matter)Yield (engineering)FluidizationCoatingMechanicsChemical engineeringChemistryWaste managementEngineeringNanotechnologyComposite materialPhysicsOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract The influence of interparticle forces (IPFs) on the hydrodynamics of a gas–solid fluidized bed was experimentally investigated with the help of a polymer coating approach. The results showed that the presence of IPFs in the bed can considerably change the hydrodynamic parameters. The tendency of the fluidizing gas passing through the bed in the emulsion phase increased with IPFs in the bubbling regime. The performance of a fluidized bed reactor was then studied through simulation of a reactive catalytic system using three different hydrodynamic models: (a) a simple two-phase flow model, (b) a dynamic two-phase flow model, and (c) a dynamic two-phase flow model, integrating the effects of superficial gas velocity and IPFs. The simple two-phase flow model was found to underestimate the reactor performance for catalytic reaction most likely due to the oversimplified assumptions involved in this model. Also, the simulation results showed that modification of the bed hydrodynamics due to IPFs resulted in a better performance for a bubbling fluidized bed reactor. This suggests that the hydrodynamic models should take into account the effects of superficial gas velocity and variation in the ratio of the magnitude of IPFs/hydrodynamic forces, due to any operational reason, to yield a more reliable evaluation of the performance of the fluidized bed reactor.

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)0.001
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 score0.054
Threshold uncertainty score0.368

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 science0.0010.000
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 head0.012
GPT teacher head0.238
Teacher spread0.225 · 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

Citations12
Published2015
Admission routes2
Has abstractyes

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