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Record W2028521050 · doi:10.1021/ie010121n

A Comparison of Two- and Single-Phase Models for Fluidized-Bed Reactors

2001· article· en· W2028521050 on OpenAlexaff
Navid Mostoufi, Heping Cui, Jamal Chaouki

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2001
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Mixing
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsButaneFluidized bedCatalysisMass transferPlug flowBubble column reactorMaleic anhydrideChemistryPhase (matter)Plug flow reactor modelBubbleChemical engineeringCountercurrent exchangeTrickle-bed reactorYield (engineering)ThermodynamicsMaterials scienceContinuous stirred-tank reactorChromatographyOrganic chemistryPhysical chemistryMechanicsMetallurgy

Abstract

fetched live from OpenAlex

Simulations of a bubbling/turbulent fluidized-bed reactor have been studied using the catalytic oxidation of n -butane to maleic anhydride (MAN) in the presence of a vanadium phosphorus oxide catalyst. The performance of the reactor was investigated using three different models: (a) a simple two-phase flow model, (b) a dynamic two-phase structure model, and (c) a plug-flow model. The simple two-phase model was found to underpredict the performance of the fluidized-bed reactors because of the oversimplified assumptions involved in this model. By analyzing the mass transfer in the two-phase models, it was shown that the conversion of reactants occurs mainly in the emulsion phase at low gas velocities and in the bubble phase at high gas velocities. The performance of the reactor, in terms of n -butane conversion, yield of MAN, and selectivity of produced MAN, was analyzed at different superficial gas velocities, initial n -butane concentrations, and deactivation rates of the catalyst.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.011
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.194
GPT teacher head0.395
Teacher spread0.201 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations72
Published2001
Admission routes1
Has abstractyes

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