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Record W2316508361 · doi:10.1021/ie100954a

Modeling of a Fluidized Bed Membrane Reactor for Hydrogen Production by Steam Reforming of Hydrocarbons

2011· article· en· W2316508361 on OpenAlexaff
M.A. Rakib, John R. Grace, C. Jim Lim, S.S.E.H. Elnashaie

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2011
Typearticle
Languageen
FieldChemical Engineering
TopicCatalysts for Methane Reforming
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsSteam reformingFluidized bedFluidizationMethaneMass transferHydrogen productionPlug flowChemistryMembrane reactorThermodynamicsPropanePlug flow reactor modelChemical engineeringHydrogenContinuous stirred-tank reactorChromatographyOrganic chemistry

Abstract

fetched live from OpenAlex

A bubbling fluidized bed membrane reactor (FBMR) is modeled to estimate and predict the steam reforming of hydrocarbons. A two-phase fluidization model is used, with both the bubble and dense phases in plug flow. Diffusional mass transfer, as well as bulk convective mass flow between the phases, is incorporated to account for reactions occurring predominantly in the dense phase and increases in molar flow due to the reaction. Steam reforming of higher hydrocarbons is limited by the thermodynamic equilibrium of the methane steam reforming and water−gas shift reactions. The model predicts flexible feedstock capabilities, showing that most of the reactor does not actually see the higher hydrocarbon feed. With a single fitted constant to account for membrane effectiveness in the fluidized bed relative to that in the absence of particles, good agreement is obtained between model predictions and reactor performance of the reforming of methane, propane, and heptane.

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)0.000
metaresearch head score (Gemma)0.001
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: Empirical
Teacher disagreement score0.037
Threshold uncertainty score0.074

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0030.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.115
GPT teacher head0.309
Teacher spread0.194 · 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

Citations16
Published2011
Admission routes1
Has abstractyes

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