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Record W2128817243 · doi:10.1002/ceat.201200029

Simulation of a Compact Multichannel Membrane Reactor for the Production of Pure Hydrogen via Steam Methane Reforming

2012· article· en· W2128817243 on OpenAlexafffund
Alexandre Vigneault, S.S.E.H. Elnashaie, John R. Grace

Bibliographic record

VenueChemical Engineering & Technology · 2012
Typearticle
Languageen
FieldChemical Engineering
TopicCatalysts for Methane Reforming
Canadian institutionsUniversity of British Columbia
FundersFonds de recherche du Québec – Nature et technologiesNatural Sciences and Engineering Research Council of Canada
KeywordsSteam reformingMembrane reactorIsothermal processHydrogen productionMethaneMethane reformerHydrogenChemistryCombustionCatalysisPlug flow reactor modelChemical engineeringNuclear engineeringSyngasThermodynamicsMaterials scienceWaste managementContinuous stirred-tank reactorEngineeringOrganic chemistryPhysical chemistryPhysics

Abstract

fetched live from OpenAlex

Abstract A steady‐state 2D model is developed for a multichannel membrane reactor (MCMR) to produce pure hydrogen. The model includes one reforming channel coupled with a PdAg membrane to produce H2 and one combustion channel to generate the heat needed for the reforming. Both isothermal and non‐isothermal simulations are applied in designing a laboratory‐scale proof‐of‐concept reactor. Isothermal sensitivity analysis indicates parameter adjustments practically available to improve reactor performance. In non‐isothermal simulations, catalyst layer thickness and kinetic pre‐exponential factor are varied along the reactor length. Predictions indicate that the reforming methane conversion increased from 74 % to 91 %, while avoiding hot spots. Compared with other membrane reactors, the MCMR has the potential for one to two orders of magnitude higher H2 production per reactor volume and per mass of 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)0.000
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 score0.023
Threshold uncertainty score0.046

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.015
GPT teacher head0.250
Teacher spread0.235 · 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

Citations23
Published2012
Admission routes2
Has abstractyes

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