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Record W1989584302 · doi:10.2202/1542-6580.1000

Modeling of Autothermal Steam Methane Reforming in a Fluidized Bed Membrane Reactor

2003· article· en· W1989584302 on OpenAlexaff
Meltem Doğan, Duško Pošarac, John R. Grace, Alaa-Eldin M. Adris, C. Jim Lim

Bibliographic record

VenueInternational Journal of Chemical Reactor Engineering · 2003
Typearticle
Languageen
FieldChemical Engineering
TopicCatalysts for Methane Reforming
Canadian institutionsMembrane Reactor Technologies (Canada)University of British Columbia
Fundersnot available
KeywordsMethane reformerSteam reformingMembrane reactorHydrogenChemical engineeringHydrogen productionMethaneFluidized bedMaterials scienceEndothermic processOxygenChemistryWaste managementOrganic chemistryAdsorption

Abstract

fetched live from OpenAlex

Abstract Fluidized bed reactors for steam methane reforming, with and without immersed membrane surfaces for withdrawal of hydrogen, are modeled with oxygen added in order to provide the endothermic heat required by the reforming reactions. Porous alumina, palladium and palladium-coated high-flux tubes are investigated as separation materials, the latter two being permselective. Hydrogen yield and permeate hydrogen molar flow are predicted to decrease with increasing oxygen flow, and to increase with temperature. When the steam-to-carbon ratio increases, permeate hydrogen yield decreases slightly, while the total hydrogen yield increases for all configurations. The flow of oxygen required to achieve autothermal conditions depends on such factors as the reactor temperature, steam-to-carbon ratio and preheating of the feed.

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 score0.032
Threshold uncertainty score0.065

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 science0.0010.001
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 head0.013
GPT teacher head0.252
Teacher spread0.239 · 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
Published2003
Admission routes1
Has abstractyes

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Same venueInternational Journal of Chemical Reactor EngineeringSame topicCatalysts for Methane ReformingFrench-language works237,207