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Record W1989565012 · doi:10.1021/ie020780f

Application of the Generic Fluidized-Bed Reactor Model to the Fluidized-Bed Membrane Reactor Process for Steam Methane Reforming with Oxygen Input

2003· article· en· W1989565012 on OpenAlexaff
Ibrahim Abba, John R. Grace, Hsiaotao T. Bi

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2003
Typearticle
Languageen
FieldChemical Engineering
TopicCatalysts for Methane Reforming
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsFluidized bedMembrane reactorMethanePlug flow reactor modelChemistrySteam reformingContinuous stirred-tank reactorHydrogenNuclear engineeringHydrogen productionChemical engineeringMaterials scienceWaste managementEngineeringOrganic chemistry

Abstract

fetched live from OpenAlex

A generic fluidized-bed reactor model (Abba et al. Chem. Eng. Sci . 2002, 57, 4797-4807; AIChE J . 2003, in press) is adapted to evaluate the performance of a fluidized-bed membrane reactor for steam reforming with oxygen input in a large-scale unit (16 m high and 2 m wide) described by Adris and Grace ( Ind. Eng. Chem. Res . 1997, 36, 4549-4556). This allows flow regimes beyond bubbling to be modeled and facilitates the treatment of the impacts of changes in volumetric flow due to variations in molar flow, temperature, and hydrostatic pressure. Improvement in the reactor performance is shown when one considers these changes. Permselective membrane tubes are shown to substantially improve the performance of the reactor. The simulation results show that an ultrathin membrane coating could result in reversal in hydrogen diffusion with height, especially at elevated temperatures. The influences on the reactor performance of several other parameters such as the superficial gas velocity and steam-to-carbon ratio are also examined. Given the right combinations of key operating parameters such as the methane-to-oxygen ratio, feed temperature, and reactor temperature, the reactor can be operated autothermally.

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)0.003
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.013
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.076
GPT teacher head0.323
Teacher spread0.247 · 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.

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

Citations20
Published2003
Admission routes1
Has abstractyes

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