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Record W2054121618 · doi:10.1002/cjce.20068

Simulation of autothermal reforming in a staged‐separation membrane reactor for pure hydrogen production

2008· article· en· W2054121618 on OpenAlexaffvenue
Anwu Li, C. Jim Lim, Tony Boyd, John R. Grace

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2008
Typearticle
Languageen
FieldChemical Engineering
TopicCatalysts for Methane Reforming
Canadian institutionsUniversity of British ColumbiaMembrane Reactor Technologies (Canada)
Fundersnot available
KeywordsMethane reformerHydrogen productionSteam reformingMembrane reactorHydrogenMethaneMembraneOxygenChemical engineeringYield (engineering)CatalysisMaterials scienceChemistryProcess engineeringWaste managementEngineeringOrganic chemistryComposite material

Abstract

fetched live from OpenAlex

Abstract Steam methane reforming with oxygen input is simulated in staged‐separation membrane reactors. The configuration retains the advantage of regular membrane reactors for achieving super‐equilibrium conversion, but reaction and membrane separation are carried out in two separate units. Equilibrium is assumed in the models given the excess of catalyst. The optimal pure hydrogen yield is obtained with 55% of the total membrane area allocated to the first of two modules. The performance of the process with pure oxygen input is only marginally better than with air. Oxygen must be added in split mode to reach autothermal operation for both reformer modules, and the oxygen input to each module depends on the process conditions. The effects of temperature, steam‐to‐carbon ratio and pressure of the reformer and the area of the membrane modules are investigated for various conditions. Compared with a traditional reformer with an ex situ membrane purifier downstream, the staged reactor is capable of much better pure hydrogen yield for the same autothermal reforming operating conditions.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.246
Threshold uncertainty score0.458

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.017
GPT teacher head0.237
Teacher spread0.220 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations5
Published2008
Admission routes2
Has abstractyes

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