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Record W2080614180 · doi:10.2202/1542-6580.1303

Hydrogen Production in Fluidized Beds with In-Situ Membranes

2005· article· en· W2080614180 on OpenAlexaff
John R. Grace, S.S.E.H. Elnashaie, C. Jim Lim

Bibliographic record

VenueInternational Journal of Chemical Reactor Engineering · 2005
Typearticle
Languageen
FieldChemical Engineering
TopicCatalysts for Methane Reforming
Canadian institutionsUniversity of British Columbia
FundersKing Saud UniversityAuburn University
KeywordsHydrogen productionSteam reformingProcess engineeringFluidized bedMembrane reactorFlexibility (engineering)HydrogenProcess (computing)Waste managementEnvironmental scienceMaterials scienceEngineeringComputer scienceChemistry

Abstract

fetched live from OpenAlex

Fluidized Bed Membrane Reactors (FBMR) offer significant advantages for steam reforming and the production of hydrogen. Potential advantages include higher yields by reducing thermodynamic equilibrium limitations, process intensification by combining three vessels into one, reduced temperatures of operation, countering the adverse effects of pressure, virtually eliminating catalyst diffusional limitations, high productivity per unit volume of reformer, and flexibility in using alternative feedstocks. Realization of the FBMR process for hydrogen production requires that a number of unusual challenges in reactor design be met. This paper discusses the technical challenges and outlines key factors which are being addressed in providing the membranes, reactor configuration and integrity, catalyst, energy integration and operating conditions needed to establish an economically viable FBMR process.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.007
GPT teacher head0.230
Teacher spread0.223 · 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 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

Citations40
Published2005
Admission routes1
Has abstractyes

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