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Record W2329304768 · doi:10.1021/ie502082q

Kinetic Study of Hydrogen Production by the High Temperature Water Gas Shift Reaction of Reformate Gas in Conventional and Membrane Packed Bed Reactors over Ca-Promoted CeO<sub>2</sub>–ZrO<sub>2</sub> Supported Ni–Cu Catalyst

2014· article· en· W2329304768 on OpenAlexafffund
Ishioma Judith Oluku, Hussameldin Ibrahim, Raphael Idem

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2014
Typearticle
Languageen
FieldChemical Engineering
TopicCatalysts for Methane Reforming
Canadian institutionsUniversity of Regina
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCatalysisHydrogenChemistryCatalytic reformingActivation energyWater-gas shift reactionPacked bedHydrogen productionPermeationMembrane reactorYield (engineering)Atmospheric temperature rangeReaction rateMembraneAnalytical Chemistry (journal)Nuclear chemistryChemical engineeringChromatographyMaterials sciencePhysical chemistryThermodynamicsOrganic chemistryMetallurgy

Abstract

fetched live from OpenAlex

The kinetics of the high temperature water gas shift (HTWGS) reaction was performed in a hydrogen selective membrane reactor (MR) and compared with that in a packed bed tubular reactor (PBTR) at a pressure range of 150–250 psi, temperature range of 400–500 °C and weight of catalyst/CO molar flow rate ratio of 1.1–2.7 (g-cat·hr)/mol using reformate gas over a Ni–Cu catalyst on Ca-promoted CeO 2 –ZrO 2 support. The purpose was to estimate the effect of hydrogen removal on conversion and reaction products yield. The characteristics and hydrogen permeation properties of the MR were also studied. The activation energy obtained for the reaction in the PBTR was 190 kJ/mol, whereas the MR exhibited activation energy of ca. 50% lower than the PBTR. MR characterization results showed that hydrogen permeation and recovery increased with temperature and trans-membrane pressure, which could be correlated using Sievert’s law to yield an activation energy of 22.7 kJ/mol.

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)0.002
metaresearch head score (Gemma)0.001
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 score0.002
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.002
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 head0.018
GPT teacher head0.253
Teacher spread0.234 · 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

Citations6
Published2014
Admission routes2
Has abstractyes

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