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Record W2021850393 · doi:10.1021/ie0511736

Modeling of Sorption-Enhanced Steam Reforming in a Dual Fluidized Bubbling Bed Reactor

2006· article· en· W2021850393 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2006
Typearticle
Languageen
FieldEngineering
TopicChemical Looping and Thermochemical Processes
Canadian institutionsUniversity of British Columbia
FundersNorges Forskningsråd
KeywordsCarbonationSteam reformingFluidized bedSorbentMethaneSorptionHydrogenHydrogen productionChemistryChemical engineeringDolomiteMethane reformerWaste managementMaterials scienceMineralogyOrganic chemistryAdsorption

Abstract

fetched live from OpenAlex

This paper highlights the use of a dual fluidized bed reactor system for producing hydrogen by sorption-enhanced steam methane reforming. Hydrogen concentrations of >98% are predicted for temperatures of ∼600 °C and a superficial gas velocity of 0.1 m/s, using a simple two-phase bubbling bed model for the reformer. The kinetics of the steam methane reforming and water-gas shift reactions are based on literature values, whereas experimentally derived carbonation kinetics are used for the carbonation of a dolomite. It is shown that the reformer temperature should not be <540 °C or >630 °C for carbon capture efficiencies to exceed 90%. Operating at relatively high solids circulation rates to reduce the need for fresh sorbent is predicted to give higher system efficiencies than for the case where fresh solid is added. This finding is attributed to the additional energy required to decompose both CaCO 3 and MgCO 3 in fresh dolomite.

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.

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)0.001
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 score0.005
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.053
GPT teacher head0.287
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