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Record W2321743156 · doi:10.1021/ie101360x

CO<sub>2</sub> Capture and Hydrogen Production in an Integrated Fluidized Bed Reformer-Regenerator System

2011· article· en· W2321743156 on OpenAlexaff
Zhongxiang Chen, John R. Grace, C. Jim Lim

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2011
Typearticle
Languageen
FieldEngineering
TopicChemical Looping and Thermochemical Processes
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsSorbentRegenerative heat exchangerHydrogen productionSteam reformingHydrogenMaterials scienceMethaneFluidized bedChemical engineeringWaste managementChemistryThermodynamicsAdsorptionHeat exchangerEngineeringOrganic chemistry

Abstract

fetched live from OpenAlex

Thermodynamic analysis of CO 2 capture and hydrogen production for steam methane reforming was carried out using ASPEN simulation software. The integrated reaction system is composed of a sorbent-enhanced fluidized bed reformer coupled with a fluidized bed sorbent regenerator (calciner) where fine CaO-based sorbents (∼100 μm mean particle diameter) were used. The system performance is evaluated as a function of a number of operating parameters for both the reformer and regenerator. The results indicate that the optimum operating parameters for reformer are temperatures from 550 to 600 °C, low pressure, steam-to-carbon molar feed ratio of 3.5, and sorbent circulation flow rate exceeding the minimum stoichiometric feed rate of active sorbent. For the sorbent regenerator, the optimum conditions are temperatures above 850 °C, low pressure, and enough sweep gas flow to completely calcine CaCO 3 . On the basis of thermodynamics, it should be possible to achieve a hydrogen purity of ∼98% and a CO 2 purity in excess of 99% after condensing sweep steam downstream. The predicted hydrogen purity is consistent with previous experiments. The high-concentration CO 2 should be suitable for sequestration or for industrial use.

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)0.001
metaresearch head score (Gemma)0.000
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 score0.003
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0010.001
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 head0.058
GPT teacher head0.266
Teacher spread0.208 · 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

Citations21
Published2011
Admission routes1
Has abstractyes

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