MétaCan
Menu
Back to cohort
Record W2128036416 · doi:10.1002/aic.11242

Multicomponent multicompartment model for Fischer–Tropsch SCBR

2007· article· en· W2128036416 on OpenAlexaff
Ion Iliuta, Faı̈çal Larachi, Jérôme Anfray, Nicolas Dromard, Daniel Schweich

Bibliographic record

VenueAIChE Journal · 2007
Typearticle
Languageen
FieldChemical Engineering
TopicCatalysts for Methane Reforming
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsFischer–Tropsch processSyngasChemistryWater gasThermodynamicsMass transferGas to liquidsWater-gas shift reactionOxygenateBubble column reactorBubbleChemical engineeringCatalysisMechanicsOrganic chemistryChromatographyPhysicsEngineering

Abstract

fetched live from OpenAlex

Abstract The Fischer–Tropsch synthesis (FTS) in which a syngas is converted into a wide range of paraffins, olefins, and oxygenates, has found renewed interest in the context of indirect conversion of natural gas. Slurry bubble column reactors (SBCR) rank high among the candidate reactors for FTS. Despite their simple construction, their design are still uncertain because of the fragmented understanding about FTS chemistry, the reactor fluid mechanics, heat and mass transfer, the thermodynamics, and how these phenomena are intermingled in the reactor. A multicomponent/compartment model was developed to account for a detailed hydrodynamics where upon were tied the Fischer–Tropsch and water‐gas‐shift reactions, the thermodynamics and thermal effects, the variable gas flow‐rate because of chemical/physical contraction, and gas and slurry (re)circulation and percompartment back‐mixing. A Cobased mechanistic kinetics accounting for olefin re‐adsorption was used to describe paraffin and olefin formation and vapor–liquid equilibria were evaluated using a Peng‐Robinson/Marano‐Holder model. The model was used to analyze the effects of catalyst loading, temperature, gas velocity, water‐gas shift, and gas contraction on the performance of SBCRs. The simulated behavior for commercial‐scale SBCR was discussed in the light of a sensitivity analysis of the model outputs with regard to the hydrodynamic, the heat and mass transfer parameters. © 2007 American Institute of Chemical Engineers AIChE J, 2007

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.688
Threshold uncertainty score0.748

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.037
GPT teacher head0.307
Teacher spread0.270 · 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

Citations20
Published2007
Admission routes1
Has abstractyes

Explore more

Same venueAIChE JournalSame topicCatalysts for Methane ReformingFrench-language works237,207