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Record W2336768476 · doi:10.1002/cjce.22429

Kinetics of low‐temperature Fischer‐Tropsch synthesis on cobalt catalysts: Are both slurry autoclave and tubular packed‐bed reactors adequate to collect relevant data at lab‐scale?

2016· article· en· W2336768476 on OpenAlexvenueno aff
Carlo Giorgio Visconti, Luca Lietti, Enrico Tronconi, Stefano Rossini

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2016
Typearticle
Languageen
FieldChemical Engineering
TopicCatalysts for Methane Reforming
Canadian institutionsnot available
Fundersnot available
KeywordsFischer–Tropsch processAutoclavePacked bedSlurrySyngasPlug flowCobaltChemical engineeringPlug flow reactor modelProduct distributionCatalysisVolumetric flow rateMaterials scienceChemistrySpace velocityContinuous stirred-tank reactorWaste managementThermodynamicsChromatographyMetallurgyOrganic chemistryComposite materialEngineering

Abstract

fetched live from OpenAlex

Abstract To verify the equivalence of kinetic data obtained in lab‐scale packed‐bed and slurry reactors for low‐temperature Fischer‐Tropsch synthesis over cobalt‐based catalysts, the same Co/γ‐Al 2 O 3 catalyst was tested in both reactors, studying the effects of the process conditions (temperature, pressure, syngas composition, gas space velocity) on CO conversion rate and on product distribution. Then, both a lumped CO conversion rate equation and detailed kinetics (i.e. describing the rates of reactants conversion and product formation) were developed based on the data collected in the packed‐bed tubular reactor. The two models were finally used to simulate the performance of the slurry autoclave. By describing the slurry autoclave as a continuous stirred tank reactor and the packed‐bed as plug‐flow reactor, the kinetic information collected with the two reactors is fully equivalent and can be used indiscriminately to describe the rates of reactant consumption and product formation.

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)0.000
metaresearch head score (Gemma)0.003
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.011
GPT teacher head0.206
Teacher spread0.194 · 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

Citations13
Published2016
Admission routes1
Has abstractyes

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