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Record W2947260425 · doi:10.1002/cjce.23518

Importance of proper hydrodynamics modelling in fixed‐bed reactors: Fischer‐Tropsch synthesis study case

2019· article· en· W2947260425 on OpenAlexvenueno aff
César I. Méndez, Jorge Ancheyta, Fernando Trejo

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2019
Typearticle
Languageen
FieldChemical Engineering
TopicCatalysts for Methane Reforming
Canadian institutionsnot available
FundersInstituto Mexicano del PetróleoConsejo Nacional de Ciencia y Tecnología
KeywordsFischer–Tropsch processIsothermal processSyngasContinuous stirred-tank reactorNuclear engineeringMaterials scienceMechanicsThermodynamicsCatalysisProcess engineeringChemistrySelectivityChemical engineeringEngineeringPhysicsOrganic chemistry

Abstract

fetched live from OpenAlex

The importance of hydrodynamics, particularly gas density, superficial gas velocity, and total pressure in axial and radial directions, was analyzed for the modelling of a catalytic reactor using a non‐isothermal pseudo‐homogeneous approach. The modelling of a fixed‐bed reactor in one and two stages for CO conversion by Fischer‐Tropsch synthesis was taken as a study case. For the validation of the proposed model, the results of the simulations for the CO conversion and temperature profiles were compared with experimental data reported in the literature. Simulations for CO conversion and reactor temperature profiles confirmed the model's ability to predict the selectivity of the liquid products in the Fischer‐Tropsch synthesis reactor in one and two stages. The proposed model predicts more suitable profiles of CO conversion and temperature along the reactor, which makes it a more robust and efficient tool for design, optimization, and control purposes.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.086
Threshold uncertainty score0.763

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 science0.0000.000
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 head0.011
GPT teacher head0.201
Teacher spread0.189 · 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 designSimulation or modeling
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

Citations7
Published2019
Admission routes1
Has abstractyes

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