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Record W4395046529 · doi:10.1002/cjce.25275

Role of catalyst topology in methanol synthesis

2024· article· en· W4395046529 on OpenAlexvenueno aff
Kemal F. Hastadi, Milinkumar T. Shah, Tejas Bhatelia, Biao Sun, Vishnu Pareek

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2024
Typearticle
Languageen
FieldEngineering
TopicHeat and Mass Transfer in Porous Media
Canadian institutionsnot available
Fundersnot available
KeywordsCatalysisPressure dropMaterials scienceYield (engineering)Reynolds numberPorosityVolume (thermodynamics)Computational fluid dynamicsMethanolDiffusionDrop (telecommunication)MechanicsComposite materialChemical engineeringChemistryThermodynamicsMechanical engineeringEngineeringPhysicsOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract Methanol synthesis is carried out in a catalytic, packed bed reactor, where the shape and size of catalyst play a critical role in dictating overall reactor performance. In the current study, the effect of five different catalyst shapes including cylindrical, ring, trilobe, wagon wheel, and spherical on the reactor performance was investigated by conducting particle‐scale computational fluid dynamics (CFD) simulations. The predictions of pressure drop, velocity, temperature, reactant distribution and product yield were analyzed. When the performance of the simulated shapes was compared at the same tube Reynolds number of 50,000, internally contoured shapes (wagon wheel and ring) resulted in 40% higher pressure drops due to the tortuous flow path. However, the shape with internal void provided access for the reactant to reach the internal part of catalyst, resulting in higher yield produced per volume of catalyst. The wagon wheel shape produced 15% and 5% more yield per volume against the cylindrical shape and trilobe, respectively. The performance of the wagon wheel can be attributed to the lower diffusion limitation due to higher surface area available for reactant to penetrate the internal part of the catalyst.

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)0.000
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.285

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.000
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 head0.006
GPT teacher head0.186
Teacher spread0.180 · 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

Citations1
Published2024
Admission routes1
Has abstractyes

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