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Record W2793699644 · doi:10.1002/cjce.23189

Modelling diffusion and reaction for inert‐core catalyst in batch and fixed bed reactors

2018· article· en· W2793699644 on OpenAlexvenueno aff
Ping Li, Guohua Xiu, Alı́rio E. Rodrigues

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2018
Typearticle
Languageen
FieldEngineering
TopicCatalysis and Hydrodesulfurization Studies
Canadian institutionsnot available
FundersNational Natural Science Foundation of China
KeywordsInertThiele modulusCatalysisMass transferIsothermal processDiffusionChemistryCore (optical fiber)Chemical engineeringInert gasChemical reaction engineeringMaterials scienceThermodynamicsChromatographyOrganic chemistryComposite material

Abstract

fetched live from OpenAlex

New analytical solutions of concentration time curves are derived for an isothermal inert-core spherical catalyst based on the mathematical models by taking into account first-order irreversible reaction and mass transfer resistances in batch and fixed bed reactors.1, 2 The effects of mass transfer resistances and Thiele modulus on catalytic efficiency and conversion of reactant are examined over a wide range of parameters for an inert-core catalyst. The results show that the inert-core catalyst can significantly improve the efficiency for fast catalytic reactions, where mass transfer limitations occur; however, the reactant conversion will decrease due to a lower loading ratio of active species of catalyst for inert-core 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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.860
Threshold uncertainty score0.283

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 head0.015
GPT teacher head0.196
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 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

Citations19
Published2018
Admission routes1
Has abstractyes

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