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Record W3120487789 · doi:10.3390/catal11010089

A Review of the Critical Aspects in the Multi-Scale Modelling of Structured Catalytic Reactors

2021· review· en· W3120487789 on OpenAlexafffund
Iván Cornejo, Robert E. Hayes

Bibliographic record

VenueCatalysts · 2021
Typereview
Languageen
FieldEngineering
TopicCatalysis and Hydrodesulfurization Studies
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaAgencia Nacional de Investigación y Desarrollo
KeywordsComputer scienceScale (ratio)GranularityChemical reaction engineeringHeat transferMass transferPressure dropProcess engineeringEngineeringCatalysisChemistryMechanicsPhysics

Abstract

fetched live from OpenAlex

Structured catalytic reactors are enjoying an increasingly important role in the reaction engineering world. At the same time, there are large and growing efforts to use advanced computational models to describe such reactors. The structured reactor represents a multi-scale problem that is typically modelled at the largest scale only, with sub-models being used to improve the model granularity. Rather than a literature review, this paper provides an overview of the key factors that must be considered when choosing these sub-models (or scale bridges). The example structured reactor selected for illustration purposes is the washcoated honeycomb monolith design. The sub-models reviewed include those for pressure drop, inter- and intra-phase mass and heat transfer, and effective thermal conductivity.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.005
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.003

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 head0.073
GPT teacher head0.321
Teacher spread0.248 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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
Published2021
Admission routes2
Has abstractyes

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