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Record W2163863799 · doi:10.1002/ceat.201300163

Catalytic Ozone Decomposition in a Gas‐Solids Circulating Fluidized‐Bed Riser

2014· article· en· W2163863799 on OpenAlexaff
Lei Kong, Jesse Zhu, Chao Zhang

Bibliographic record

VenueChemical Engineering & Technology · 2014
Typearticle
Languageen
FieldEngineering
TopicGranular flow and fluidized beds
Canadian institutionsWestern University
Fundersnot available
KeywordsFluid catalytic crackingComputational fluid dynamicsCatalysisFluidized bed combustionReaction rateFluidized bedDecompositionParticle (ecology)ThermodynamicsChemistryMechanicsMaterials scienceChemical engineeringEngineeringPhysicsOrganic chemistryGeology

Abstract

fetched live from OpenAlex

Abstract Computational fluid dynamics (CFD) modeling of the catalytic ozone decomposition reaction in a circulating fluidized‐bed (CFB) riser, using iron‐impregnated fluid catalytic cracking particles as catalyst, is carried out. The catalytic reaction is defined as a one‐step reaction, and the reaction equation is modified by with respect to the particle surface area, Ap, and an empirical coefficient. The Eularian‐Eularian method with the kinetic theory of granular flow is used to solve the gas‐solids two‐phase flow in the CFB riser. The simulation results are compared with experimental data, and the reaction rate is modified by using an empirical coefficient, to provide better simulation results than the original reaction rate. Moreover, the particle size has great effects on the reaction rate. The generality of the CFD model is further validated under different operating conditions of the riser.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.003
GPT teacher head0.190
Teacher spread0.187 · 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 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

Citations10
Published2014
Admission routes1
Has abstractyes

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