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Record W2979957852 · doi:10.1021/acs.iecr.9b04166

Performance Enhancement of Fluidized Bed Catalytic Reactors by Going to Finer Particles

2019· article· en· W2979957852 on OpenAlexaff
Yuqi Zhang, Yandaizi Zhou, Jiangshan Liu, Jesse Zhu

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2019
Typearticle
Languageen
FieldEngineering
TopicGranular flow and fluidized beds
Canadian institutionsWestern University
FundersChina Scholarship Council
KeywordsFluidizationCatalysisFluidized bedChemical engineeringFluid catalytic crackingDecompositionMaterials scienceParticle (ecology)Particle sizeCrackingPhase (matter)ChemistryComposite materialOrganic chemistry

Abstract

fetched live from OpenAlex

Fluidization is an important operation for multiphase reactions, especially for gas-phase catalytic reactions because it provides a large solid surface area for improved gas–solid contact efficiency. Although fine particles with small particle size (<45 μm) could be more desirable in providing larger interfacial area, their strong interparticle forces lead to poor or even no fluidization. In this project, a “nanomodification” technique has been adopted to reduce the interparticle forces so as to release the potential of fine particles in multiphase reactions. As a first attempt, “nanomodified” fine catalysts (32 μm) were used in the ozone decomposition reaction and displayed better reaction performance than regular catalysts due to larger interfacial area and more gas holdup in the bed, indicating better gas–solid contact. The reaction conversion and contact efficiency using the nanomodified fine fluid cracking catalytic (FCC) catalysts significantly increased compared to that of using regular FCC catalysts (100 μm).

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)0.000
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 score0.018
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.035
GPT teacher head0.265
Teacher spread0.231 · 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

Citations9
Published2019
Admission routes1
Has abstractyes

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