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Record W2615689074 · doi:10.1021/acs.iecr.7b00989

Flow Patterns of Feed Spray in Different Fluid Catalytic Cracking Feed Injection Schemes

2017· article· en· W2615689074 on OpenAlexaff
Zihan Yan, Yiping Fan, Xiaotao Bi, Chunxi Lu, Jing Bian

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2017
Typearticle
Languageen
FieldEngineering
TopicGranular flow and fluidized beds
Canadian institutionsUniversity of British Columbia
FundersChina Scholarship CouncilMinistry of Science and Technology of the People's Republic of China
KeywordsResidence time distributionPéclet numberMechanicsFlow (mathematics)NozzleMixing (physics)Plug flowDispersion (optics)Residence time (fluid dynamics)Fluid catalytic crackingMaterials scienceEnvironmental scienceCrackingGeologyThermodynamicsPhysicsGeotechnical engineeringComposite materialOptics

Abstract

fetched live from OpenAlex

The helium tracer method is used to investigate the residence time distribution and flow patterns of feed spray in different fluid catalytic cracking feed injection schemes by cold model experiments. The axial Peclet number in the upward and downward oriented feed injection schemes was obtained by fitting the residence time distribution into the one-dimensional axial dispersion model with an open–open boundary condition. Results suggest that the flow pattern of the mixed stream is closer to complete mixing in the initial contact region of spray with catalysts when the nozzles are mounted downward. A flow pattern variation index β is proposed to show the flow patterns in the feed injection schemes quantitatively. It is shown that a larger β is obtained when the feed nozzles are faced downward, meaning that the flow pattern of mixed stream in the feeding zone can develop more quickly from a likely complete mixed flow into a likely plug flow.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.058
GPT teacher head0.300
Teacher spread0.242 · 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 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

Citations17
Published2017
Admission routes1
Has abstractyes

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