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Record W2110685603 · doi:10.1002/cjce.22187

Numerical evaluation and improvement efficiency of radial flow moving‐bed reactors for catalytic pyrolysis of light hydrocarbons to low carbon olefins

2015· article· en· W2110685603 on OpenAlexvenueno aff
Fang‐Zhi Xiao, Houyang Chen, Zheng‐Hong Luo

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2015
Typearticle
Languageen
FieldEngineering
TopicThermochemical Biomass Conversion Processes
Canadian institutionsnot available
Fundersnot available
KeywordsMicroreactorPyrolysisHeat transferYield (engineering)Materials scienceMechanicsPlug flow reactor modelFlow (mathematics)Volumetric flow rateCarbon fibersLaminar flow reactorContinuous reactorCatalysisChemical engineeringThermodynamicsChemistryContinuous stirred-tank reactorComposite materialOrganic chemistry

Abstract

fetched live from OpenAlex

A three‐dimensional (3D) reactor model based on the Eulerian‐Eulerian approach was applied to describe the gas‐solid flow and heat transfer performance in a radial flow moving bed reactor (RFMBR). A six‐lumped kinetic model for the catalytic pyrolysis of C 4 hydrocarbon was employed. The heat transfer characteristics and species concentration profiles were investigated in the reactor under various reaction conditions. Effects of operation parameters and reactor structures on the reactor performance were also evaluated and optimized numerically. Simulation results show that there exists a good heat transfer performance between gas and solid phases in the catalyst bed. The temperature profiles and the species yield distributions are different with respect to bed height positions. Moreover, the results indicate that product yield is more sensitive to the reaction temperature than to the dilution rate and the reaction residence time. For the Z‐type centripetal flow RFMBR, an annular tube with an inverted cone structure is helpful to improve the uniformity of flow distribution and increase low‐carbon olefins yields.

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)0.001
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 score0.076
Threshold uncertainty score0.502

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.008
GPT teacher head0.194
Teacher spread0.186 · 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

Citations5
Published2015
Admission routes1
Has abstractyes

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