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Record W2972706760 · doi:10.1002/cjce.23644

CFD <b>s</b>tudy on partial oxidation of methane in fixed‐bed reactor

2019· article· en· W2972706760 on OpenAlexvenueno aff
Yonghui Li, Chao Lü, Xingxing Cao, Zhongfeng Geng, Minhua Zhang

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2019
Typearticle
Languageen
FieldEngineering
TopicHeat and Mass Transfer in Porous Media
Canadian institutionsnot available
Fundersnot available
KeywordsCatalysisMethaneCokeSpace velocitySelectivityPartial oxidationChemistryMass transferReaction rateHeat transferSyngasChemical engineeringThermodynamicsMaterials scienceChromatographyOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract Partial oxidation of methane (POM) is a preferred method for synthesis gas, which usually occurs in fixed bed reactors. In this paper, the discrete element method (DEM) is used to reconstruct the structure of a reactor bed via simulating the process of filling the reactor with catalyst. The particle resolved CFD physical model with the detailed micro‐kinetcis of the POM reaction was established to study the interaction among reactant flow, heat and mass transfer, and reaction in the fixed bed. The gas composition and temperature distribution in the reactor were obtained based on the simulation results. The effects of the space velocity and the reaction temperature on the CH 4 conversion, catalyst selectivity, and catalyst surface coke formation were analyzed. The simulation results show that the temperature hot spots of the catalyst in the bed occur at the inlet and the temperature increases further near the wall. With the increase in space velocity, the conversion rate of CH 4 decreases gradually, and the selectivity does not change significantly. As the temperature increases, the conversion rate of CH 4 gradually increases and the selectivity decreases. The risk of coke formation on the catalyst surface rises axially and the C species concentration is relatively higher near the outlet. Appropriately increasing the gas velocity and increasing the temperature helps to reduce the surface coke accumulation of the catalyst.

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)0.000
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 score0.027
Threshold uncertainty score0.362

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.009
GPT teacher head0.190
Teacher spread0.181 · 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

Citations4
Published2019
Admission routes1
Has abstractyes

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