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Record W2515169978 · doi:10.1002/cjce.22627

Comprehensive CFD simulation of the optimizations of geometric structures and operating parameters for industrial acetylene hydrogenation reactors

2016· article· en· W2515169978 on OpenAlexvenueno aff
Liang Tian, Guihua Hu, Wenli Du, Feng Qian

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2016
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer and Boiling Studies
Canadian institutionsnot available
FundersNational Natural Science Foundation of China
KeywordsAdiabatic processComputational fluid dynamicsIsothermal processLaminar flowAcetyleneMaterials scienceTurbulenceMechanicsThermodynamicsNuclear engineeringChemistryPhysicsEngineeringOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract In this study, for the first time, comprehensive simulations of the industrial adiabatic and isothermal reactors utilized for the hydrogenation of acetylene were performed by computational fluid dynamics (CFD). The standard k–ϵ and Spalart‐Allmaras models were employed for describing the turbulence characteristics of the adiabatic and isothermal reactors, respectively. The porous medium model was applied to the flow of catalyst particles. The laminar finite‐rate model was employed for simulating the reaction in the reactor. The results obtained from simulations were in close agreement with those obtained from the industrial adiabatic reactor. Based on the validated CFD models, three operating conditions of the adiabatic reactor are simulated for comparing the selectivity of acetylene; as a result, H and T segmentation is utilized as an optimum process parameter. The different geometric structures of the adiabatic and isothermal reactors are simulated for obtaining the best diameter to length (D/L) ratio. The simulation results indicated an optimal D/L of 0.3852 for the adiabatic reactor, and the diameter‐to‐length ratio exerts a marginal effect on the reaction selectivity of isothermal reactors.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.731
Threshold uncertainty score0.161

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 head0.028
GPT teacher head0.216
Teacher spread0.188 · 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 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

Citations7
Published2016
Admission routes1
Has abstractyes

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