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Record W2555669281 · doi:10.1002/aic.15582

Simulation of dry reforming of methane in a conventional downfired reformer

2016· article· en· W2555669281 on OpenAlexafffund
Yutian R. Zhao, Dean A. Latham, Brant A. Peppley, Kimberley B. McAuley, Hui Wang, Rick Lehoux

Bibliographic record

VenueAIChE Journal · 2016
Typearticle
Languageen
FieldChemical Engineering
TopicCatalysts for Methane Reforming
Canadian institutionsUniversity of SaskatchewanGreenField Specialty Alcolhols (Canada)Process Research Ortech (Canada)Queen's University
FundersNatural Sciences and Engineering Research Council of CanadaQueen's University
KeywordsMethaneSteam reformingCarbon dioxide reformingProcess engineeringMethane reformerSyngasSolverDry gasProcess (computing)Gas compositionNuclear engineeringThermodynamicsWaste managementEnvironmental scienceMechanicsChemistryEngineeringHydrogen productionCatalysisComputer sciencePhysics

Abstract

fetched live from OpenAlex

A model for industrial top‐fired dry reforming of methane (DRM) and for combined dry reforming and steam reforming of methane was developed for the first time. The model calculates and gives predictions on the temperature profiles for fuel gas, tube walls, and process gas, as well as the process gas composition profiles over the length of the tubes. Radiative heat transfer is modeled by Hottel Zone method. Material and energy balances are solved numerically using Newton‐Raphson solver. Kinetic models for two different DRM catalysts are applied in the model for comparison. Simulation results show that water–gas shift reaction is important in DRM and addition of steam in the feed of process gas is beneficial for industrial production. © 2016 American Institute of Chemical Engineers AIChE J , 63: 2060–2071, 2017

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)0.001
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 score0.020
Threshold uncertainty score0.277

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.019
GPT teacher head0.279
Teacher spread0.260 · 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

Citations3
Published2016
Admission routes2
Has abstractyes

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