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Record W4392303547 · doi:10.1016/j.cej.2024.150053

Thermally coupled catalytic hydrogen combustion-reverse water gas shift reactor: Model-based feasibility study and parametric analysis

2024· article· en· W4392303547 on OpenAlexaff
Guanjie Sun, David S. A. Simakov

Bibliographic record

VenueChemical Engineering Journal · 2024
Typearticle
Languageen
FieldChemical Engineering
TopicCatalysts for Methane Reforming
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsWater-gas shift reactionCombustionHydrogenTransient (computer programming)ThermalChemistryNuclear engineeringCatalysisParametric statisticsThermodynamicsMaterials scienceMechanicsPhysical chemistryPhysicsOrganic chemistry

Abstract

fetched live from OpenAlex

Thermally coupled catalytic hydrogen combustion-reverse water gas shift reactor for CO2 conversion to synthesis gas was designed and numerically analyzed using a 2D model with a shell-and-tube configuration. A transient, pseudo-homogeneous mathematical model was formulated accounting for axial and radial heat dispersion and using experimentally obtained reaction kinetic parameters. Transient reactor behavior was studied to analyze the dynamic behaviour. Steady-state reactor performance was analyzed in terms of temperature and reactant/product distribution, as well as output parameters of practical importance, namely maximum and outlet reactor temperatures, and outlet conversions. Numerical simulations demonstrate the feasibility of the suggested reactor concept and providing insights into thermal management, including the formation of hot spots, appearance of temperature fronts, and the importance of thermal insulation. The model predicted the possibility of 100 % catalytic H2 combustion conversion and 80 % CO2 conversion, with the reactor temperature of ca. 900 °C.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.348
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.016
GPT teacher head0.247
Teacher spread0.231 · 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.

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

Citations8
Published2024
Admission routes1
Has abstractyes

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