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Record W4414552006 · doi:10.1002/cjce.70092

Modelling and simulation of hydrogen production via natural gas plasma pyrolysis

2025· article· en· W4414552006 on OpenAlexafffundvenue
Muhammad B.I. Chowdhury, Anton Alvarez‐Majmutov, Jinwen Chen

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2025
Typearticle
Languageen
FieldChemical Engineering
TopicAdvanced Combustion Engine Technologies
Canadian institutionsDevon Energy (Canada)Natural Resources Canada
FundersNatural Resources CanadaOffice of Energy Research and DevelopmentGovernment of Canada
KeywordsPyrolysisMethaneNatural gasHydrogenCarbon fibersProcess (computing)SootProcess simulation

Abstract

fetched live from OpenAlex

Abstract Plasma pyrolysis is a cutting‐edge technology that produces hydrogen and solid carbon from methane without emitting carbon dioxide. In this study, we developed a process simulation model in Aspen HYSYS to analyze the behaviour of this technology at the commercial scale. Methane pyrolysis reactions were modelled using a non‐isothermal equilibrium approach, coupled with a kinetic model from the literature. The model was validated using experimental data from laboratory‐scale studies and then extended to represent the commercial‐scale process using natural gas as feedstock. The effects of temperature, pressure, and plasma gas volume on process performance were examined. While analysis of simulation results suggested that the process temperature required to maximize hydrogen yield with minimum by‐products like soot should be close to 3000°C, the commercially applicable temperature is known to be lower, around 2000°C. The estimated specific energy requirement (SER) of the process under commercially relevant conditions (20.12 kWh/kg H 2 ) was found to be in good agreement with that reported by the only known industrial facility (21.54 kWh/kg H 2 ).

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.189
Threshold uncertainty score0.422

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 head0.007
GPT teacher head0.199
Teacher spread0.192 · 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

Citations1
Published2025
Admission routes3
Has abstractyes

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