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Record W4301399264 · doi:10.1002/cjce.24696

Comparative analysis of ethanol‐steam and ‐autothermal reforming for hydrogen production using Aspen Plus

2022· article· en· W4301399264 on OpenAlexafffundvenue
Pali Rosha, Hussameldin Ibrahim

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2022
Typearticle
Languageen
FieldChemical Engineering
TopicCatalysts for Methane Reforming
Canadian institutionsUniversity of Regina
FundersNatural Sciences and Engineering Research Council of CanadaMitacsCanada Foundation for InnovationUniversity of Regina
KeywordsSteam reformingMethane reformerHydrogen productionMole fractionHydrogenEthanolChemistryChemical engineeringMaterials scienceWaste managementNuclear chemistryOrganic chemistryEngineeringPhysical chemistry

Abstract

fetched live from OpenAlex

Abstract A comparative analysis of the ethanol reforming concerning steam and autothermal reformer was conducted to the evaluate parametric conditions for a hydrogen‐rich product stream. The present simulation study includes a first attempt to report the optimal parametric conditions for ethanol‐steam and ‐autothermal reformer. Various operating parameters, including temperature, pressure, steam‐to‐ethanol ratio, and oxygen‐to‐ethanol ratio, were considered in this analysis. The result illustrated that the hydrogen mole fraction increased with rising temperature in the steam reforming of ethanol, but it remained constant beyond reaction temperature of 750°C. On the other hand, as the pressure and the steam‐to‐ethanol ratio increased, the H 2 mole fraction decreased. Furthermore, with an enriched oxygen‐to‐ethanol ratio reactant stream, H 2 and CO contents in the product effluent were found to be reduced in the autothermal ethanol reforming. The results showed that an autothermal reforming strategy under optimized parameters (temperature of 600°C, steam‐to‐ethanol ratio of 5, and oxygen‐to‐ethanol ratio of 0.6) produced the maximum H 2 yield (3.78 kmol/h) per mole of ethanol. It was observed that introducing O 2 into the reformer helped reduce the amount of energy required for the steam reforming reaction. This study indicates that, although considerable work has been conducted on reforming catalysts development, simulation‐based studies are still useful to help understand the overall process behaviour without undertaking laborious, high‐cost involved time‐consuming experiments.

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 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 score0.096
Threshold uncertainty score0.537

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.025
GPT teacher head0.249
Teacher spread0.225 · 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

Citations10
Published2022
Admission routes3
Has abstractyes

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