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Thermodynamic Analysis of Autothermal Reforming of Synthetic Crude Glycerol (SCG) for Hydrogen Production

2017· article· en· W2738707393 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueChemEngineering · 2017
Typearticle
Languageen
FieldEngineering
TopicCatalysis for Biomass Conversion
Canadian institutionsUniversity of Regina
Fundersnot available
KeywordsHydrogen productionHydrogenGlycerolThermodynamicsChemistrySteam reformingIsothermal processGibbs free energyYield (engineering)MethaneMethanolMaterials scienceOrganic chemistryPhysics

Abstract

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This study presents the thermodynamic modelling of hydrogen production from autothermal reforming of synthetic crude glycerol using the Peng-Robinson Stryjek-Vera thermodynamic model and Gibbs free energy minimization approach. In order to simulate the typical crude glycerol, a solution was prepared by mixing glycerol, methanol, soap, and fatty acids. The equilibrium compositions of the reforming gas were obtained and the impacts of the operating temperature, steam to crude glycerol ratio (S/SCG), and oxygen to crude glycerol ratio (O/SCG) on hydrogen production were investigated. Under isothermal conditions, the result showed that maximum hydrogen production is favoured at conditions of high temperatures, high S/SCG, and low O/SCG ratio. However, under thermoneutral conditions where no external heat is supplied to the reformer, results indicate that high hydrogen yield is realised at conditions of high temperatures, high S/SCG and high O/SCG ratio. Furthermore, it was concluded that under thermoneutral condition, steam to SCG ratio of 3.6, oxygen to SCG ratio of 0.75, and adiabatic temperature of 927 K yields maximum hydrogen.

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.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.333
Threshold uncertainty score0.644

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 head0.008
GPT teacher head0.221
Teacher spread0.212 · 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