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Record W2028200415 · doi:10.1002/cjce.20621

Thermodynamic analysis of hydrogen production from glycerol at energy self‐sufficient conditions

2011· article· en· W2028200415 on OpenAlexaffvenueabout
Thirasak Pairojpiriyakul, Worapon Kiatkittipong, Apinan Soottitantawat, Amornchai Arpornwichanop, Navadol Laosiripojana, Wisitsree Wiyaratn, Eric Croiset, Suttichai Assabumrungrat

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2011
Typearticle
Languageen
FieldEngineering
TopicCatalysis for Biomass Conversion
Canadian institutionsUniversity of Waterloo
FundersCommission on Higher EducationRoyal Golden Jubilee (RGJ) Ph.D. ProgrammeThailand Research FundHigher Education Research Promotion
KeywordsGlycerolGibbs free energyHydrogenHydrogen productionThermodynamicsChemistryEnergy (signal processing)Hydrogen fuelMaterials scienceChemical engineeringPhysicsOrganic chemistryEngineering

Abstract

fetched live from OpenAlex

Abstract A thermodynamic analysis based on the principle of minimising the Gibbs free energy is performed for hydrogen production from glycerol. When the operating parameters such as water/glycerol ratio ( WGR ), oxygen/glycerol ratio ( OGR ) and operating temperature ( T ) are carefully chosen, the energy self‐sufficient conditions can be achieved. Two levels of energy self‐sufficient, (i) within the reformer and (ii) within the overall system, are considered. Unlike the consideration in the reformer level reported in most works in literature, the consideration in the overall system level represents more realistic results based on the fact that some energy is required for heating up the feeds to a desired operating temperature. The obtained results demonstrate that the maximum hydrogen production significantly decreases from 5.65 mol H 2 /mol glycerol for the reformer level to 3.31 mol H 2 /mol glycerol for the system level, emphasising the significant demand of energy for feed preheating. © 2011 Canadian Society for Chemical Engineering

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.150
Threshold uncertainty score0.429

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.006
GPT teacher head0.154
Teacher spread0.148 · 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

Citations5
Published2011
Admission routes3
Has abstractyes

Explore more

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