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Record W2249330846 · doi:10.14447/jnmes.v16i3.25

Thermodynamic Analysis of the Absorption Enhanced Steam Reforming of Biofuel Model Compounds

2013· article· en· W2249330846 on OpenAlexvenueno aff
V. Collins-Martı́nez, Miguel A. Escobedo-Bretado, Jesús Salinas Gutiérrez, M. Meléndez Zaragoza, Alejandro López-Ortíz

Bibliographic record

VenueJournal of New Materials for Electrochemical Systems · 2013
Typearticle
Languageen
FieldChemical Engineering
TopicCatalysts for Methane Reforming
Canadian institutionsnot available
Fundersnot available
KeywordsStoichiometryFurfuralChemistryVanillinSteam reformingMethanolNuclear chemistryBiofuelCarbon fibersChemical engineeringHydrogenHydrogen productionOrganic chemistryMaterials scienceCatalysisWaste management

Abstract

fetched live from OpenAlex

Thermodynamic analysis of the steam reforming of biofuel model compounds using CaO, and Na2ZrO3, as CO2 absorbents was performed to determine favorable operating conditions to produce a high hydrogen ratio (HR, molsH2 produced/molsHC fed) and concentration (%H2) gas product. Biofuel compounds (HC’s) used were: 2,4-dimethylphenol (DMP), furfural (FUR) and vanillin (VAI). Equilibrium product compositions were studied at 300-850°C, steam to hydrocarbon molar ratio (S/HC) and CO2 absorbent at 1 atm. S/HC varied from stoichiometric; 15:1 (DMP), 13:1 (VAI) and 8:1 (FUR) to twice and trice their stoichiometric values, respectively. At stoichiometric S/HC ratios results indicate significant carbon formation with conventional reforming at T < 600°C, with no carbon formation using absorbents with any of the HC’s. The use of a CO2 absorbent resulted in an increase in HR and H2 purity of about 3 and 30% higher, respectively. The order from high to low HR was: VA>DMP>FUR.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

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)0.0020.000

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 head0.012
GPT teacher head0.242
Teacher spread0.230 · 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 source (direct Gemma or distilled Codex), 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".

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Citations1
Published2013
Admission routes1
Has abstractyes

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