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Record W2328183251 · doi:10.1021/ie5034705

Catalytic Steam Reforming of Aqueous Phase of Bio-Oil over Ni-Based Alumina-Supported Catalysts

2014· article· en· W2328183251 on OpenAlexafffund
Fakhry Seyedeyn‐Azad, Jalal Abedi, Saeed Sampouri

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2014
Typearticle
Languageen
FieldEngineering
TopicCatalysis and Hydrodesulfurization Studies
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of CanadaAgriculture Funding ConsortiumUniversity of Calgary
KeywordsCatalysisYield (engineering)Aqueous solutionNickelAqueous two-phase systemHydrogenHydrogen productionChemical engineeringChemistryMaterials scienceNuclear chemistryOrganic chemistryMetallurgy

Abstract

fetched live from OpenAlex

Production of hydrogen (H 2 ) from catalytic steam reforming of the aqueous phase of bio-oil was investigated in a fixed bed tubular flow reactor over nickel-based alumina-supported catalysts promoted with magnesia (Ni-MgO/Al 2 O 3 ). The effects of time, amount of Ni, preparation condition, and initial bio-oil to water ratio on the yield of various outlet gases including hydrogen was investigated at 850 °C, and the outlet gas distributions were obtained. The average H 2 yield was very low with a maximum of 30% over the alumina support when the aqueous phase of the bio-oil at a bio-oil to water ratio of 1 was employed. The hydrogen yield nearly doubled with the addition of 12.8% nickel and 33.3% magnesia for the three bio-oil aqueous phase samples at various bio-oil to water ratios. This effect was more pronounced in the aqueous bio-oil phases with greater water content. On the contrary, the effect of the preparation method on H 2 yield was more pronounced in the aqueous phase samples with lower water content. Among the catalysts tested, the highest H 2 yield (61%) was achieved over Ni-MgO/Al 2 O 3 -3 with the aqueous phase of bio-oil with a bio-oil to water ratio of 1/1, indicating that a greater bio-oil to water ratio does not necessarily provide greater H 2 yield.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.032
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 integrity0.0000.001
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 head0.059
GPT teacher head0.323
Teacher spread0.264 · 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.

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

Citations23
Published2014
Admission routes2
Has abstractyes

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