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Record W2329612136 · doi:10.1002/aic.15252

Supercritical water gasification of aqueous fraction of pyrolysis oil in the presence of a Ni‐Ru catalyst

2016· article· en· W2329612136 on OpenAlexafffund
Izad Behnia, Zhongshun Yuan, Paul A. Charpentier, Chunbao Xu

Bibliographic record

VenueAIChE Journal · 2016
Typearticle
Languageen
FieldEngineering
TopicSubcritical and Supercritical Water Processes
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPyrolysisCatalysisAqueous solutionSupercritical fluidFraction (chemistry)ChemistryHydrogenCarbon fibersChemical engineeringPyrolysis oilRaw materialNuclear chemistryInorganic chemistryMaterials scienceOrganic chemistryComposite material

Abstract

fetched live from OpenAlex

This study demonstrated that aqueous fraction of pyrolysis oil can be efficiently gasified into fuel gases methane and hydrogen via supercritical water gasification (SCWG) at moderate temperatures (500–700°C) over Ni 20% Ru 2% /γ‐Al 2 O 3 catalyst. All experiments were performed in a bench‐scale continuous down‐flow tubular reactor packed with the catalyst. Carbon gasification efficiency of 0.91 mol/mol‐C (converted into CH 4 and CO 2 ) was achieved in SCWG of the aqueous fraction of pyrolysis oil (containing 2.98 wt % C) at 700°C in the presence of the catalyst. A similar carbon gasification efficiency (approx. 0.89 mol/mol‐C) was obtained at a lower temperature (600°C) with a diluted feedstock (0.7 wt %C). Scanning Electron Microscopy coupled with Energy Dispersive x‐ray and inductively coupled plasma analysis results confirmed that this catalyst was stable during SCWG of aqueous fraction of pyrolysis oil after 6 h on‐stream. © 2016 American Institute of Chemical Engineers AIChE J , 62: 2786–2793, 2016

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 score0.008
Threshold uncertainty score0.166

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 head0.012
GPT teacher head0.226
Teacher spread0.214 · 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

Citations20
Published2016
Admission routes2
Has abstractyes

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