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Catalytic Steam Gasification of Athabasca Visbroken Residue by NiO–Kaolin-Based Catalysts in a Fixed-Bed Reactor

2017· article· en· W2623881175 on OpenAlexafffund
Azfar Hassan, Lante Carbognani-Arambarri, Nashaat N. Nassar, Gerardo Vitale, Mónica Bartolini, Pedro Pereira‐Almao

Bibliographic record

VenueEnergy & Fuels · 2017
Typearticle
Languageen
FieldEngineering
TopicThermochemical Biomass Conversion Processes
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of CanadaChina National Offshore Oil CorporationCarbon Management Canada
KeywordsCatalysisNon-blocking I/OResidue (chemistry)ChemistryAdsorptionChemical engineeringNuclear chemistrySteam reformingWaste managementMaterials scienceHydrogen productionOrganic chemistry

Abstract

fetched live from OpenAlex

In this study, H 2 production via low-temperature catalytic steam gasification (CSG) of Athabasca visbroken vacuum residue adsorbed onto NiO–kaolin-based catalysts in a fixed-bed reactor was investigated. The CSG was carried out at 600, 650, and 700 °C. Two NiO–kaolin-based catalysts were used, containing either K 2 O or Cs 2 O along with BaO. XRD data showed the presence of a crystalline Ni 3 S 2 phase in the spent catalyst only when K was used instead of Cs in the catalyst preparation. CSG experiments conducted for 100 h followed by regeneration confirmed that both catalysts are good candidates for H 2 production via CSG of adsorbed heavy hydrocarbon feed in a fixed-bed reactor unit. However, the H 2 /CO 2 ratio obtained during CSG is closer to 2 at all three temperatures studied for the 3NiO6K6Ba catalyst. Furthermore, the activation energies determined for the 3NiO6K6Ba and 3NiO6Cs6Ba catalysts for CSG of visbroken residue were 83 and 95 kJ/mol, respectively.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.009

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)0.0000.000

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 head0.009
GPT teacher head0.216
Teacher spread0.207 · 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 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

Citations4
Published2017
Admission routes2
Has abstractyes

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