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Record W2321313481 · doi:10.1627/jpi1958.43.357

Effect of Addition of K to Ni/SiO2 and Ni/Al2O3 Catalysts on Hydrothermal Cracking of Bitumen.

2000· article· en· W2321313481 on OpenAlexaboutno aff
Jie Chang, Noritatsu Tsubaki, Kaoru Fujimoto

Bibliographic record

VenueSekiyu Gakkaishi · 2000
Typearticle
Languageen
FieldChemistry
TopicPetroleum Processing and Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsCokeCatalysisHydrothermal circulationCrackingChemical engineeringFluid catalytic crackingNickelChemistryHydrocarbonYield (engineering)AutoclaveNuclear chemistryPyrolysisMaterials scienceMetallurgyInorganic chemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Hydrothermal cracking of Canadian Athabasca bitumen was performed over Ni/SiO2 and Ni/Al2O3 catalysts under 703K and 5.0MPa of hydrogen pressure in a batch reactor. Comparing with thermal cracking under the same reaction conditions, hydrothermal cracking process obviously suppressed the formation of coke from 5.5 to 3.5wt% (Ni/SiO2) and to 3.0wt% (Ni/Al2O3), and the formation of gaseous hydrocarbon products. To decrease coke formation further, a small amount of potassium was impregnated in the catalysts. The spectroscopy of NH3-TPD showed that the amount of acidic sites in both catalysts, Ni/Al2O3 and Ni/SiO2, was dramatically decreased by K2O modification. The acid-catalyzed polymerization of residuum induced by acidic sites in the catalyst, that might result in the formation of coke, was suppressed by neutralization of the acidic sites. Adding 3% of potassium onto Ni/SiO2 decreased the yield of coke from 3.5 to 2.1wt%.

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)0.001
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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.004
GPT teacher head0.234
Teacher spread0.229 · 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
Published2000
Admission routes1
Has abstractyes

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