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Record W2316664410 · doi:10.1021/ef200427a

Cracking Performance of Gasoline and Diesel Fractions from Catalytic Pyrolysis of Heavy Gas Oil Derived from Canadian Synthetic Crude Oil

2011· article· en· W2316664410 on OpenAlexaboutno aff
Xianghai Meng, Chunming Xu, Li Li, Jinsen Gao

Bibliographic record

VenueEnergy & Fuels · 2011
Typearticle
Languageen
FieldEngineering
TopicThermochemical Biomass Conversion Processes
Canadian institutionsnot available
Fundersnot available
KeywordsGasolineDiesel fuelFraction (chemistry)Fluid catalytic crackingCrackingPyrolysisLight crude oilChemistryFuel oilCokeYield (engineering)Pyrolysis oilOrganic chemistryChemical engineeringWaste managementMaterials scienceMetallurgy

Abstract

fetched live from OpenAlex

The secondary cracking performance of gasoline and diesel fractions from the catalytic pyrolysis of heavy gas oil derived from Canadian synthetic crude oil was investigated. Both diesel and gasoline fractions showed poor cracking performance. The feed conversion of the diesel fraction was below 51 wt %, and the yield of total light olefins was below 11 wt %. Meanwhile, the feed conversion of the gasoline fraction was below 30 wt % and the yield of total light olefins was below 7 wt %. The selectivity of total light olefins at 660 °C was only 22% for both fractions, and the selectivity of dry gas at 660 °C reached 25% for the diesel fraction and 34% for the gasoline fraction. The selectivity of coke at 660 °C reached 26% for the diesel fraction and 16% for the gasoline fraction. The diesel and gasoline fractions could partly crack to lighter components and could partly condense to heavier components. The condensation reaction played an important role in catalytic pyrolysis, and the selectivity of condensation products at 660 °C reached 42% for the cracking of the diesel fraction and 55% for the cracking of the gasoline fraction. The monomolecular cracking is the predominant cracking type, and both the free radical and the carbonium ion mechanisms play an important role under the experimental conditions.

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 score0.083
Threshold uncertainty score0.997

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 head0.009
GPT teacher head0.180
Teacher spread0.171 · 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

Citations9
Published2011
Admission routes1
Has abstractyes

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