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Record W2001063477 · doi:10.1021/ie070781d

Catalytic Pyrolysis of Gas Oil Derived from Canadian Oil Sands Bitumen

2008· article· en· W2001063477 on OpenAlexaboutno aff
Li Li, Gang Wang, Xianghai Meng, and Chunming Xu, Jinsen Gao

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2008
Typearticle
Languageen
FieldChemistry
TopicPetroleum Processing and Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsCokePyrolysisOil sandsChemistryCatalysisSynthetic crudeAsphalteneGasolineFluidized bedProduct distributionLight crude oilAsphaltYield (engineering)Fluid catalytic crackingChemical engineeringFossil fuelOrganic chemistryMaterials scienceUnconventional oilMetallurgyComposite material

Abstract

fetched live from OpenAlex

The catalytic pyrolysis behaviors of gas oil derived from Canadian oil sands bitumen over catalyst CEP-1 in a confined fluidized bed reactor have been investigated in the present paper. The effect of reaction temperature, catalyst-to-oil weight ratio, steam-to-oil weight ratio, and residence time of oil gas on product distribution was researched. The results show that the optimal reaction temperature, weight ratios of catalyst to oil and steam to oil, and residence time of oil gas are about 660 °C, 15, 0.55, and 2 s, respectively. Under the optimal operating conditions, the yield of total light olefins exceeds 38.0 wt %. A new five-lump kinetic model is developed for the catalytic pyrolysis of gas oil, and a catalyst deactivation model based on the coke content is also presented. Rate constants and apparent activation energies were estimated with the least-squares method. The effect test shows that the five-lump kinetic model can predict the yields of gasoline, light olefins, light alkanes, and coke very well.

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 score0.819
Threshold uncertainty score0.360

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 head0.055
GPT teacher head0.278
Teacher spread0.223 · 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

Citations15
Published2008
Admission routes1
Has abstractyes

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