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Record W2012620740 · doi:10.1021/ie050683x

Kinetic Modeling of Catalytic Cracking of Gas Oil Feedstocks:  Reaction and Diffusion Phenomena

2006· article· en· W2012620740 on OpenAlexaff
Mustafa Al‐Sabawi, J. A. Atias, Hugo de Lasa

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2006
Typearticle
Languageen
FieldChemistry
TopicZeolite Catalysis and Synthesis
Canadian institutionsWestern University
Fundersnot available
KeywordsFluid catalytic crackingCokeCrackingCatalysisZeoliteGasolineChemical engineeringSelectivityFluidized bedChemistryDiffusionHydrocarbonMaterials scienceDry gasThermodynamicsOrganic chemistry

Abstract

fetched live from OpenAlex

Catalytic cracking experiments of vacuum gas oil on fluid catalytic cracking (FCC)-type catalysts are carried out in a fluidized bench-scale batch Chemical Reactor Engineering Centre (CREC) riser simulator reactor. These experiments are conducted under operating conditions similar to those of the industrial FCC process in terms of temperature, catalyst-to-oil ratio, partial pressure of reactant and products, and reaction times. The crystallite size of the supported zeolite is varied between 0.4 and 0.9 microns with both activity and selectivity being monitored. A five-lump kinetic model describing the catalytic cracking of vacuum gas oil is considered, which accounts for diffusional constraints experienced by hydrocarbon species while evolving in the zeolite pore network. This study provides insights into the effect of intracrystalline diffusion in the catalytic cracking of heavy feedstocks. Results show that the catalyst with the smaller crystallite size provides higher activity and selectivity for desirable intermediate products (gasoline) and lower selectivity for terminal products (coke).

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)0.001
metaresearch head score (Gemma)0.001
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 score0.088
Threshold uncertainty score0.822

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 integrity0.0000.001
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 head0.052
GPT teacher head0.275
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 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

Citations32
Published2006
Admission routes1
Has abstractyes

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