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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 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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
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 head0.052
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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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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