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Record W2040857468 · doi:10.1021/ef000115o

Comparison of Catalytic Cracking Performance between Riser Reactor and Microactivity Test (MAT) Unit

2001· article· en· W2040857468 on OpenAlexaff
Siauw Ng, Hong Yang, Jinsheng Wang, Yuxia Zhu, Craig Fairbridge, Sok Yui

Bibliographic record

VenueEnergy & Fuels · 2001
Typearticle
Languageen
FieldChemistry
TopicPetroleum Processing and Analysis
Canadian institutionsSyncrude (Canada)
Fundersnot available
KeywordsCokeFluid catalytic crackingCrackingYield (engineering)Liquefied petroleum gasEnvironmental sciencePetroleum engineeringFlow (mathematics)Materials scienceNuclear engineeringWaste managementEngineeringMechanicsComposite materialPhysics

Abstract

fetched live from OpenAlex

A fluid catalytic cracking (FCC) study of 10 vacuum gas oil feeds was performed using a fixed-bed microactivity test (MAT) unit. Gas, liquid, and coke yields at several conversion levels were compared with reported pilot plant data obtained from a modified ARCO riser reactor using the same feeds. The data indicated that except for coke yield, MAT results were, in most cases, within 15% of the corresponding riser yields. Good linear correlations could be established between MAT and riser yields except for liquefied petroleum gas (LPG) and light cycle oil (LCO). Causes for the relatively poor correlations for the two products were analyzed and discussed. Despite the substantial differences in reactor design, flow pattern, and operation between the two systems, the MAT unit can predict the riser performance when appropriate test conditions are applied.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.027
GPT teacher head0.277
Teacher spread0.250 · 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

Citations10
Published2001
Admission routes1
Has abstractyes

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