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Record W2905744056

Modeling Fluid Coker Cyclone Fouling

2018· article· en· W2905744056 on OpenAlex
Erica Glatt

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueScholarship@Western (Western University) · 2018
Typearticle
Languageen
FieldEngineering
TopicCyclone Separators and Fluid Dynamics
Canadian institutionsnot available
FundersSyncrudeMcKnight Foundation
KeywordsCoker unitCyclone (programming language)FoulingEnvironmental scienceMeteorologyWaste managementEngineeringChemistryGeography
DOInot available

Abstract

fetched live from OpenAlex

Fluid CokingTM is a continuous process that thermally converts heavy hydrocarbons, such as oil-sands bitumen, to lighter and higher-value products by horizontal injection onto a fluidized bed of hot coke particles. The deposition of carbonaceous materials in the cyclone sections of commercial Fluid Cokers has been observed throughout each run. The main objective of this work is to improve unit reliability by proposing cyclone fouling mitigation strategies based on a localized phenomenological model using Aspen Plus®. The heavy ends condensation fouling mechanism was studied by incorporating vapour-liquid thermodynamics, thermal cracking reactions, and overall fluid dynamics in the Fluid Coker. Four case studies were performed to determine the impacts of transfer line temperature, hot coke flow rate, hot coke entrainment and scouring coke flow rate on the predicted temperatures and liquid flow rates. Scouring coke flow rate was identified as the most promising process lever to mitigate Fluid Coker cyclone fouling.

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.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.245
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.281
Teacher spread0.225 · 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