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Record W2009132386 · doi:10.1002/cjce.20225

The use of physical modeling in the optimisation of a primary separation vessel feedwell

2009· article· en· W2009132386 on OpenAlexaffvenue
J. Tyler, J. Spence, Darwin Kiel, Jason Schaan, George C Larson

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2009
Typearticle
Languageen
FieldEngineering
TopicDrilling and Well Engineering
Canadian institutionsSyncrude (Canada)Coanda Research and Development Corporation (Canada)
Fundersnot available
KeywordsAsphaltSeparation (statistics)Materials scienceFlow (mathematics)Scale (ratio)Mechanical engineeringEngineeringMechanicsComputer scienceComposite materialPhysics

Abstract

fetched live from OpenAlex

Abstract A physical modeling program was undertaken to assess and eliminate a significant vessel wear problem observed in a primary separation vessel used in the recovery of bitumen from oil sands. A 1:20 scale model was fabricated, and process rates and materials were selected on the basis of dimensional analysis. Experiments showed that the existing feedwell produced poor circumferential distribution and formed a localised jet that entered the vessel over a narrow sector. Tests were then conducted with several different modifications to the feedwell to improve the flow distribution. The final design produced uniform circumferential distribution and a modest improvement in model separation efficiency. This design has been in commercial service for several years and has eliminated the local wear problems.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
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 head0.020
GPT teacher head0.195
Teacher spread0.176 · 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

Citations2
Published2009
Admission routes2
Has abstractyes

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