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Record W4247663979 · doi:10.32920/ryerson.14653860

Dispersion Coefficient Determination In Vapex Process

2021· preprint· en· W4247663979 on OpenAlexaff
Muhammad Imran

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldChemistry
TopicPetroleum Processing and Analysis
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsDispersion (optics)ButaneMaterials scienceViscositySolubilityAsphaltWork (physics)Petroleum engineeringSolventThermodynamicsMechanicsAnalytical Chemistry (journal)ChemistryMineralogyChromatographyComposite materialOrganic chemistryOpticsPhysicsGeology

Abstract

fetched live from OpenAlex

In this work, the dispersion of butane in heavy oil and bitumen is experimentally determined during the V apex process with varying drainage heights. The experiments are performed at constant temperature using butane as solvent at the dew point pressure. Cylindrical models with different heights packed with uniform mixture of bitumen and glass beads are used. From the experimental data, the production rate dependency towards the drainage height is evaluated along with determination of butane gas solubility, live oil density and viscosity. A mathematical model is developed to simulate live oil production rates. A variable metric method in multi dimensions is used to optimally determine concentration dependent dispersion coefficient as well as solvent mass fraction at liquid gas interface by matching up experimental and predicted live oil production rates. A computational algorithm is developed to solve a set of models simultaneously to evaluate effect of drainage height on dispersion of solvent gas.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
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 head0.012
GPT teacher head0.276
Teacher spread0.264 · 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

Citations3
Published2021
Admission routes1
Has abstractyes

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