MétaCan
Menu
Back to cohort
Record W2092047553 · doi:10.1080/10916460500296528

Viscosity Model for Petroleum Gases

2007· article· en· W2092047553 on OpenAlexafffund
Andrew J. Parsons, Tara M. McKenna, Adango Miadonye

Bibliographic record

VenuePetroleum Science and Technology · 2007
Typearticle
Languageen
FieldEngineering
TopicVehicle emissions and performance
Canadian institutionsCape Breton University
FundersCoordenação de Aperfeiçoamento de Pessoal de Nível SuperiorCape Breton University
KeywordsViscosityCarbon dioxidePetroleumPetroleum engineeringThermodynamicsWork (physics)Environmental scienceChemistryGeologyPhysicsOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract The production processes for petroleum gases employ a broad range of simulation packages to reduce capital, time, and cost associated with actual recovery and pipeline transportation. Viscosity model is an important component of these packages. In this work, we have presented an empirical model for predicting the viscosity of petroleum gases, developed from the three-parameter Yaws equation. New constants were derived for various petroleum gases, as well as for gaseous carbon dioxide. Results obtained with the new model were compared with the viscosity predictions from the Yaws model, and the Miadonye and Clyburn correlation. For four petroleum gases and carbon dioxide at temperatures of 110 K to 1,500 K, the model gave an excellent viscosity prediction with overall average absolute deviations of 0.34% and 0.98%, respectively. The model is simple to incorporate into design and simulation packages, and more accurate than any correlation currently used in petroleum industry for predicting the viscosities of petroleum gases.

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 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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.907
Threshold uncertainty score0.365

Codex and Gemma teacher scores by category

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

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 head0.011
GPT teacher head0.242
Teacher spread0.231 · 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 teacher head, 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

Citations0
Published2007
Admission routes2
Has abstractyes

Explore more

Same venuePetroleum Science and TechnologySame topicVehicle emissions and performanceFrench-language works237,207