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Record W4385886268 · doi:10.55274/r0011492

PR-461-14602-R02 Sample Mixing and Centrifugal Forces on Density and Sediment-Water Results

2018· report· en· W4385886268 on OpenAlexaff
Bei Zhao, James Laszlo, Heather Kaminsky

Bibliographic record

Venuenot available
Typereport
Languageen
FieldEngineering
TopicMineral Processing and Grinding
Canadian institutionsNorthern Alberta Institute of Technology
Fundersnot available
KeywordsCentrifugeMixing (physics)Sample (material)SedimentPetroleum engineeringViscosityEnvironmental scienceCrude oilPipeline (software)MechanicsMaterials scienceSoil scienceGeologyChemistryChromatographyMechanical engineeringEngineeringPhysics

Abstract

fetched live from OpenAlex

The crude oil being transported by pipeline varies widely in density, viscosity, sulfur content, and other fluid properties. In a custody transfer, both parties are concerned with the quantity and the quality of the crude oil since this determines the monetary value of the transaction. The objective of this project is to investigate various factors that can impact accurate determination of the density, sediment and water content of crude. The findings will be critical to improve current procedures, including proper sample mixing and handling, analytical methodologies, and operation parameters, so that reliable and accurate custody transfer can be provided to all involved parties internationally. This project investigated the impact of mixing and analytical methodologies on crude density and S and W determination. Operating parameters tested included mixing time (3 or 5 minutes), mixing system (new or old) and sample size (3 or 7 liters). Density determination was conducted by a thermos-hydrometer test and a digital density meter and the results were compared. S and W content was quantified by the centrifuge method and Karl Fischer titration and the results were compared. For the centrifuge method, the impact of two centrifugal forces on S and W results was also studied. There is a related webinar.

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)0.000
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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.105
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
Bibliometrics0.0000.000
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 head0.025
GPT teacher head0.249
Teacher spread0.224 · 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.

Study designNot applicable
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
Published2018
Admission routes1
Has abstractyes

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