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Effect of Diluents on the Rheological Properties of Maya Crude Oil

2016· article· en· W2235855564 on OpenAlexafffund
Sepideh Mortazavi-Manesh, John M. Shaw

Bibliographic record

VenueEnergy & Fuels · 2016
Typearticle
Languageen
FieldChemistry
TopicPetroleum Processing and Analysis
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaTotalVirtual Materials GroupConocoPhillipsShellUniversity of AlbertaBP
KeywordsDiluentThixotropyTolueneShear thinningRheologyChemistryButanoneViscosityHeptaneRheometerChromatographyChemical engineeringOrganic chemistryMaterials scienceComposite material

Abstract

fetched live from OpenAlex

Understanding the rheological properties of mixtures of heavy oil or bitumen and diluents, specifically at low temperatures, is key in designing different processes employed in the production or transportation of these resources reliably and efficiently. The effect of diluents ( n -heptane, toluene, and toluene + butanone (50:50 vol %)) on the non-Newtonian behavior of Maya crude oil including shear thinning and thixotropy at temperatures from 258 to 333 K are discussed. Toluene + butanone (50:50 vol %) addition to Maya crude oil induces the greatest reduction in shear thinning behavior irrespective of temperature. Thixotropic properties of mixtures of Maya crude oil and diluent were studied through startup experiments. It was shown that toluene + butanone (50:50 vol %) is the best diluent in moderating the thixotropic effect, whereas n -heptane showed the most pronounced thixotropic effect. It was shown that toluene + butanone (50:50 vol %) is more promising in decreasing oil viscosity in comparison to two other diluents tested. Less of this diluent is required to decrease the viscosity to a certain value, which confirms its potential to be used in the industry as a diluent. The results presented also provide a reliable database for model development and evaluation.

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 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 score0.066
Threshold uncertainty score0.419

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 head0.012
GPT teacher head0.219
Teacher spread0.208 · 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 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

Citations34
Published2016
Admission routes2
Has abstractyes

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