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Record W2093777488 · doi:10.2495/cmem090121

Study of the thermo-physical properties of bitumen in hydrocarbon condensates

2009· article· en· W2093777488 on OpenAlexafffund
Adango Miadonye, J. Cyr, K. Secka, Allen J. Britten

Bibliographic record

VenueWIT transactions on modelling and simulation · 2009
Typearticle
Languageen
FieldChemistry
TopicPetroleum Processing and Analysis
Canadian institutionsCape Breton University
FundersNatural Sciences and Engineering Research Council of CanadaUniversities Space Research Association
KeywordsAsphalteneAsphaltToluenePrecipitationViscositySolventDiluentHydrocarbonMixing (physics)Chemical engineeringChemistryAromatic hydrocarbonOrganic chemistryMaterials scienceComposite material

Abstract

fetched live from OpenAlex

The reduction of viscosity with inappropriate diluents could lead to asphaltene precipitation. To use condensates effectively however, their effects on asphaltene precipitation and deposition must be well examined. In this study, the influence of inter-particle interactions of asphaltene on the viscosity and heat of mixing in solutions of bitumen-condensate mixtures are compared to those of bitumen in aromatic solvents and in condensate-toluene mixtures. Three bitumen, two heavy oils, and five condensates from different reservoirs were examined. The results indicate that the presence of aromatic solvent in bitumen delays the on-set of asphaltene precipitation due to the presence of dipole-dipole interactions as well as hetero-molecular interactions. In pure condensates, strong hydrogen bonding and moderate homo-molecular interactions are predominant and, therefore, result in reduced asphaltene precipitation and low enthalpy of mixing. The on-set of asphaltene precipitation in bitumen and heavy oils is delayed in the presence of aromatic solvents, decreasing with reduction in the composition of aromatic solvent in the condensates.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.198

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 head0.030
GPT teacher head0.250
Teacher spread0.219 · 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

Citations3
Published2009
Admission routes2
Has abstractyes

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