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Record W2327348671 · doi:10.1021/ef4022784

Measurement and Prediction of Density for the Mixture of Athabasca Bitumen and Pentane at Temperatures up to 200 °C

2014· article· en· W2327348671 on OpenAlexaff
Hossein Nourozieh, Mohammad Kariznovi, Jalal Abedi

Bibliographic record

VenueEnergy & Fuels · 2014
Typearticle
Languageen
FieldChemistry
TopicPetroleum Processing and Analysis
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsAsphaltPentaneVolume (thermodynamics)Mixing (physics)ChemistryEquation of stateSpecific gravityThermodynamicsSolventBulk densityFraction (chemistry)Analytical Chemistry (journal)MineralogyMaterials scienceChromatographySoil scienceOrganic chemistryEnvironmental scienceComposite material

Abstract

fetched live from OpenAlex

The main recovery mechanism in the solvent-based bitumen recovery processes is gravity drainage. The density of heated bitumen or diluted bitumen at operational conditions is required to predict the production rate and cumulative oil recovery. In this manuscript, the densities of bitumen, pentane, and their mixtures at different pentane weight fractions (0.05, 0.1, 0.2, 0.3, 0.4, and 0.5) have accurately been measured. The measurements were conducted under conditions applicable for both in situ recovery methods and pipeline transportation of heavy oil. The experiments were taken using Athabasca bitumen at temperatures varying from ambient up to 200 °C and at pressures up to 10 MPa. The volume change upon mixing for the mixtures is evaluated from the experimental results, and the influence of pressure, temperature, and solvent weight fraction on the volume change upon mixing and density is studied. The density data are also represented with three different approaches considering no volume change, excess volume, effective liquid densities, and equation of state. The results indicated that the mixture data are well-predicted using equation of state and effective liquid densities with average absolute relative deviations (AARD) of 0.55% and 0.57%, respectively.

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 score0.013
Threshold uncertainty score0.235

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 head0.010
GPT teacher head0.210
Teacher spread0.199 · 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

Citations48
Published2014
Admission routes1
Has abstractyes

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