Density and Viscosity for Mixtures of Athabasca Bitumen and Aromatic Solvents
Bibliographic record
Abstract
A new experimental apparatus was used to accurately measure the density and viscosity of aromatic solvents (toluene and xylenes), of Athabasca bitumen, and of their mixtures at different compositions. The measurements were taken under conditions applicable for both in situ recovery methods and pipeline transportation of heavy oil, that means, at temperatures varying from ambient temperature up to 343.15 K and at pressures up to 10 MPa and on mixtures with different weight fractions of the solvents (0.05, 0.1, 0.2, 0.3, 0.4, 0.5, and 0.6). The experimental density and viscosity data for the solvents and for raw bitumen were correlated using different correlation equations from the literature. Based on the experimental results, the influence of pressure, temperature, and solvent weight fraction on the density and viscosity of the mixtures was considered. The experimental density and viscosity data for the mixtures of Athabasca bitumen with toluene and xylenes were evaluated with predictive schemes as well as with correlation models representing certain mixing rules proposed in the literature. The density data are well predicted using an equation without adjustable parameter in which it is assumed that no volume change occurs. In contrast, the viscosity data are correlated reasonably over the studied conditions with Lederer’s and power law models which include one adjustable parameter each.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".