Measurement and Evaluation of Bitumen/Toluene-Mixture Properties at Temperatures Up to 190?°C and Pressures Up to 10 MPa
Bibliographic record
Abstract
Summary The viscosity of bitumen and heavy oil is extremely high at both reservoir and surface conditions, on the order of 1 million cp. Therefore, viscosity reduction is necessary for production from the reservoir, pipeline transportation, and oil processing. The aim of this study is to evaluate the effect of different parameters (temperature, pressure, and solvent-weight fraction) on the density and viscosity of bitumen-containing mixtures. Thus, the density and viscosity of mixtures are measured for a sample of Athabasca bitumen diluted with different fractions of toluene at pressures from 0.1 to 10 MPa and at temperatures from 22 to 190 °C. The mixture densities show a linear decrease with temperature, pressure, and solvent concentration. The viscosity of the mixtures indicates a curvilinear trend with respect to the solvent-weight fraction and temperature. The effect of pressure on the mixture viscosity is more pronounced at lower-solvent-weight fractions. The mixture-density data are evaluated with two different methods: no volume change upon mixing and excess volume. The excess-volume method predicts the mixture-density data with an overall average absolute relative deviation (AARD) of 0.34%. The viscosity data for mixtures are compared with different models: Arrhenius (1987), Cragoe (1933), Shu (1984), Lobe (1973), double-log (Yarranton et al. 2013), Lederer (1933), power-law (Kendall and Monroe 1917), and Bij (Yarranton et al. 2013). The Bij model (Yarranton et al. 2013) produces the most-reliable results for mixture viscosities, with 5.5% AARD.
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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".