Experimental Measurement and Thermodynamic Modeling of Vapor–Liquid Equilibrium Data of C<sub>1</sub>/Bitumen, CO<sub>2</sub>/Bitumen, C<sub>1</sub>/CO<sub>2</sub>/Bitumen, C<sub>1</sub>/Diluent/Bitumen, and C<sub>1</sub>/CO<sub>2</sub>/Diluent/Bitumen Systems
Bibliographic record
Abstract
Solvent-assisted heavy oil recovery methods have gained significance in recent years. The injected solvents can be of hydrocarbon and non-hydrocarbon chemicals or a mixture of both. The addition of a diluent, as a mixture of light and intermediate hydrocarbons, to bitumen has a significant effect on reducing the viscosity and density of bitumen. In this work, the experimental measurements and thermodynamic modeling of thermophysical properties of C 1 /bitumen, CO 2 /bitumen, C 1 /CO 2 /bitumen, C 1 /diluent/bitumen, and C 1 /CO 2 /diluent/bitumen systems are performed. The measured data includes liquid viscosity, liquid density, and mole fraction of components in vapor and liquid phases. Experimental data show that the addition of CO 2 into the injected gas mixture results in bitumen viscosity reduction, especially at lower temperatures. A diluent has a significant effect on the viscosity and density of the liquid mixtures in all cases. The results showed that the component K -values are nearly independent of the solvent composition. The consistency of K -values of the intermediate components was examined by the Hoffmann and Wilson plots. The obtained linear trends revealed that the K -values of the intermediate components are only a function of temperature and pressure. The thermodynamic modeling of phase equilibria is performed using the cubic plus association equation of state. The unknown parameters of the model, including binary interaction parameters and critical properties of C 6+ in the diluent, are tuned versus experimental liquid density and solvent solubility (i.e., the mole fraction of solvent in the bitumen-rich phase). The developed model is able to predict the liquid density, C 1 solubility, and CO 2 solubility with an absolute average relative deviation of 0.28, 11.41, and 12.22%, respectively.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".