The Phase Behavior of Heavy Oil and Propane Mixtures
Bibliographic record
Abstract
Abstract The phase behavior of heavy oil and propane mixtures was mapped from temperatures ranging from 20 and 180°C and pressures up to 10 MPa. Both vapour-liquid (VL) and liquid-liquid (LL) regions were observed. Saturation pressures (VL boundary) were collected in a Jefri 100 cm3 PVT cell and blind cell apparatus. The propane content at which a propane-rich light phase and a heavy pitch phase formed (LL boundary) were visually determined with a high pressure microscope while titrating propane into the bitumen. High pressure and high temperature yield data were measured using a blind cell apparatus. Here, yield is defined as the mass of the indicated component(s) in the heavy pitch phase divided by the mass of bitumen in the feed. A procedure was developed and used to measure pitch-rich and propane-rich phase compositions in a PVT cell. Pressure-temperature and pressure-composition phase diagrams were constructed from the saturation pressure and heavy pitch phase onset data. High pressure micrographs demonstrated that, at lower temperatures and propane contents, the heavy pitch phase appeared as glassy particles while at higher yields and temperatures it appeared as liquid phase. Propane-free pitch yields as high as 70 wt% of the bitumen were observed. The propane contents of the pitch phase at 50°C and 2 MPa, 50°C and 5 MPa, and 130°C and 10 MPa (all for a feed of 50 wt% propane) were 22 ±0.4 wt%, 26 ±0.4 wt% and 2 ±1 wt%, respectively. The ability of a cubic equation of state to model the data was explored. The model with temperature dependent binary interaction parameters (BIP) matched the saturation pressures and the LL boundaries at one pressure. However, the model could not match the effect of pressure on the LL boundary. The model also underpredicted yields at high dilution and could not match the LL phase compositions.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".