The Phase Behavior of Athabasca Vacuum Bottoms + n-Alkane Mixtures
Bibliographic record
Abstract
Abstract The phase behavior of Athabasca vacuum bottoms (ABVB), a 798.15+ K (525+ °C) boiling fraction comprising 32 wt. % pentane asphaltenes, + pentane mixtures is elucidated using x-ray transmission tomography. These pseudo binary mixtures are models for the development of novel heavy oil/bitumen production and refining processes. Depending on the overall composition, these mixtures are shown to exhibit three and four phase equilibria including both expected (L1L2V) and unexpected (L2L3V) phase behavior separated by a small L1L2L3V zone. These multiphase equilibria provide challenges in production environments, where miscible flooding is typically desired, but afford new opportunities for the development of separation technologies in refining. Example pressure-temperature at constant composition phase diagrams and pressure-composition at constant temperature phase diagrams, which focus on the bubble pressure region where multiphase regions arise, are presented. Limited phase equilibria data sets for ABVB + heptane, decane and dodecane are also presented so that trends for ABVB + alkane mixtures with the size of the alkane diluent can be explored. We expect these phase diagrams to provide insights to heavy oil producers and refiners in addition to providing a benchmark for testing phase behaviour models for these and related complex hydrocarbon mixtures.
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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".