Characterization of Heavy Oils and Bitumens. 1. Vapor Pressure and Critical Constant Prediction Method for Heavy Hydrocarbons
Bibliographic record
Abstract
Vapor pressures, critical constants, and the acentric factor are generally used in thermodynamic correlations based on corresponding states to perform phase equilibrium and physical property calculations. These thermophysical properties cannot be measured for heavy hydrocarbons due to thermal decomposition at temperatures far below the critical point. An integrated method is described for predicting the critical constants as well as the vapor pressures over a broad range of temperatures, and it is the first step for the development of a comprehensive methodology for the characterization and simulation of heavy oil and bitumen systems. The method applies perturbation theory using n -paraffins as a reference system and correlates departures of the heavy hydrocarbons from paraffinic behavior. The critical constants and vapor pressures of heavy hydrocarbons were correlated as a function of only their molecular weight and specific gravity at 15.6 °C. The molecular weights ranged from 28.05 to 695.30 g/gmol, while the specific gravities ranged from 0.4327 to 1.4154. For the hydrocarbons used in this study, the predicted critical constants and vapor pressures showed a significant improvement over previously published correlations. The experimental critical temperatures and critical pressures were reproduced closely with an average absolute percentage deviation of 2 and 8%, respectively. The resultant vapor pressure equation fit the available vapor pressure data with an average absolute deviation of 17% between reduced temperatures of 0.37 and 0.95.
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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 | 0.002 | 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 | 0.000 |
| 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".