Deep-Vacuum Fractionation of Heavy Oil and Bitumen, Part II: Interconversion Method
Bibliographic record
Abstract
In Part I of this two-part series, a recently developed deep-vacuum fractionation apparatus (DVFA-II) was standardized and used to fractionate heavy oil samples by boiling point at a pressure below 1 Pa. Up to 50 wt % of a Western Canadian bitumen was distilled, compared with 26 wt % distilled with conventional spinning band distillation (SBD), and eight cuts were recovered. Here, an interconversion technique is developed to determine the normal boiling point (NBP) curve from the low-pressure boiling point data collected using DVFA-II. A simultaneous correlation of vapor pressure and heat capacity data based on the Clapeyron equation was used to determine the NBP of each cut. Of the vapor pressure correlations considered, the three-parameter Cox equation best fit the data, with average absolute relative deviations within 7% and 1% for the vapor pressure and heat capacity data (Δ C ′ exp ), respectively. The estimated maximum and minimum errors in the calculated NBPs were 2.2% and −2.5% (8 and 9 K), respectively, and the calculated NBPs were within 2% of the SBD data. It was demonstrated that the distillation data for a heavy oil sample follow a Gaussian distribution and therefore that the NBP curve of heavy oil maltenes can be represented well using a Gaussian extrapolation.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".