Deep-Vacuum Fractionation of Heavy Oil and Bitumen, Part I: Apparatus and Standardized Procedure
Bibliographic record
Abstract
Distillation assays provide the most widely used characterization data for modeling crude oils in refinery processes but can only define a relatively small fraction of the boiling curve for heavy oils. One way to extend the distillation range for heavy oils is to lower the distillation pressure while still avoiding thermal cracking temperatures. A vacuum distillation apparatus (DVFA-I) that was previously used to measure the vapor pressure of heavy components was modified to fractionate heavy oils and bitumens. The modified apparatus (DVFA-II) is a batch distillation system without reflux operating at pressures down to approximately 0.01 Pa, compared with 100 Pa for a conventional vacuum distillation. With the standard procedure developed for DVFA-II, up to 50 wt % of a Western Canadian bitumen was distilled into eight cuts plus a residue. This is an improvement over the 26 wt % distilled by a spinning band vacuum apparatus. The cumulative weight percent distilled was repeatable to within 2.3% for a given boiling point range. The densities and molecular weights of the cuts from two fractionations increased monotonically versus the weight percent distilled and were repeatable to within 0.18 and 4.0%, 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.007 |
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".