Detailed Characterization of Diesel Fractions from Co-Hydroprocessing Vegetable Oil and Petroleum Heavy Vacuum Gas Oil Blends
Bibliographic record
Abstract
In this study, 20 diesel fractions were obtained by co-hydroprocessing blends of low-grade canola oil and Canadian oil sand bitumen-derived heavy vacuum gas oil (HVGO) at different canola oil/HVGO blending ratios, reaction temperatures and pressures, and liquid hourly space velocities. A commercial hydroprocessing catalyst was used in the experiments under typical commercial operating conditions. The obtained diesel fractions were fully characterized by using standard ASTM methods and advanced two-dimensional gas chromatography. Characterization results of the diesel fractions showed that, with the increased canola oil content in the feed blends, the contents of aromatics and cycloparaffins decreased and the content of isoparaffins remained relatively constant. In contrast, the content of normal paraffins (n-paraffins) increased. The observed increase in the n-paraffins in the diesel fractions was attributed to the hydrodeoxygenation and hydrodecarboxylation of triglycerides in the canola oil. The n-paraffins in the diesel were mostly n-heptadecane (product of hydrodecarboxylation) and n-octadecane (product of hydrodeoxygenation) with traces of other lighter or heavier n-paraffins. The formation of n-heptadecane and n-octadecane resulted in improved physical and combustion properties of the diesel fractions, such as density, boiling point distribution, and cetane index/number.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".