Product Selectivity during Hydrotreating and Mild Hydrocracking of Bitumen-Derived Gas Oil
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Bibliographic record
Abstract
The hydrotreating (HT) and mild hydrocracking (MHC) of gas oil derived from Athabasca bitumen have been performed in a micro trickle-bed reactor, using a commercial NiMo/Al 2 O 3 catalyst. The operating conditions were varied as follows: temperature range, 340−420 °C; reactor pressure, 6.5−11.0 MPa; liquid hourly space velocity (LHSV) range, 0.5−2.0 h - 1; and hydrogen/gas oil ratio, 600 mL/mL. The removal values of sulfur and total nitrogen, basic nitrogen, and non-basic nitrogen obtained under optimum conditions were 99, 92, 99, and 88 wt %, respectively. The highest selectivities for sulfur and basic nitrogen removal occurred at the lowest temperature and pressure and the highest LHSV values (i.e., 340 °C, 6.5 MPa, and 2 h - 1, respectively), whereas those for total and non-basic nitrogen removal occurred at the highest temperature and pressure and the lowest LHSV values (i.e., 420 °C, 11 MPa, and 0.5 h - 1, respectively). High levels of aromatic saturation were also observed (22.7 wt % aromatics at 400 °C and 11 MPa). The overall boiling point of each product fraction decreased over the entire temperature range, because of (i) the conversion of sulfur and nitrogen heteroatoms at lower temperatures (≤380 °C) and (ii) MHC at higher temperatures (>380 °C). The yield and selectivities of gasoline, kerosene, and light gas oil (LGO) increased as the operating severity increased. The highest yield of gasoline (10 wt %), kerosene (12 wt %), and LGO (19 wt %) were obtained at the highest severity of 420 °C. Vacuum gas oil (VGO) was the main fraction of the gas oil feed that apparently underwent conversion. No apparent significant change was observed in the net content of the heavy gas oil (HGO) fraction under all operating conditions. A reaction pathway is postulated for the conversion of the gas oil to products via heteroatom removal, saturation of aromatics, and hydrocracking.
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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 it