Product Selectivity during Hydrotreating and Mild Hydrocracking of Bitumen-Derived Gas Oil
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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) | 0.001 | 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 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".