Catalyst Deactivation, Kinetics, and Product Quality of Mild Hydrocracking of Bitumen-Derived Heavy Gas Oils
Bibliographic record
Abstract
To assess mild hydrocracking as an option to improve the quality of the heavy gas oil (HGO) fraction of Syncrude's synthetic crude oil (known as Syncrude Sweet Blend or SSB), severe hydrotreating tests were performed by using Athabasca oilsands bitumen-derived coker HGO, heavy vacuum gas oil, and a blend of the two in a pilot-scale down-flow reactor over a typical commercial NiMo/Al2O3 hydrotreating catalyst. Kinetics of sulfur and nitrogen removal, 343°C+ conversion, and aromatics hydrogenation were investigated by incorporating the effect of catalyst deactivation. The total liquid products (TLPs) from the pilot tests were distilled into naphtha, light gas oil (LGO), and HGO fractions, and the TLPs and distilled products were characterized. Cetane number (CN) was determined by engine test for selected LGOs and by ignition quality tester for all LGOs. The quality of product HGOs as fluid catalytic cracking (FCC) unit feedstock was evaluated by using correlations (developed based on feed properties including GC-MS data) to predict FCC product yields. The CN of the LGOs and the predicted gasoline yields from HGO products were much better than that produced from the corresponding fractions of current SSB. The CN and FCC gasoline yield were related to the level of 343°C+ conversion (i.e., the higher the conversion, the higher the CN and FCC gasoline yield).
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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 | 0.000 |
| 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".