Maximizing Aromatic Hydrogenation of Bitumen-Derived Light Gas Oil: Statistical Approach and Kinetic Studies
Bibliographic record
Abstract
Bitumen-derived light gas oil (LGO) was hydrotreated over commercial NiMo/Al 2 O 3 catalysts in a trickle bed reactor. Statistical design of experiments was used to develop response surface models for predicting percentage conversions of aromatics, sulfur, and nitrogen in the LGO feed from Athabasca oil sands. The statistical approach was also used to study the effect of process variables and their interaction on aromatic hydrogenation (AHYD), hydrodesulfurization (HDS), and hydrodenitrogenation (HDN) activities. The two-level interaction between temperature and pressure was determined to affect AHYD significantly, whereas the interaction between temperature and the liquid hourly space velocity (LHSV) was the most important parameter affecting both HDS and HDN activities. Optimal conditions for the conversion of aromatics were observed at a temperature of 379 °C, a pressure of 11.0 MPa, and an LHSV of 0.6 h - 1 . Under these conditions, a maximum conversion of 63% can be attained. The cetane index of the diesel fraction was affected by changes in the aromatic compounds, as well as by the temperature and pressure of hydrotreating. Product distribution and gasoline yield of the liquid products were also greatly influenced by the reaction temperature, with a slight impact from pressure and LHSV. The kinetics of AHYD was modeled using a singe-site mechanism form of the Langmuir−Hinshelwood rate of reaction, whereas HDS and HDN were best described by an irreversible pseudo-first-order power-law reaction. Results of the kinetic studies showed significant inhibition of hydrogenation by hydrogen sulfide (H 2 S) gas produced during the HDS process.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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 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".