Performances of Co−W/γ-Al<sub>2</sub>O<sub>3</sub> Catalysts on Hydrotreatment of Light Gas Oil Derived from Athabasca Bitumen
Bibliographic record
Abstract
γ-Al 2 O 3 -supported Co−W-based catalysts with varying cobalt (1−3 wt %) and tungsten (7−13 wt %) loadings were prepared using impregnation and sonochemical methods. Brunauer−Emmett−Teller (BET) analysis indicated that the sonochemical method of preparation resulted a larger reduction in surface area of the γ-Al 2 O 3 support than the impregnation method for all the prepared catalysts. X-ray photoelectron spectroscopy (XPS) showed that most of tungsten metal segregated on the support surface of sonochemically prepared catalysts, whereas catalysts prepared via the impregnation method showed uniform metal dispersion on the support. The performances of all the synthesized catalysts were tested at a pressure of 8.9 MPa, a liquid hourly space velocity (LHSV) of 2 h -1, and temperatures of 340, 350, and 360 °C in a trickle-bed microreactor for the hydrodesulfurization (HDS) and hydrodenitrogenation (HDN) of light gas oil (LGO) derived from Athabasca bitumen. The initial screening tests indicated that an impregnated catalyst with 3 wt % cobalt and 10 wt % tungsten and a sonochemical catalyst with 3 wt % cobalt and 13 wt % tungsten are the most active catalysts for the HDN and HDS of LGO. These two catalysts were selected for detail performance, optimization, and kinetic studies. The effects of reaction temperature (340−380 °C), pressure (7.6−10.3 MPa), LHSV (1.5−2.0 h -1 ), and H 2 /gas oil ratio (400−800 mL/mL) were examined in the HDS and HDN of LGO with these catalysts. The impregnated catalyst showed higher nitrogen and sulfur conversion than the sonochemical catalyst under all reaction conditions. The reaction kinetics for HDS was best-fitted with a power-law model, whereas the same for HDN was determined to be best represented by a Langmuir−Hinshelwood model with a reasonable accuracy (0.90 < R 2 <0.95).
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".