Catalytic performances of NiMo/Zr-SBA-15 catalysts for the hydrotreating of bitumen derived heavy gas oil
Bibliographic record
Abstract
Gas-oil obtained from bitumen contains a significant amount of impurities, which are difficult to remove using a conventional alumina supported hydrotreating catalyst. Innumerable studies have been carried out to develop a highly effective hydrotreating catalyst, and among all utilizing more advanced support is considered as a better alternative. Recently, SBA-15, which is an ordered mesoporous silica support, has received importance as a catalyst support because of its excellent textural properties. However, SBA-15 lacks surface acidity and provides very low metal-support interaction. By modifying SBA-15 with zirconia, an optimum level of surface acidity and Si-Mo interaction can be achieved. Also, by doping zirconia with SBA-15, the textural properties of zirconia can be improved. Hence, a synergistic effect can be obtained while incorporating zirconia onto SBA-15 and the resulting material Zr-SBA-15 can be used as an effective support for hydrotreating catalyst. In the present study, Zr-SBA-15 supports were prepared by the post synthesis and the direct synthesis method with different zirconia loading. Zr-SBA-15 supported NiMo catalysts were prepared by incipient wetness impregnation technique. Catalysts and supports were characterized by small angle X-ray scattering (SAXS), nitrogen adsorption/desorption (BET), powder X-ray diffraction (XRD), transmission electron spectroscopy (TEM), scanning electron microscopy (SEM), and Raman spectroscopy methods. Characterization of support confirmed that the zirconia was successfully incorporated in a mesoporous SBA-15 structure without significantly changing the textural properties of SBA-15. The performance of the Zr-SBA-15 supported NiMo catalysts was evaluated based on hydrodesulfurization and hydrodenitrogenation activities exhibited during hydrotreating of heavy gas oil derived from Athabasca bitumen at industrial operating condition (temperature 375-395 °C, pressure 8.9 MPa, LHSV 1.0 hr-1 and gas/oil ratio 600 Nm3/m3). The comparison of catalytic activities showed that the NiMo catalysts supported on Zr-SBA-15, prepared by direct and post synthesis method exhibited higher hydrotreating activity compared to SBA-15 supported catalyst. NiMo catalyst supported on Zr-SBA-15 with 23 wt% of ZrO2 loading, prepared by post synthesis method showed the highest activity among all the catalysts. After determining the best support, the optimum catalyst metal loadings on the Zr-SBA-15 support was found to be 17 wt% of Mo and 3.4 wt% of Ni. This catalyst also showed higher activity in mass basis for the hydrotreating of heavy gas oil compared to that of commercial hydrotreating catalyst. A kinetic study was performed on the optimum NiMo/Zr-SBA-15 catalyst to predict its HDS and HDN activities while varying the parameters of temperature, liquid hourly space velocity (LHSV), pressure and gas-to-oil ratio. Rate expressions were developed using Power Law and Langmuir-Hinshelwood model to predict the behavior of both the HDS and HDN reactions. Power law models were best fit with reaction orders of 1.8 and 1.3, and activation energies of 115 kJ/mol and 121 kJ/mol, for HDS and HDN reactions, respectively. The activation energies calculated using Langmuir-Hinshelwood model considering H2S inhibition were found to be 122 kJ/mol and 138 kJ/mol, for HDS and HDN reactions, respectively.
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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 | 0.001 | 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".