Effect of pore diameter variation of FeW/SBA-15 supported catalysts on hydrotreating of heavy gas oil from Athabasca bitumen
Bibliographic record
Abstract
The pore diameter of a catalyst support controls the diffusion of reactant molecules to the catalytic active sites; thus, affecting the rates and conversions of the hydrotreating reactions. Desirable textural properties of SBA-15 makes it a potential alternative to the conventionally used ɣ-Al₂O₃ support due to the fact that its pore size can be manipulated via controlling the synthesis parameters, while maintaining relatively high surface area. Larger pore diameter SBA-15 supports may facilitate the diffusion of bulky molecules as that of the asphaltenes present in the heavy petroleum fractions, making it a potential catalyst support for hydrotreating operations. Considering the very sour nature of Canada’s bitumen with high sulfur contents in the range of 2-6 wt %, the appreciably high sulfur contents particularly present in Athabasca derived heavy gas oils (about 4 wt % sulfur), the rising demand for cleaner fuels, and also the increasing stringency on environmental standards, the need for novel and improved hydrotreating catalysts cannot be overemphasized. By varying the molar ratio of hexane to ammonium fluoride, the pore channels of SBA-15 could be varied. Controlling the pore diameter of these supports via micelle swelling facilitated the production of larger pore diameter SBA-15-supported catalysts. In this project, four mesoporous silica SBA-15 catalyst supports with pore diameters in the range of 5-20 nm were synthesized in the preliminary phase using hexane as the micelle swelling agent and subsequently utilized for the loading of 2 wt.% Fe and 15 wt.% W catalyst metals, respectively. The hexagonal mesoscopic structure of these materials were characterized using powder small-angle X-ray scattering (SAXS), N₂ adsorption-desorption isotherms, TEM and SEM images. Powder XRD analysis evidenced inhomogeneous metal dispersion on the largest pore diameter catalyst. An optimum pore diameter of 10 nm was found for Cat-B and subsequently used to obtain the optimum Fe and W loadings required to achieve the best hydrodesulfurization (HDS) and hydrodenitrogenation (HDN) activities. The optimum catalyst was found to be Cat-H with metal loadings of 3 wt.% Fe and 30 wt.% W. At these loadings and temperatures of 375°C, 388°C, and 400°C, HDS activities of 53.4%, 64.1%, and 73.3% with corresponding HDN activities of 21.9%, 26.2%, and 38.3%, respectively, were recorded. Catalytic performance evaluations conducted on equal mass loading using a reference commercial ɣ-Al₂O₃-supported FeW catalyst offered HDS activities of 69.3%, 80.4%, and 89.1%, with corresponding HDN activities of 16.4%, 32.4%, and 49.3% at the same temperatures studied. However, no significant changes in HDS and HDN activities were observed for similar evaluations on volume percent metals loading basis. Kinetic studies performed with the optimum FeW/SBA-15 catalyst suggested activation energies of 147.2 and 150.6 kJ/mol for HDS and HDN, respectively, by the Langmuir-Hinshelwood’s model. Similar results were predicted by the Power Law and Multi-parameter models for HDS (129.6 and 126.7 kJ/mol, respectively), which does not conclusively make the latter model clearly stand out as the best. Data fitting by the Power Law suggested reaction orders of 2 and 1.5 for HDS and HDN, which seem to be consistent for the hydrotreatment of heavy gas oil. Finally, a long-term deactivation study spanning a period of 60 days time-on-stream showed the optimum catalyst to be stable under hydrotreating experiments conducted in a downward flow micro-trickle bed reactor at temperature, pressure, liquid hourly space velocity (LHSV), and gas/oil ratio of 375–400°C, 8.8 MPa, 1h⁻¹, and 600 mL/mL (at STP), 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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".