Catalytic performances of NiMo/Zr-SBA-15 catalysts for the hydrotreating of bitumen derived heavy gas oil
Notice bibliographique
Résumé
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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Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».