Hydrotreatment Followed by Oxidative Desulfurization and Denitrogenation to Attain Low Sulphur and Nitrogen Bitumen Derived Gas Oils
Bibliographic record
Abstract
To lower the sulphur content below 500 ppm and to increase the quality of bitumen derived heavy oil, a combination of hydrotreating followed by oxidative desulfurization (ODS) and oxidative denitrogenation (ODN) is proposed in this work. NiMo/γ-Al2O3 catalyst was synthesized and used to hydrotreat heavy gas oil (HGO) and light gas oil (LGO) at typical operating conditions of 370–390 °C, 9 MPa, 1–1.5 h−1 space velocity and 600:1 H2 to oil ratio. γ-Alumina and alumina-titania supported Mo, P, Mn and W catalysts were synthesized and characterized using X-ray diffractions, N2 adsorption-desorption using Brunauer–Emmett–Teller (BET) method, X-ray photoelectron spectroscopy (XPS) and Fourier transform infrared spectroscopy (FT-IR). All catalysts were tested for the oxidation of sulphur and nitrogen aromatic compounds present in LGO and HGO using tert-butyl hydroperoxide (TBHP) as oxidant. The oxidized sulphur and nitrogen compounds were extracted using adsorption on activated carbon and liquid-liquid extraction using methanol. The determination of oxidation states of each metal using XPS confirmed the structure of metal oxides in the catalyst. Thus, the catalytic activity determined in terms of sulphur and nitrogen removal is related to their physico-chemical properties. In agreement with literature, a simplistic mechanism for the oxidative desulfurization is also presented. Mo was found to be more active in comparison to W. Presence of Ti in the support has shown 8–12% increase in ODS and ODN. The MnPMo/γ-Al2O3-TiO2 catalyst showed the best activity for sulphur and nitrogen removal. The role of Mn and P as promoters to molybdenum was also discussed. Further three-stage ODS and ODN was performed to achieve less than 500 ppm in HGO and LGO. The combination of hydrotreatment, ODS and ODN has resulted in removal of 98.8 wt.% sulphur and 94.7 wt.% nitrogen from HGO and removal of 98.5 wt.% sulphur and 97.8 wt.% nitrogen from LGO.
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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 | 0.000 | 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 | 0.000 | 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 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".