Oxidative Desulfurization of Heavy Gas Oil over a Ti–TUD-1-Supported Keggin-Type Molybdenum Heteropolyacid
Bibliographic record
Abstract
Catalytic oxidative desulfurization (ODS) is emerging as a potential alternative to deep hydroprocessing as a result of its milder operating conditions and no hydrogen requirements. In this study, ODS catalysts based on a mesoporous TUD-1 support were developed to overcome the diffusion limitation of zeolite-based catalysts in oxidizing large-size organosulfur compounds present in real petroleum feedstocks. Different mesoporous oxidation catalysts were formed by substituting Ti in the TUD-1 framework and impregnating Keggin molybdenum heteropolyacid (HPA) on the TUD-1 support. The mesoporosity of TUD-1 and the presence of Ti(IV) and Mo Keggin units in the prepared catalysts were confirmed from the characterization results of X-ray diffraction, X-ray photoelectron spectroscopy, X-ray absorption near edge structure, and Brunauer–Emmett–Teller N 2 surface area analyses. The ODS performance of catalysts was studied using a mild hydrotreated bitumen-derived heavy gas oil feedstock. The HPA-dispersed Ti–TUD-1 catalyst was found to be most active for desulfurizing the heavy gas oil feedstock as a result of a strong synergy effect of Ti and Mo Keggin ions on catalyzing oxygen transfer from an oxidant to a substrate. Oxidants, such as hydrogen peroxide, cumene hydroperoxide, tert -butyl hydroperoxide, and molecular oxygen, were screened in this study. The first two oxidants were better than others and equally efficient. The HPA/Ti–TUD-1 catalyst was found to be suitable for ODS and oxidative denitrogenation (ODN) in both the batch stirred-tank reactor and continuous fixed-bed reactor systems.
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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 | 0.000 | 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 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".