Oxidative Desulfurization of Tire Pyrolysis Oil over Molybdenum Heteropolyacid Loaded Mesoporous Catalysts
Bibliographic record
Abstract
Pyrolysis oil derived from waste tires consists of sulfur content in the range of 7000 to 9000 ppm. For use in diesel engines, its sulfur content must be lowered to 10 to 15 ppm. Though conventional hydrodesulfurization is suitable for the removal of sulfur from tire pyrolysis oil, its high cost provides an avenue for alternative desulfurization technologies to be explored. In this study, oxidative desulfurization (ODS), a low-cost technology, was explored for the desulfurization of tire pyrolysis oil. Two categories of titanium-incorporated mesoporous supports with 20 wt% loaded heteropoly molybdic acid catalyst (HPMo/Ti-Al2O3 and HPMo/Ti-TUD-1) were developed and tested for ODS of tire pyrolysis oil at mild process conditions. Catalysts were characterized by X-ray diffraction, BET-N2 physisorption, and X-ray photoelectron spectroscopy (XPS). The incorporation of Ti into Al2O3 and TUD-1 frameworks was confirmed by XPS. The surface acidity of catalysts was studied by the temperature-programmed desorption of NH3 and pyridine FTIR analyses. HPMo/Ti-Al2O3 and HPMo/Ti-TUD-1 catalysts contained both Lewis and Brønsted acid sites. The presence of titanium in catalysts was found to promote the ODS activity of phosphomolybdic acid. The Ti-TUD-1-supported catalysts performed better than the Ti-Al2O3-supported catalysts for the ODS of tire pyrolysis oil. Hydrogen peroxide and cumene peroxide were found to be better oxidants than tert-butyl hydroperoxide for oxidizing sulfur compounds of tire pyrolysis oil. Process parameter optimization by the design of experiments was conducted with an optimal catalyst along with the catalyst regeneration study. An ANOVA statistical analysis demonstrated that the oxidant/sulfur and catalyst/oil ratios were more significant than the reaction temperature for the ODS of tire pyrolysis oil. It followed the pseudo-first-order kinetics over HPMo/Ti-TUD-1.
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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) | 0.001 | 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".