Deep desulfurization of alkylated oil by alumina adsorbents: characteristics and mechanism study
Bibliographic record
Abstract
Deep desulfurization of alkylated oil is the primary problem that has long plagued the petroleum refining industry. In this study, alkaline alumina adsorbent microspheres were synthesized by carbonization–hot oil column pelletization method. The adsorption desulfurization performance of as-synthesized adsorbent and three commercial alumina-based adsorbents were systematically evaluated and compared. The results showed that alkaline alumina adsorbent had the optimal adsorption performance with a saturated adsorption capacity of 8.604 mg/g. Meanwhile, FTIR and sulfur speciation analysis indicated that the alkaline alumina adsorbent could deeply remove various sulfides (methyl mercaptan, dimethyl disulfide, hexacarbon sulfide, dibenzothiophene, etc.) from alkylated oil. Furthermore, the adsorption kinetics study manifested that the adsorption of sulfide was dominated by chemical adsorption, supplemented by physical adsorption, and accompanied by competitive adsorption among different sulfides. In addition, the regeneration experiment showed that nitrogen (90 °C) could realize the stable regeneration of the alkaline alumina adsorbent. To ensure stable regeneration performance in the industry, it is recommended that the alkaline alumina adsorbent be regenerated once with nitrogen at 90 °C. This study will provide theoretical support for the process optimization of deep desulfurization of alkylated oil and contribute to the high-quality production of clean fuels worldwide.
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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".