Extractive desulfurization of fuel oils using ionic liquids
Bibliographic record
Abstract
The sulphur content of transportation fuels must be reduced in high-sulphur crude oil by desulfurization. Traditionally, desulfurization methods have required harsh reaction conditions and are not very effective at removing refractory sulfur compounds such as benzothiophene (BT), dibenzothiophene (DBT) and 4,6-dimethyldibenzothiophene (4,6-DMDBT). Alternative methods, such as ionic liquid (IL)-mediated desulfurization, are both effective and environmentally friendly. Isolants ideal for desulfurization are required to be recyclable, insoluble in oil, selective for compounds containing sulphur, and eco-friendly. These properties are offered by ILs based on pyridinium. Therefore, the primary objectives of this thesis were to: (1) investigate the properties of N-butyl-pyridinium tetrafluoroborate ([BPy][BF₄]) and N-carboxymethyl pyridinium hydrogen sulfate ([CH₂COOHPy][HSO₄]); (2) understand the effects of reaction parameters (temperature, volume ratio, oxidant dosage, quantities of sulphur compound extracted, etc.) on desulfurization efficiency; (3) clarify the interactions between ILs and sulphur compounds; and (4) investigate the recycling and regeneration of ILs. Experimental results showed that the desulfurization efficiency of [BPy][BF₄] increased with temperature and oxidant dosage and declined with IL to fuel volume ratio. It was observed that at 30ﾟC, 1:1 ration of IL to model fuel [BPy][BF₄] could remove up to 79% of DBT in 80 min in the presence of oxidant H₂O₂. [CH₂COOHPy] [HSO₄] was found to be more effective in desulfurization, capable of removing up to 99.9% of DBT in the presence of oxidant H₂O₂ within 40 min at 25ﾟC, 1:1 ratio of IL to model fuel. The recycled [CH₂COOHPy][HSO₄] marginally lost effectiveness after 8 recycles. It was also found that the effectiveness of both ILs was lower in real diesel compared to model fuels. Computational density functional theory-based structural analysis revealed that there were two types of possible π-π interactions between [BPy] [BF₄] and DBT/DBTO₂, resulting in the formation of complexes with different geometries. [CH₂COOHPy][HSO₄] also exhibits similar potential π−π interactions with DBT/DBTO₂. Moreover, both ILs undergo the same oxidative mechanism of desulfurization, as they involve π-π interactions and hydrogen bonds.
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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".