Ozonation/extraction coupled non‐catalytic desulphurization of <scp>VGO</scp> /natural gas condensate
Bibliographic record
Abstract
Abstract The desulphurization of hydrocarbon fuels with high sulphur content and a wide variety of sulphur‐containing compounds brings significant challenges. In the present work, the non‐catalytic desulphurization of vacuum gas oil (VGO) and natural gas condensate mixed fuel has been investigated. In this regard, ozone was employed as the oxidant in a bubble column gas–liquid contactor, and an extraction step was further incorporated. The effects of the oxidation reactor scheme, ozonation time, and extraction with different solvents, applying pre‐ and post‐extraction strategies (i.e., ozonation/extraction/extraction and extraction/ozonation/extraction), have been studied. It was found that pre‐extraction of mixed fuel increases the oxidative desulphurization (ODS) efficiency due to the removal of light sulphur‐containing compounds (SCCs). N‐methyl‐2‐pyrrolidone (NMP) has been found to be the most effective extraction agent, removing 44% of sulphur by pure extraction and 77.4% by ozonation + extraction. However, in terms of fuel loss, dimethylformamide (DMF) performs better than NMP, and it has been shown to be the most appropriate solvent for achieving a 90% desulphurization ratio. Finally, it was found that the applied procedure could effectively remove the wide range of SCCs, especially refractive components such as benzothiophene (BT) and dibenzothiophene (DBT).
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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 | 0.001 |
| 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".