Challenges of oxidative/extractive desulphurization of heavy fuel oil
Bibliographic record
Abstract
Abstract Oxidative desulphurization (ODS) of heavy fuel oil (HFO) has some challenges such as gum formation and a high level of waste hydrocarbons. Simple calculations show that, assuming dibenzothiophene (DBT) as a representative component of sulphur‐containing components in the cut, about 20% of hydrocarbons are lost within the extraction process. An experimental investigation has been conducted in a three‐neck glass flask with a mechanical stirrer to obtain more insight about the gum formation during ODS of HFO. The gum formation process was investigated in diluted forms of fuel oil. It was observed that the fuel oil converted to the gum increases by decreasing the hydrogen/carbon (H/C) content of the diluting solvents. The main reason for gum formation during ODS is some polymer formation reactions induced by peroxide radicals. At an oxidant to sulphur ratio (O/S) ratio of 0.25, no gum was formed, while at O/S = 5.0, all of the fuel oil was converted to gum. Finally, a simple and efficient extractive desulphurization procedure has been proposed as an alternative method. At the best condition, about 84% desulphurization was obtained with dimethylformamide (DMF) solvent extraction of HFO. It can be assumed as a potential method to use mild mixing conditions with low contact time for extractive desulphurization of HFO on an industrial scale.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".