Performance of a novel cobalt phthalocyanine sulfonamide‐based catalyst for oxidative desulphurization of gas condensate
Bibliographic record
Abstract
Abstract Sulphur compounds and mercaptans in gas condensate cause corrosion problems, poison catalysts in catalytic processes, and have adverse environmental impact through SOx emission upon combustion. Therefore, it is essential to remove sulphur and mercaptan from gas condensate to standard levels. In this study, the oxidative desulphurization (ODS) process in the presence of a cobalt phthalocyanine sulfonamide‐based catalyst was investigated. The homogeneous catalyst containing 23 wt.% active material was used and optimum operating conditions of temperature, volume ratio of caustic to gas condensate, caustic concentration, and solvent amount were determined to decrease sulphur content to less than 500 ppm and mercaptan to less than 200 ppm. The catalyst consumption in all experiments was kept constant at a minimum level (0.01 g/400 mL gas condensate). Results showed that this catalyst improved the reaction rate in the lower temperature and lower caustic to feed ratio and lower caustic concentration. The maximum sulphur removal was obtained at 5°C and 4 vol.% of caustic to gas condensate ratio at 16.7 wt.% of caustic concentration. Moreover, this catalyst improved sulphur removal from the gas condensate to about 5% compared to caustic sweeting without catalyst. Sulphur compounds had higher solubility in acetone rather than methanol, and sulphur removal by acetone was 96% while it was 91% for methanol. Finally, the economical and acceptable solvent for sulfoxides removal was methanol with 5% volume fraction.
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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".