Investigation of the effect of presulphidation on coke deposition on <scp>25Cr‐35Ni</scp> alloy during ethane steam cracking
Bibliographic record
Abstract
Abstract The process of hydrocarbons cracking is carried out in the presence of heat‐resistant alloys Fe‐Ni‐Cr, which HP40 alloy (25Cr‐35Ni) has the most applications among olefin plants. Since these alloys naturally tend to form coke, the industry has always tried to reduce the coke formation by reducing the catalytic properties of the coils. In this research, the effect of dimethyl disulphide (DMDS) concentration (200–900 ppm) on the HP40 alloy of industrial coils at the presulphidation stage is evaluated. In the presulphidation stage, the alloy surface is in contact with sulphur in the absence of hydrocarbons, and this affects the amount of coke formation in the cracking process. Also, the surface composition and morphology of coke are identified using EDX and SEM analysis. These results showed that at the 500 ppm concentration of DMDS, coke deposition is minimized. Additionally, our findings indicated that coke morphology has not changed under different presulphidation conditions, and coke is still a filament type, but the size of the filaments has changed. Moreover, the study of HP40 composition in both preoxidized and presulphide stages shows that presulphidation reduces the amount of Fe and Ni in the coke layer significantly.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".