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Record W4294415639 · doi:10.1002/cjce.24638

Investigation of the effect of presulphidation on coke deposition on <scp>25Cr‐35Ni</scp> alloy during ethane steam cracking

2022· article· en· W4294415639 on OpenAlexvenueno aff
Somayeh Mohebi, Ali Darvishi, Shahin Hosseini, Akbar Bolhasani, Soroush Karamian, O. Dehghani, Faraz Khanbolouk

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2022
Typearticle
Languageen
FieldEngineering
TopicHeat transfer and supercritical fluids
Canadian institutionsnot available
Fundersnot available
KeywordsCokeCrackingAlloyMetallurgyMaterials scienceFluid catalytic crackingDeposition (geology)Chemical engineeringComposite material

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.187
Threshold uncertainty score0.428

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.006
GPT teacher head0.176
Teacher spread0.170 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2022
Admission routes1
Has abstractyes

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