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Record W2783514660 · doi:10.1021/acs.iecr.7b04136

Role of Presulfidation and H<sub>2</sub>S Cofeeding on Carbon Formation on SS304 Alloy during the Ethane–Steam Cracking Process at 700 °C

2018· article· en· W2783514660 on OpenAlexafffund
Anand Singh, Scott Paulson, Hany Farag, Viola Birss, Venkataraman Thangadurai

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2018
Typearticle
Languageen
FieldEngineering
TopicMetallurgical Processes and Thermodynamics
Canadian institutionsNova Chemicals (Canada)University of Calgary
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCrackingCarbon fibersMaterials scienceAlloySpallCatalysisPyrolytic carbonChemical engineeringFluid catalytic crackingMetallurgyChemistryComposite materialPyrolysisOrganic chemistry

Abstract

fetched live from OpenAlex

The influence of presulfidation and H 2 S cofeeding on the carbon formation on SS304 alloy in the ethane–steam cracker was investigated in a laboratory-scale quartz reactor setup. SS304H coupons and SS304L powder samples were exposed to ethane–steam and dry ethane in varying H 2 S content (0–50 ppm), and the SS304 samples were characterized by scanning electron microscopy. This study shows that H 2 S cofeeding decreases catalytic carbon formation; while it increases the pyrolytic carbon formation during ethane–steam cracking. Preoxidation, presulfidation, and addition of steam to ethane feed also reduces the amount of catalytic carbon formed on the SS304H surface in short-term experiments (4 h). Presulfidation and addition of H 2 S to ethane feed significantly influences the shape and size of the carbon formed on the surfaces of investigated metal alloys. Presulfidation and H 2 S cofeeding reduced spalling of the SS304H coupon surface during coking/decoking and thermal cycling.

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)0.001
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 score0.224
Threshold uncertainty score0.752

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.030
GPT teacher head0.271
Teacher spread0.242 · 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

Citations8
Published2018
Admission routes2
Has abstractyes

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