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Record W4414954664 · doi:10.1115/pvp2025-152046

C-½Mo Vs 1Cr-½Mo: A Comparative Study of Coke Drum Shell Materials

2025· article· en· W4414954664 on OpenAlexaff
John Fernando, Enzo Falo, Millar Iverson, Henry Kwok, Simon Yuen, Haixia Guo, Leanne Wong, Luke Chan

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnergy
TopicCoal and Coke Industries Research
Canadian institutionsSuncor Energy (Canada)
Fundersnot available
KeywordsCokeDrumCrackingShell (structure)Base metalGrain sizeDeformation (meteorology)

Abstract

fetched live from OpenAlex

Abstract To mitigate the impact of severe thermal loads during operation, two different design methodologies are typically adopted when selecting base metal materials for coke drum fabrication. The first approach favors the use of certain chromoly steels (i.e. 1¼Cr and higher), prioritizing strength characteristics to improve resistance to bulging. The second approach, which favors carbon steel and C-½Mo, acknowledges the inevitability of bulging and targets materials with lower strength and finer grain size to optimize fracture ductility, allowing larger bulges to develop before cracking occurs. The trade-off between optimizing bulge resistance and cracking resistance is a critical consideration, particularly when other factors, such as the superior reparability of C-½Mo, may influence material selection. This study examines eight coke drums from the same upgrader unit that were designed using “lower strength” base metal materials; six were fabricated using a C-½Mo alloy, two were fabricated using a 1Cr-½Mo alloy. Despite differences in material composition, both sets of drums share many design and operating characteristics, providing an ideal framework for directly comparing the two base metal materials. An extensive experimental testing program was conducted on boat samples extracted from all eight coke drums, along with a review of historical inspection data. Based on the performance metrics outlined in this study, the C-½Mo drums generally outperform their 1Cr-½Mo counterparts.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.378
Threshold uncertainty score0.997

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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.000

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 head0.061
GPT teacher head0.352
Teacher spread0.292 · 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.

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

Citations0
Published2025
Admission routes1
Has abstractyes

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