Correlating Coke Drum Profiles With Observed Surface Damage
Bibliographic record
Abstract
The frequency and extent of vessel bulging and cracking being registered in delayed coke drums throughout the global coking industry has accelerated significantly as refinery operators reduce their cycle times. Several theoretical approaches have been developed to identify how a bulged area may lead to drum damage; however, limited information has been presented to match the theoretical predictions with actual surface damage reported by coke drum operators. The results of hundreds of laser scans spanning the last 25 years have been analyzed to correlate vessel bulging with observed surface damage. Specific categorizations of bulge profiles, and the proximity of these to circumferential weld seams (circs), have been calibrated against hundreds of real-world examples of drum damage and failure, including through wall cracking and stress cracking of the cladding, and further associated with the triggers for repair strategies implemented by industry leading refiners. Strong correlations between specific aspects of bulge profiles and the presence of surface damage were found resulting in an assessment tool that can rank and prioritize coke drum distortions on the likelihood of damage, and can serve as a useful guide for planning future coke drum maintenance.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".