A Rational Methodology for Determination of Service Life for a Delayed Coker Drum
Bibliographic record
Abstract
Delayed unit coker drums operate in a severe service environment that precludes long term reliability due to excessive shell bulging and cracking of shell joint and shell to skirt welds. Thermal fatigue is recognized as the leading damage mechanism and past work has provided an idealized description of the thermo-mechanical mechanism via local hot and cold spot formation to quantify a lower bound life estimate for shell weld failure. The present work extends this idealized thermo-mechanical damage model by evaluating actual field data to determine a potential upper bound life estimate. This assessment also provides insight into practical techniques for equipment operators to identify design and operational opportunities to extend the service life of coke drums for their specific service environments. A modern trend of specifying higher chromium and molybdenum alloy content for drum shell material in order to improve low cycle fatigue strength is seen to be problematic; rather, the use of lower alloy materials that are generally described as fatigue tough materials are better suited for the high strain-low cycle fatigue service environment of coke drums. Materials such as SA 204 C (C – ½ Mo) and SA 302 B (C – Mn – ½ Mo) or SA 302 C (C – Mn – ½ Mo – ½ Ni) are shown to be better candidates for construction in lieu of low chromium alloy steel materials such as SA 387 grades P11 (1¼ Cr – ½ Mo), P12 (1 Cr – ½ Mo), P22 (2¼ Cr – 1 Mo) and P21 (3 Cr – 1 Mo).
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".