The Evolution of Bulged Areas in the Cylindrical Section of Coke Drums
Bibliographic record
Abstract
Abstract In recent years the understanding of the relationship between drum damage and bulge sharpness has improved significantly. The authors of this paper developed a new parameter called bulge sharpness and have previously shown the relationship between sharpness and observed damage. Further to this study, the authors have exhaustively studied the evolution of stress cracking (elephant skin) on mid-course bulges and have estimated the likelihood of finding a particular type of surface damage based on the observed sharpness levels. This correlation has led to a proposed scale to categorize stress cracking into three levels: minor, intermediate, and significant. In addition, the progression of bulge sharpness over time was analyzed and it was determined through statistical modeling that bulge sharpness can have a range of rates of change or sharpness growth rates: low, medium, and high. These sharpness growth rates were subsequently studied and their relationship with overall cycle times analyzed. The study also shows that individual coke drums can experience different sharpness growth rates and there can be a distribution of these rates. To determine when repairs should be conducted, coke drum operators must consider the expected operational run. While the random nature of coke drum damage can defy such targets, bulge sharpness growth assessments can be used to better define when repairs should be conducted. Understanding current bulge sharpness levels, year-over-year sharpness growth rates and their distribution, can significantly assist in targeting areas of concern for optimized repair strategies and can also be used to avoid unnecessary repairs.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".