A Probabilistic Approach to Fired Heater Tube Remaining Life Assessments
Bibliographic record
Abstract
A case history of a Catalytic Reformer fired heater assessment is used to illustrate an alternative approach to quantify some of the uncertainties associated with fired heater tube remaining life estimates. Such uncertainties include temperature and pressure history, tube wall thickness, and existing creep damage. While it is well understood that fired heater tubes are susceptible to creep and corrosion damage, it can be challenging to quantify the margin of error associated with typical analyses. For example, in order to determine estimates of remaining life, it is not unusual for analysts to use overly conservative input parameters (e.g., minimum measured thicknesses, maximum temperatures and pressures). This often results in a “worst case scenario” or “pass/fail” answer, but it does not necessarily provide insight into the effect of uncertainty. In this paper, an alternative method is utilized to predict heater tube lifetimes. The uncertainty associated with the primary input parameters for the API-579 Part 10 Omega creep procedure is characterized statistically by treating them as random variables and random processes. The probability of tubes failing by a specified time is estimated by performing Monte Carlo simulations. This study is of a practical nature, illustrating how the methodology can be used to aid in decision making.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".