Insulations Ageing & CUI Implications – A Comparison of Lab & Field Samples
Bibliographic record
Abstract
Abstract Corrosion under insulation (CUI) is among the leading damage mechanisms in the hydrocarbon industry and its prediction has reportedly been ambiguous as that relies on many unknown unknowns. Insulation condition is one of the key factors in predicting the CUI risk in a modern-day risk-based inspection (RBI) program. On the other hand, there is not much clarity on predicting the insulation condition as the insulation(s) generally tend to change their physical properties while being subjected to heat and external environmental conditions. Also, there are no established baselines to gauge the extent of aging of insulations for accurate prediction of CUI risk(s). This study attempts to compare the aging behavior/ condition of two commonly used types of insulations namely mineral wool and Calcium silicate (CalSil) as they were subjected to aging in (a) lab setting and (b) field environment. To account for aging behaviors, the leachates were prepared per ASTM C871 followed by Inductively coupled plasma (ICP) spectroscopy and corrosion tests via three different methods namely autoclave immersion tests, ASTM C1617 dripping tests and ASTM G189-07 CUI simulation tests. The corroded steel samples from each test configuration and candidate insulations were further characterized by weight loss methods to establish corrosion rate followed by confocal laser topography and scanning electron microscopy (SEM) to understand the corrosion modes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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; both teacher heads agree on what is shown here.
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".