Forensic Investigation of Corrosion under Insulation from Rust Scale Sample
Bibliographic record
Abstract
Abstract Corrosion under insulation (CUI) is among the leading damage mechanisms in oil refining and hydrocarbon production facilities. CUI reportedly drives 40-60 % of the piping-related repairs and constitutes 10% of the overall maintenance spending. Numerous conventional and advanced inspection measures look for the occurrence and severity of CUI. On the other hand, the CUI formation reasons, and kinetics may not be well understood with the common inspection strategies. Like any other type(s) of corrosion, the rust scale samples can provide useful evidence in understanding CUI. With clarity on drivers and kinetics, the root cause analysis and decision making for CUI management can benefit from such information on drivers and kinetics. This article addresses the three different case studies on the forensic investigation of CUI via chemical analysis of rust scale samples. Rust samples from various assets in downstream and upstream facilities were analyzed using x-ray diffraction (XRD) which revealed range of corrosion products such as hematite (Fe2O3), goethite (α-FeOOH), akageneite (β-FeOOH), magnetite (Fe3O4), etc. The study then addresses the kinetics behind these corrosion products and suggests some practical measures for utilizing the forensic information on rust scale(s).
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".