A Case Study on the Failure of Fiber Reinforced Epoxy (FRE) Lining on Condensate Storage Tank
Bibliographic record
Abstract
Abstract Protective coatings are used to protect the metal surfaces to hinder their direct interaction with service fluids. In this case study, a broad investigation was performed to determine the reason behind the failure of the FRE (fiber-reinforced epoxy) lining of a condensate storage tank located in the Arabian Gulf region. Visual inspections revealed various coating defects, including checking and crazing marks. Pull-off testing revealed that two out of six readings were not meeting the specifications for adhesion compliance. DFT (dry film thickness) checks revealed several locations, which were either undercoated or over coated beyond the specified limits. SEM (scanning electron microscope) analysis alongside the cross-sections revealed inter-layers delamination. In addition, the primer layer was visible from the surface, which implies the degradation of intermediate and topcoats. Finally, rust stains were visible at certain locations. FTIR (Fourier transform infrared) spectrum of the applied lining system indicated that it bears resemblance with phenoxy resin, which was in fact, incompatible with the condensate service. The failure mechanism leading to the failure of the applied lining is analyzed and discussed in this paper.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 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".