Proposed Test Protocol for Evaluating Internal Coating Systems for Railcar Sour Oil Applications
Bibliographic record
Abstract
Abstract Crude oil is usually transported by pipelines from the production areas to refineries. However, in North America, particularly Canada, a significant amount of crude oil is transported via specially designed railcars. The internals of the railcars are typically coated with a high-quality coating system to prevent corrosion. Still, coating failures do occur due to the unique cyclic operating service conditions of the railcars. Thus, elevated temperatures are used to reduce fluid viscosity with a resultant increase in the vapor pressure while the loading of the oil in the railcars. Once loaded, the crude is transported under ambient temperature and pressure until the railcars reach their destination. During off-loading, elevated temperatures are again used for reducing the viscosity of the oil. After emptying the contents, the railcars are allowed to return to ambient conditions and sent back to the production area to repeat the loading, transportation, unloading cycle. Currently, there is no established test protocol to evaluate candidate coating systems for railcar internals under simulated operating conditions. Testing is usually done under a single set of operating conditions of loading or unloading and does not include the synergistic effect of the different temperature variations. This paper presents a detailed test protocol for evaluating candidate coating systems for railcar internals. The proposed test protocol was designed to simulate the expected service conditions of railcars using a modified NACE TM0185-2006 test procedure, “Evaluation of Internal Plastic Coatings for Corrosion Control of Tubular Goods by Autoclave Testing”. The modification includes the cyclic service condition of railcars – fuel loading, loaded transportation, off-loading, dry heat and empty transportation.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".