Digital Analysis of Rod Coupons for NACE Test Method TM0172: “Determining the Corrosive Properties of Petroleum Cargos”
Bibliographic record
Abstract
NACE Test Method TM0172, “Determining the Corrosive Properties of Petroleum Cargoes” [1] is a simple and valuable test to determine whether refined products, such as motor fuels, are sufficiently dosed with inhibitor to prevent corrosion in the event that water enters the product stream. This test is based on optical examination of the area of the coupon affected by corrosion after a specified exposure to a hydrocarbon/water mixture. Letter “grades” from A to E are subjectively determined by the technician performing the test. Experience has shown that if enough inhibitor is present to produce B+ or better results as defined in this standard, general corrosion in flowing pipelines may be controlled. Grade “B+” or better represents a practical absence of corrosion (less than 5% area). For the purposes of confirming a practical absence of corrosion, the subjective evaluation by a technician is adequate. However, as this test may be applied to less refined, uninhibited products where larger areas of corrosion may be observed, the letter grading does not provide discrimination within very wide ranges of corrosion affected area (20–25% of coupon area). Pipeline integrity managers interested in the relative susceptibility to internal corrosion caused by uninhibited hydrocarbons may be interested a higher level of corrosion discrimination and precision. Enbridge Pipelines proposed a digital image analysis technique in 2009, and this technique has been developed, refined, and successfully employed by Alberta Innovates - Technology Futures (AITF). This paper discusses the development of this technique, as well as comparative results using both the original letter grading system and the digital analysis method. Other observations regarding the application of the TM0172 method on a variety of hydrocarbon categories are presented.
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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 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".