Evaluation of Inhibitor Consumption by Particulates in Very Light Hydrocarbon Pipelines
Bibliographic record
Abstract
Abstract Bitumen diluents with very low basic sediment and water (< 0.5%) are transported in Enbridge’s pipelines. The dissolved water and oxygen in diluents have the potential to cause corrosion in the pipeline. Due to similarity of the diluent to refined petroleum products such as gasoline, NACE TM-0172 method was carried out to qualify corrosion inhibitors for the pipeline. This method uses clear hydrocarbon product without considering the impact pipelines solids may have on the inhibitor performance. In this work we modify the NACE TM-0172 method to include the effect of solids. The pipeline solids, collected from pig trap of a diluent pipeline, were first thoroughly characterized for their physicochemical properties by several techniques. Their impact on corrosion inhibitor performance was examined by adding solids at various levels to the clear hydrocarbon. Increasing solids concentration was found to reduce the performance of the corrosion inhibitor. This reduced performance was correlated to decreased residual inhibitor concentration in the diluent, which was made possible by developing a sensitive analytical method to measure sub-ppm residual inhibitor concentration. The residual inhibitor method is expected to serve as a key performance indicator for continuous pipeline inhibitor program.
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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 | 0.000 |
| 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) | 0.001 | 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".