Innovative Mitigation Strategies to Address AC Corrosion at Higher Frequencies
Bibliographic record
Abstract
Abstract A 2018 field investigation on an NPS 6, approximately 4 km long, liquid pipeline identified a rectifier as the likely source of the elevated DC and AC current densities, which resulted in AC corrosion anomalies detected during in-line inspections (ILI). Initial findings indicated that the 120 Hz rectifier ripple may have contributed to the accelerated AC corrosion at this location. A literature review identified a gap in the industry with no existing standards that address AC corrosion at these higher frequencies, and very little research on the topic. This resulted in additional field and laboratory investigations to further quantify the risks of higher frequency harmonics on cathodically protected pipelines. Due to the complexity of the system, innovative mitigation approaches were required to mitigate the AC corrosion risk and minimize DC interference risks, while at the same time ensuring adequate CP is maintained along the pipeline. A comprehensive mitigation and monitoring system was installed and commissioned. This paper focuses on the design and implementation of the mitigation system, along with a full assessment of the system commissioning and monitoring data. Lessons learned during the mitigation and commissioning process are intended to provide guidance to operators encountering similar conditions on their pipeline systems.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".