Synergistic Effect of Biocide and Biodispersant to Mitigate Microbiologically Influenced Corrosion in Crude Oil Transmission Pipelines
Bibliographic record
Abstract
Abstract Crude oil transmission pipelines are treated with chemicals to mitigate microbiologically influenced corrosion (MIC). Often such corrosion occurs at over and under bends in pipelines due to the flow regimes that allow the suspended particulate in the crude to settle. The resulting sludge, typically composed of oil, water, and sand, can be microbiologically active. For testing, sludge is collected from the nose of a cleaning pig, but the locations of sample collection are frequently remote. As such, onsite testing of the sludge can be impractical, thus samples usually travel long distances to the laboratory for testing. A study was completed on sludge sampled from a crude oil transmission pipeline in order to (i) observe the impact of biocide, biodispersant, and combined biocide-biodispersant treatment on sludge microbial communities and MIC, and (ii) compare molecular microbiological methods to a frequently used culture-based enumeration technique. Untreated samples, along with samples treated with a matrix of biocide, biodispersant, or a combination of these chemicals, were evaluated using both enumeration tests and 16S rRNA gene sequencing (for microbial community composition identification) to determine treatment efficacy. A subgroup of the samples also underwent corrosion assays (by weight loss) and optical microscopy. These experiments revealed that a combined biocide-biodispersant inhibitor treatment was the most effective for preventing MIC in the laboratory samples tested, and highlights that multiple approaches have value for assessing MIC potential in pipeline sludges.
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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 | 0.000 |
| 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) | 0.000 | 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".