Assessment of Microbially Influenced Corrosion Threats Using Molecular Microbiological Methods
Bibliographic record
Abstract
Abstract The presence of solids with nutrients that can support the growth of microbial communities may lead to microbially influenced corrosion (MIC) in carbon steel pipelines. Many factors affect MIC rates, for example, biofilms in pipeline sludges can produce corrosive chemicals that can attack metals, alter local acidity, and create differential aeration and galvanic cells. This paper examined the microbial diversity of sludges obtained from four (4) different locations of a crude oil transmission system. Bacterial activity reaction tests (BARTTM) and molecular microbiological methods (MMM) were used to determine microbial numbers (cells/g of sludge). X-ray diffraction (XRD) and Energy-dispersive X-ray spectroscopy (EDX) analyses were also performed on the sludge to identify key corrosion indicators. Furthermore, pipeline mitigation history along with the operating conditions of the pipelines were collected from the operators to better understand the corrosion mechanisms and help the operators with pipeline integrity management practices. Generally, some degree of correlation in microbial numbers between BARTs and MMM was observed for the pipeline sludges analyzed; i.e. for both test methods, the microbial numbers was higher in sample B followed by sample C. Microbial numbers were higher when MMM was used as compared to BARTs indicating the later may underestimate microbial count. In general bio-treatment reduced microbial numbers with corresponding before and after treatment values of >104 cells/g and 10 cells/g for pipeline A and >106 cells/g and 103 cells/g for pipeline B. When MMM was used all the sludges analyzed presented the six (6) groups of microorganisms believed to pose MIC threat, including archaea and bacteria. Hence while BARTs can be used to assess the diversity of microbial communities already known by the corrosion industry, MMM is recommended to comprehensively assess the susceptibility of pipelines to MIC, i.e. characterize sludges for bacteria, archaea and emerging microbes that may contribute to corrosion.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".