Comparative Analysis of Microbiological Testing Technologies Used in the Energy Industry
Bibliographic record
Abstract
Abstract Within the energy industry, there are several technologies used to quantify microbiological contamination of fluids and assets. Some of these technologies can also be used to identify or characterize microorganisms of interest. It is important to understand the scope of detection and limitations of individual assays so that accurate, data-driven decisions can be made. Three fluids varying in chemical composition and origin within the energy sector were tested in this study. Serial dilution for detection of acid producing bacteria (APB) and sulfate reducing bacteria (SRB), activity-reaction tests measuring SRB, an assay quantifying bacterial hydrolases, and adenosine triphosphate (ATP) quantification were compared and assessed against molecular microbiological methods (MMM). Data were collected to determine the ease of use, precision, and comparability of the testing technologies to each other. A kill study using organic biocides evaluated the performance of these tests in quantifying changes in the microbiological populations over time. The testing technologies delivered results on the order of minutes (ATP and enzymatic assays) to days (activity-reaction tests and MMM) to weeks (serial dilution). Comparing data from 1stgeneration ATP and the bacterial hydrolase tests to the data generated by the other technologies proved challenging due to the lack of reference standards and equivocal nature of the raw output from those technologies. A relatively high limit of detection was determined for 1st generation ATP technology in fluids where the bioburden was estimated below 104 cells/mL. Interpretation of results in culture-dependent activity-reaction tests was found to be subjective, requiring users to distinguish between visual indicators to estimate bioburden. This was further confounded when testing fluids for industrial uses that have complex mineral content and turbidity. The choice of culture-dependent technology to enumerate SRB resulted in up to 3-log SRB/mL variance compared to other tests. Variable responses of assayed biomolecules to chemical treatment (e.g., biocide) were notable in the kill study, where the choice of testing technology impacted the interpretation of biocide effectiveness. Accurate evaluation of microbiological contamination is essential to operational decision-making in the energy industry. Understanding the strengths and limitations of different testing technologies ensures optimized chemical treatments, reduced costs, and improved environmental outcomes.
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".