Methodology for Accelerated Microbiologically Influenced Corrosion in under Deposits from Crude Oil Transmission Pipelines
Bibliographic record
Abstract
Abstract The current study presents the progress made in developing a protocol for accelerated microbiologically influenced corrosion (MIC) in under deposits for crude oil transmission pipelines. The methodology incorporates nutrient-rich broths for the cultivation of naturally occurring bacteria obtained from sludge samples and the formation of an active biofilm on the surface of carbon steel metal specimens. The biofilm is then overlain with sludge, exposed to crude oil and an under deposit environment is formed. Using this approach, corrosion rates in excess of 1.5 mm/y have been duplicated with evidence of pitting; more importantly, laboratory testing has shown that these corrosion rates could be decreased with the addition of a biocide and proprietary chemical package injection. Moreover, pilot scale experiments were conducted by forming the biofilm/under deposit environment on the surface of a corrosion probe that was recessed within an oil flow loop. The pilot scale flow loop evaluation showed that the addition of proprietary chemical package alone could dramatically decrease the overall corrosion rate by 83 % within hours and by 95 % in days.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".