Evaluation of a Portable Online 4-Probe Sensor for Simultaneous Monitoring of Corrosion Rate and Sulfate Reducing Bacteria Activity
Bibliographic record
Abstract
Abstract Corrosion is the main component of the operating and maintenance costs of petroleum industry. Microbiologically influenced corrosion (MIC) significantly contributes to the overall corrosion. Sulfate reducing bacteria (SRB) are the primary cause of MIC. A patented 4-probe sensor for simultaneous monitoring of SRB activity and corrosion was demonstrated previously. The online SRB monitoring probe (OnSM) was based on biosensor technology in which sulfide oxidase (SO) was used as the indicating element. The OnSM was previously validated using biogenic sulfide produced by SRB in continuous flow-loop experiments. The sources of the SRB culture were onshore oilfield in Alberta and offshore oilfield in Newfoundland. The OnSM was integrated with conventional electrochemical corrosion rate monitoring technique to produce a 4-probe sensor. This 4-probe sensor monitored both SRB activity and corrosion rate and had four sensor elements: UNS-G10180 carbon steel working electrode (WE), stainless steel counter electrode (CE), saturated calomel electrode (SCE) reference electrode (RE), and SO enzyme electrode (EE). For monitoring SRB activity a combination of EE, CE, and RE (i.e., OnSM probe) was used and for monitoring corrosion rate a combination of WE, CE, and RE was used. The probes in this configuration had different shape and dimensions and were placed in the laboratory test cell as individual probes. Though this configuration worked perfectly in the laboratory, it can not be used in the field. This paper demonstrates a portable 4-probe sensor and compares the results obtained from the portable with those of the sensor with four individual probes.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 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".