Testing of an Oil-on-Water Sensing Technology for Detecting Pipeline Leaks in Remote Locations Subject to Freezing Conditions
Bibliographic record
Abstract
This paper presents the results of the testing of an oil-on-water leak detection technology for isolated locations without power or communications infrastructure. A special attention was paid to the ability of the sensors to detect hydrocarbon leaks under freezing conditions, with thick ice formed on the surface of the water. A viable solution for remote locations and large water crossings needs ultra low-power solution and/or cyclic operation. The technology evaluated was a fully passive impedance polymer-absorption sensor (PAS) featuring “zero-power” consumption. This technology also provides an additional advantage, “an event memory”, and is perfectly suitable for cyclic operation for detecting moving oil stains. In October 2017 three polymer-absorption sensors of different lengths were placed in outdoor location in Ontario, Canada for long-term testing of reliability in freezing conditions. The sensors were connected to cellular modem for generating alerts. Another battery of three sensors of same lengths was installed in outdoor testing facility near Ottawa, ON, Canada and connected to real-time data acquisition equipment. A preliminary series of leak tests performed in October/November 2017 confirmed the initial assumptions of excellent sensitivity of the hydrocarbon oil-on-water detection based on polymer absorption. The average power consumption of the sensor excitation and its measurement frontend during the first two months of testing were found to be extremely low, a fraction of the power needed for the wireless modem itself. The leak tests were extended to oil under ice detection performed with 5 North-American crude oils and with 3 refined products from Mid-December 2017 to Mid-February 2018. The sensitivity, the sensor excitation/measurement front end power consumption, and the reliability of the sensors were assessed at freezing temperatures, with thickness of the ice comprised between 80 and 100 mm. The paper also presents the availability of stand-alone communication equipment suitable for integrating oil-on-water sensors, as well the energy harvesting or energy storage technologies for different climatic conditions.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".