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Record W2899778574 · doi:10.1115/ipc2018-78624

Testing of an Oil-on-Water Sensing Technology for Detecting Pipeline Leaks in Remote Locations Subject to Freezing Conditions

2018· article· en· W2899778574 on OpenAlexaboutno aff
Stefan Balatchev

Bibliographic record

VenueVolume 3: Operations, Monitoring, and Maintenance; Materials and Joining · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicWater Quality Monitoring Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsLeakPipeline transportAbsorption (acoustics)Reliability (semiconductor)Computer scienceEnvironmental sciencePower (physics)Petroleum engineeringElectrical engineeringEngineeringMaterials scienceEnvironmental engineering

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.098
Threshold uncertainty score0.690

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.031
GPT teacher head0.284
Teacher spread0.253 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2018
Admission routes1
Has abstractyes

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