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Record W2900233227 · doi:10.1115/ipc2018-78224

Microwave Sensor Array for Corrosion Prediction in Steel Tank Bottoms

2018· article· en· W2900233227 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicRFID technology advancements
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsCorrosionStorage tankCathodic protectionRoofCoatingCorrosion monitoringVisual inspectionEngineeringMarine engineeringMaterials scienceStructural engineeringComputer scienceMechanical engineeringComposite materialElectrode

Abstract

fetched live from OpenAlex

Throughout North America there are many crude oil storage tank facilities — also called terminals — serving as hubs, transfer points and storage. Safety precautions such as pre-service integrity testing, cathodic protection, primary and secondary containment measures, and grounding techniques have been utilized to assure safety as a top priority. These tanks undergo an in-service API 653 external inspection at least every 5 years, and are taken out of service to undergo an API 653 internal/external inspection at least every 30 years [1], [2], [3]. For these aboveground storage tanks, the bottom plate is the most vulnerable area to corrosion [4] and is also the most challenge area to inspect visually. Both sides (product-side and soil-side) of the tank bottom plate are prone to high rates of corrosion in comparison to other components such as the roof and shell [5]. Corrosion generally starts with coating defects such as air or water ingress to underling layers and exposing the steel to uncontrolled environmental factors. Internal inspection can be performed using ultrasonic measures to calculate the sheet thickness, however, external inspection is impossible without having access to the tank bottom. This paper will introduce a novel inspection method for external monitoring of the surface of the tank bottom plate in real-time. The proposed technique proactively approaches the problem by predicting the corrosion before it occurs. In this technique an array of microwave-based sensors operating at ISM band (2.57 GHz) are introduced for defect prediction. The array is composed of equally-distant and identical microwave spiral ring resonators (SRR) [6] that are electromagnetically coupled to a transmission line. All resonances created by the array elements merge in one band-stop frequency response with very high isolation. Once the sensors’ environment is altered by any defects such as an air breach, liquid ingress [7] or corrosion initiated, a resonance shift will occur indicating coating risks. To prove the concept, an initial prototype for small tanks of 3–5 ft. diameter is investigated. Two-port system data illustrates that in case of a coating defect, the frequency profile accordingly changes and provides a signature. The obtained data is used to predict possible corrosion in timely manner. The proposed sensor array enables external monitoring of tank bottoms surface where visual inspection is impossible while the tank is in-service.

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.

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)0.000
metaresearch head score (Gemma)0.000
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: none
Teacher disagreement score0.573
Threshold uncertainty score0.360

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.009
GPT teacher head0.224
Teacher spread0.215 · 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

Quick stats

Citations7
Published2018
Admission routes1
Has abstractyes

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