Applications of optical fiber sensor technology for prioritized industry in Thailand development strategy: a review

Thaisongkroh, Phairin; Samartkit, Piyawat; Pullteap, Saroj

doi:10.1117/12.2542671

Cited by 1 publication

(1 citation statement)

References 23 publications

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“…Finally, semi-supervised ML algorithms are those where learning is done with supervised and unsupervised inputs, seeking to find grouping and response criteria [9]. The use of OFS in conjunction with ML has been widely employed [10].…”

Section: Introductionmentioning

confidence: 99%

Simulation of FBG Temperature Sensor Array for Oil Identification via Random Forest Classification

Pereira

Lazaro

Coimbra

et al. 2020

7th International Electronic Conference on Sensors and Applications

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Water–oil separation is important in the oil industry, as the incorrect classification of oil can lead to losses in the production and have an environmental impact. This paper proposes the use of fiber Bragg grating (FBG) temperature sensor array to identify the oil in water–emulsion–oil systems, using only the temperature responses for oil classification results in operational and economic benefits. To demonstrate the possibility of using the FBG temperature sensor to classify oil level, the temperature distribution of an oil storage tank, with 2 m height and 0.8 m in diameter, is simulated using thermal distribution models. Then, the temperature effect in a 2 m long FBG array with a different number and distribution of FBGs is simulated using the transfer matrix method. In each case, we extract the wavelength shift (Δλ), total width at half the maximum (FWHM) and the location of the FBG in the fiber. For the oil classification, we dichotomized the fluids into oil and non-oil (water and emulsion). Due to the low separability of the classes, the random forest algorithm was chosen for classification, starting with 200 FBG equidistant sensors and decreasing to 6, with different distributions along the fiber. As expected, the highest accuracy occurs with the 200 FBGs array (96%). However, it was possible to classify the oil with an accuracy of 94.89% with only 8 FBGs, using tests for two proportions (with a significance of 5%); the accuracy of 8 FBGs is the same as of 50 FBGs.

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Section: Introductionmentioning

confidence: 99%