Multi-interface level in oil tanks and applications of optical fiber sensors

Leal‐Junior, Arnaldo; Marques, Carlos; Frizera, Anselmo; Pontes, María José

doi:10.1016/j.yofte.2017.11.006

Cited by 87 publications

(37 citation statements)

References 53 publications

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“…The water-oil separation is an important process in oil industry [1]. Nowadays, oil is the principal energy source of the world [2].…”

Section: Introductionmentioning

confidence: 99%

“…The effective monitoring, control and separation of oil and water layers in production tanks result in economy on oil production costs and reduction of the environmental pollution [3]. A problem in oil classification is to detect emulsion layers, a region between water and oil in which fluids blend homogeneously [1]. The interface of the tank is organized at sludge at the bottom, water, emulsion, oil, foam, and gas at the top of the tank [3].…”

Section: Introductionmentioning

confidence: 99%

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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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“…The water-oil separation is an important process in oil industry [1]. Nowadays, oil is the principal energy source of the world [2].…”

Section: Introductionmentioning

confidence: 99%

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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“…The development of new sensors will benefit from the advantages of FBG in CYTOP fiber reported in this paper. The refractive index of the CYTOP POF at 600 nm region is similar to the refractive index of water at 25 º C [18] and such feature can be employed to detect oil and water layers in crude oil tanks [19]. Furthermore, this polymer is more chemically stable against chemical solutions and has a lower absorption of water than PMMA and other amorphous transparent thermoplastics [20].…”

Section: Disccusionmentioning

confidence: 99%

Fabrication and Characterization of Bragg Grating in CYTOP POF at 600-nm Wavelength

et al. 2018

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“…A fiber Bragg grating (FBG) sensors have flexible characteristics, low cost and are readily available [ 7 ]. Examples of FBG sensor applications include structural health monitoring in civil engineering [ 8 , 9 ], electric power systems [ 10 , 11 ], railways and roadways monitoring [ 12 , 13 ], in oil tanks monitoring and as chemical sensors [ 14 , 15 ], in biomechanics and in medicine [ 16 , 17 ]. FBG sensors can be fabricated using optical fibers made of different materials, such as glass [ 18 ], polymers [ 19 ], or sapphire [ 20 ].…”

Section: Introductionmentioning

confidence: 99%

Demonstration of a Filterless, Multi-Point, and Temperature-Independent Fiber Bragg Grating Dynamical Demodulator Using Pulse-Width Modulation

Rosolem

Argentato

Bassan

et al. 2020

Sensors

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We demonstrated in this work a filterless, multi-point and temperature-independent FBG (fiber Bragg grating) dynamical demodulator using pulse-width-modulation (PWM). In this approach, the FBG interrogation system is composed of a tunable laser and a demodulator that is designed to detect the wavelength shift of the FBG sensor without any optical filter making it very suitable to be used in harsh environments. In this work, we applied the proposed method that uses the PWM technique for FBG sensors placed in high pressure and high-temperature environments. The proposed method was characterized in the laboratory using an FBG sensor modulated in a frequency of 6 Hz, with a 1 kHz sweeping frequency in the wavelength range from 1527 to 1534 nm. Also, the method was evaluated in a field test in an engine of a thermoelectric power plant.

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Multi-interface level in oil tanks and applications of optical fiber sensors

Cited by 87 publications

References 53 publications

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

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

Fabrication and Characterization of Bragg Grating in CYTOP POF at 600-nm Wavelength

Demonstration of a Filterless, Multi-Point, and Temperature-Independent Fiber Bragg Grating Dynamical Demodulator Using Pulse-Width Modulation

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