FBG-Based Temperature Sensors for Liquid Identification and Liquid Level Estimation via Random Forest

Pereira, Katiuski; Coimbra, Wagner; Lazaro, Renan C.; Frizera, Anselmo; Marques, Carlos; Leal‐Junior, Arnaldo

doi:10.3390/s21134568

Cited by 30 publications

(4 citation statements)

References 33 publications

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“…In preview works, we presented a method based on machine learning, which used the temperature of fluids to estimate liquid level by using an array of 3 FBGs multiplexed. In the experiment, a glass test tube with a 2.2 cm radius and a height of 22.5 cm presented a temperature gradient maximum of around 2.8 °C, considering the distance of 18.5 cm between the FBGs [ 32 ]. In relation to that, all experiments described in this paper were realized by positioning the FBG at the same fixed point once changes in the FBG position could cause errors in the estimation of heat distribution.…”

Section: Materials and Methodsmentioning

confidence: 99%

FBG-Based Sensor for the Assessment of Heat Transfer Rate of Liquids in a Forced Convective Environment

Lazaro

Frizera

Marques

et al. 2021

Sensors

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The assessment of heat transfer is a complex task, especially for operations in the oil and gas industry, due to the harsh and flammable workspace. In light of the limitations of conventional sensors in harsh environments, this paper presents a fiber Bragg grating (FBG)-based sensor for the assessment of the heat transfer rate (HTR) in different liquids. To better understand the phenomenon of heat distribution, a preliminary analysis is performed by constructing two similar scenarios: those with and without the thermal insulation of a styrofoam box. The results indicate the need for a minimum of thermal power to balance the generated heat with the thermal losses of the setup. In this minimum heat, the behavior of the thermal distribution changes from quadratic to linear. To assess such features, the estimation of the specific heat capacity and the thermal conductivity of water are performed from 3 W to 12 W, in 3 W steps, resulting in a specific heat of 1.144 cal/g °C and thermal conductivity of 0.5682 W/m °C. The calibration and validation of the HTR sensor is performed in a thermostatic bath. The method, based on the temperature slope relative to the time curve, allowed for the measurement of HTR in water and Kryo 51 oil, for different heat insertion configurations. For water, the HTR estimation was 308.782 W, which means an uncertainty of 2.8% with the reference value of the cooling power (300 W). In Kryo 51 oil, the estimated heat absorbed by the oil was 4.38 kW in heating and 718.14 kW in cooling.

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Section: Materials and Methodsmentioning

confidence: 99%

FBG-Based Sensor for the Assessment of Heat Transfer Rate of Liquids in a Forced Convective Environment

Lazaro

Frizera

Marques

et al. 2021

Sensors

Self Cite

View full text Add to dashboard Cite

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“…Other, more elaborated methods take advantage of the reflection/refraction properties of the medium and substance of interest, this is the case in fiber optic sensors [20]. Fiber optic sensors have also been used for detecting liquid-liquid phases but they must come in contact with the liquids [25] or be bundled with other transducers [26]. These principles of operation are also used to physically detect the location of objects and are well established in industrial settings [12].…”

Section: Related Workmentioning

confidence: 99%

Near Infrared Sensor Setup for General Interface Detection in Automatic Liquid-Liquid Extraction Processes

2022

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This work presents a novel sensor setup for the general detection of liquid-liquid interfaces in different mixes of liquids as part of a liquid-liquid extraction device. The sensor setup is applied to a laboratory scale separatory funnel. It uses a near infrared sensor array which receives light going through the liquids inside the funnel, which are illuminated by a light source located on the other side and below the funnel. Light refracts inside the funnel and the liquids and reflects on the interface creating changing patterns in the light intensity measured by the sensor, providing a way of locating the liquid-liquid interface. Liquid mixes with different optical features, from transparent to opaque, emulsion and clean, are used to test whether different types of interfaces produce a distinguishable response on the sensor, allowing to detect interfaces in different situations that can occur as part of an Artificial Intelligence orchestrated battery chemical synthesis process. Emulsion interfaces create a discernible change in the sensor input by lowering the light intensity registered when crossing in front of the sensor making them easier to locate than with other optical techniques. The setup opens the possibility of detecting a liquid-liquid interface as it is forming and can be miniaturized to be attached to laboratory funnels as a manual aid or used with other transparent vessels in automatic solutions like liquid handling robots or pipetting robots.

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“…A current trend in optical fiber sensor development is the use of machine learning approaches for the extension of sensors' capabilities and overall performance [25]. In this regard, supervised approaches are commonly employed for the assessment of different parameters in distributed approaches [26], as well as in soft sensor development using one measured parameter to estimate another [27]. In addition, the use of neural networks, in conjunction with the distributed optical fiber developments, increases the sensor capability in multiparameter sensing, and spatial resolution [28].…”

Section: Introductionmentioning

confidence: 99%

Development and Analysis of Multifeature Approaches in SPR Sensor Development

2023

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This paper presents the development and signal analysis of surface plasmon resonance (SPR)-based sensors in D-shaped polymer optical fibers (POFs). A gold-palladium (Au-Pd) coating was applied to the D-shaped region to obtain the SPR signal in the transmitted spectrum of the POFs, where different samples were fabricated using the same methods and parameters. In this case, the transmitted spectra of three sets of samples were compared, which indicated variations in the SPR signature that can influence the sensors’ application and reproducibility. Then, the intensity of and wavelength shift in the SPR signals were analyzed as a function of the refractive index variation, where it was possible to observe differences in the sensors’ sensitivities and the linearity of the different samples. In this regard, additional features, namely the area below the curve and the peak amplitude of the fast Fourier transform (FFT) applied to the transmitted spectra, were used to enhance the sensors’ accuracy and precision. To verify the use of such additional features in the sensor analysis, an unsupervised approach based on k-means clustering was used considering a single dataset with the results of all the sensors. The results showed clustering with the number of different refractive indices tested, which motivated the use of these features (intensity, wavelength, area and FFT amplitude) in the refractive index assessment. In this context, random forest was the supervised algorithm with the smallest root mean squared error (RMSE) among the algorithms tested, where an RMSE of 0.0057 was obtained considering all the datasets. For the analysis of each sensor (considering the three sets of sensor samples), the mean RMSE using random forest applied to the multifeature approach returned relative errors below 9%, considering the entire tested range of refractive index variation.

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FBG-Based Temperature Sensors for Liquid Identification and Liquid Level Estimation via Random Forest

Cited by 30 publications

References 33 publications

FBG-Based Sensor for the Assessment of Heat Transfer Rate of Liquids in a Forced Convective Environment

FBG-Based Sensor for the Assessment of Heat Transfer Rate of Liquids in a Forced Convective Environment

Near Infrared Sensor Setup for General Interface Detection in Automatic Liquid-Liquid Extraction Processes

Development and Analysis of Multifeature Approaches in SPR Sensor Development

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