This paper proposed a liquid level measurement and classification system based on a fiber Bragg grating (FBG) temperature sensor array. For the oil classification, the fluids were dichotomized into oil and nonoil, i.e., water and emulsion. Due to the low variability of the classes, the random forest (RF) algorithm was chosen for the classification. Three different fluids, namely water, mineral oil, and silicone oil (Kryo 51), were identified by three FBGs located at 21.5 cm, 10.5 cm, and 3 cm from the bottom. The fluids were heated by a Peltier device placed at the bottom of the beaker and maintained at a temperature of 318.15 K during the entire experiment. The fluid identification by the RF algorithm achieved an accuracy of 100%. An average root mean squared error (RMSE) of 0.2603 cm, with a maximum RMSE lower than 0.4 cm, was obtained in the fluid level measurement also using the RF algorithm. Thus, the proposed method is a feasible tool for fluid identification and level estimation under temperature variation conditions and provides important benefits in practical applications due to its easy assembly and straightforward operation.

This paper presents the development, characterization and analysis of optical fiber integrated sensors systems for structural health monitoring (SHM) in oil tanks. The sensors are based on fiber Bragg gratings (FBGs) embedded in nitrile rubber strips through a vulcanization method. Two sensors arrays (with 5 FBGs each) are fabricated and installed at different regions of the oil tank, namely the metallic side wall and polycarbonate display of the tank. The sensors were characterized as a function of the strain in the nitrile rubber strips, where all analyzed sensors presented a high linearity, since a determination coefficient (R ² ) higher than 0.999 is obtained. Tests with different oil levels inside the tank indicate the feasibility of the sensors in the shape reconstruction and real-time strain monitoring of the structural element, where even the bending in the sidewall of the tank is estimated. Such results indicated a correlation between the wavelength shift of the FBGs and the liquid level inside the tank, which leads to the possibility of measuring liquid level from the tank strain distribution in a non-contact approach using a Random Forest algorithm for data processing. The proposed non-contact and machine learning-enable liquid level assessment resulted in errors of around 1.07%. Thus, the proposed approach indicate the feasibility of a predictive maintenance of oil tank through real-time monitoring of strain and possible structural flaws of the oil tanks in conjunction to non-contact approach of oil processing monitoring that can be useful in data fusion as well as multiparameter approaches for oil monitoring.

This paper presents the development of a thermal measurement system for density, specific heat capacity and thermal conductivity in fluids. Due to toxic and flammable environment often found in oil industry, Fiber Bragg Gratings (FBGs) were used in the sensor development, which consists in a diaphragm-embedded FBG, to estimate density, and a FBG temperature sensor, to estimate specific heat and thermal conductivity. The diaphragm system, with sensitivity of 0.025 nm/kPa, was employed for density measurements in water, diesel and crude oil, becoming a feasible option for density estimation in fluids. The temperature sensor, with sensitivity of 11.5 pm/°C, was used to propose a method to estimate specific heat and thermal conductivity. The method uses the quantity of heat equation to estimate specific heat, then, density and specific heat to estimate thermal conductivity. To validate the method, it was presented three setups based in an acrylic tank, a Becker and a test tube. The tests were performed with water, mineral oil and kryo 51 oil. In addition the proposed system can be integrated in order to realize simultaneous measurements of density, specific heat and thermal conductivity with the same structure.

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.

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 ($\Delta \lambda$), 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.