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.

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.