Utilizing a Four-Concave Capacitance Sensor and ANN to Forecast Void Fraction in Two-Phase Stratified Flow Independent of Liquid Type

The determination of void fraction in various two-phase flows holds great significance across a range of industries, including gas, oil, chemical, and petrochemical sectors. Scientists have proposed a wide array of methods for measuring void fractions. In comparison to other methods, capacitive-based sensors stand out as a good choice due to their affordability, nondestructively, robustness, and reliability. However, one of the factors that can affect the accuracy of these sensors is changes in the fluid composition. For instance, even a minor alteration in the fluid within the pipe can result in a significant void fraction measurement error. To address this issue, regular calibration is necessary, which can be a laborious task. In this paper, an Artificial Neural Network (ANN) is employed in order to make sensor measurements independent of fluid changes, which allows for more reliable and precise measurements without the need for frequent calibration. Our focus is on studying stratified two-phase flow. In this research, four different combinations of electrodes of a four-concave sensor are utilized as the input of an ANN. As a result, the ANN’s output accurately quantifies the void fraction. COMSOL Multiphysics software is utilized to simulate the behavior and measure the capacitance value of different combinations of this sensor. Additionally, a Multilayer Perceptron (MLP) neural network in MATLAB is designed and implemented, which can forecast the gas percentage within a two-phase fluid containing different liquids, achieving a remarkable mean absolute error of only 0.0031.