Using ANN and Combined Capacitive Sensors to Predict the Void Fraction for a Two-Phase Homogeneous Fluid Independent of the Liquid Phase Type

Measuring the void fraction of different multiphase flows in various fields such as gas, oil, chemical, and petrochemical industries is very important. Various methods exist for this purpose. Among these methods, the capacitive sensor has been widely used. The thing that affects the performance of capacitance sensors is fluid properties. For instance, density, pressure, and temperature can cause vast errors in the measurement of the void fraction. A routine calibration, which is very grueling, is one approach to tackling this issue. In the present investigation, an artificial neural network (ANN) was modeled to measure the gas percentage of a two-phase flow regardless of the liquid phase type and changes, without having to recalibrate. For this goal, a new combined capacitance-based sensor was designed. This combined sensor was simulated with COMSOL Multiphysics software. Five different liquids were simulated: oil, gasoil, gasoline, crude oil, and water. To estimate the gas percentage of a homogeneous two-phase fluid with a distinct type of liquid, data obtained from COMSOL Multiphysics were used as input to train a multilayer perceptron network (MLP). The proposed neural network was modeled in MATLAB software. Using the new and accurate metering system, the proposed MLP model could predict the void fraction with a mean absolute error (MAE) of 4.919.

Automated summary

Measuring the void fraction in different multiphase flows is crucial for industries such as gas, oil, chemical, and petrochemical. Capacitive sensors have been widely used for this purpose, but fluid properties can affect their performance and lead to significant errors in void fraction measurement. In this study, an artificial neural network model was developed to measure the gas percentage in a two-phase flow without the need for recalibration, using a new combined capacitance-based sensor design.