Qaisi Ramy Mohammed Aiesh, Fouladinia Farhad, Mayet Abdulilah Mohammad, Guerrero John William Grimaldo, Loukil Hassen, Raja M Ramkumar, Muqeet Mohammed Abdul, Eftekhari-Zadeh Ehsan
Department of Electrical and Electronics Engineering, College of Engineering, University of Jeddah, Jeddah 21589, Saudi Arabia.
Faculty of engineering, Rzeszow University of Technology, Powstancow Warszawy 12, 35-959 Rzeszow, Poland.
Sensors (Basel). 2023 Aug 5;23(15):6959. doi: 10.3390/s23156959.
Two-phase fluids are widely utilized in some industries, such as petrochemical, oil, water, and so on. Each phase, liquid and gas, needs to be measured. The measuring of the void fraction is vital in many industries because there are many two-phase fluids with a wide variety of liquids. A number of methods exist for measuring the void fraction, and the most popular is capacitance-based sensors. Aside from being easy to use, the capacitance-based sensor does not need any separation or interruption to measure the void fraction. In addition, in the contemporary era, thanks to Artificial Neural Networks (ANN), measurement methods have become much more accurate. The same can be said for capacitance-based sensors. In this paper, a new metering system utilizing an 8-electrode sensor and a Multilayer Perceptron network (MLP) is presented to predict an air and water volume fractions in a homogeneous fluid. Some characteristics, such as temperature, pressure, etc., can have an impact on the results obtained from the aforementioned sensor. Thus, considering temperature changes, the proposed network predicts the void fraction independent of pressure variations. All simulations were performed using the COMSOL Multiphysics software for temperature changes from 275 to 370 degrees Kelvin. In addition, a range of 1 to 500 Bars, was considered for the pressure. The proposed network has inputs obtained from the mentioned software, along with the temperature. The only output belongs to the predicted void fraction, which has a low MAE equal to 0.38. Thus, based on the obtained result, it can be said that the proposed network precisely measures the amount of the void fraction.
两相流体在一些行业中得到广泛应用，如石油化工、石油、水等行业。液相和气相这两个相都需要进行测量。空隙率的测量在许多行业中至关重要，因为存在许多含有各种液体的两相流体。存在多种测量空隙率的方法，最常用的是基于电容的传感器。基于电容的传感器除了易于使用外，测量空隙率时不需要任何分离或中断操作。此外，在当代，由于人工神经网络（ANN），测量方法变得更加准确。基于电容的传感器也是如此。本文提出了一种利用8电极传感器和多层感知器网络（MLP）的新型计量系统，用于预测均匀流体中的空气和水的体积分数。一些特性，如温度、压力等，可能会对上述传感器获得的结果产生影响。因此，考虑温度变化，所提出的网络能够独立于压力变化预测空隙率。所有模拟均使用COMSOL Multiphysics软件进行，温度范围为275至370开尔文。此外，压力范围考虑为1至500巴。所提出的网络的输入来自上述软件以及温度。唯一的输出是预测的空隙率，其平均绝对误差（MAE）较低，等于0.38。因此，根据获得的结果，可以说所提出的网络能够精确测量空隙率的大小。
