Intelligent Measurement of Void Fractions in Homogeneous Regime of Two Phase Flows Independent of the Liquid Phase Density Changes

Fractal and Fractional

article

Abdullah M. Iliyasu¹  Farhad Fouladinia³  Ahmed S. Salama⁴  Gholam Hossein Roshani³  Kaoru Hirota² 

[1] Electrical Engineering Department, College of Engineering, Prince Sattam bin Abdulaziz University;School of Computing, Tokyo Institute of Technology;Electrical Engineering Department, Kermanshah University of Technology;Faculty of Engineering and Technology, Future University in Egypt;School of Automation, Beijing Institute of Technology

关键词: artificial intelligence;    capacitance sensor;    homogeneous;    fractional;    two-phase flow;    experimental validation;   

DOI  :  10.3390/fractalfract7020179

学科分类：社会科学、人文和艺术（综合）

来源: mdpi

【 摘 要 】

Determining the amount of void fraction of multiphase flows in pipelines of the oil, chemical and petrochemical industries is one of the most important challenges. Performance of capacitance based two phase flow meters highly depends on the fluid properties. Fluctuation of the liquid phase properties such as density, due to temperature and pressure changes, would cause massive errors in determination of the void fraction. A common approach to fix this problem is periodic recalibration of the system, which is a tedious task. The aim of this study is proposing a method based on artificial intelligence (AI), which offers the advantage of intelligent measuring of the void fraction regardless of the liquid phase changes without the need for recalibration. To train AI, a data set for different liquid phases is required. Although it is possible to obtain the required data from experiments, it is time-consuming and also incorporates its own specific safety laboratory consideration, particularly working with flammable liquids such as gasoline, oil and gasoil. So, COMSOL Multiphysics software was used to model a homogenous regime of two-phase flow with five different liquid phases and void fractions. To validate the simulation geometry, initially an experimental setup including a concave sensor to measure the capacitance by LCR meter for the case that water used as the liquid phase, was established. After validation of the simulated geometry for concave sensor, a ring sensor was also simulated to investigate the best sensor type. It was found that the concave type has a better sensitivity. Therefore, the concave type was used to measure the capacitance for different liquid phases and void fractions inside the pipe. Finally, simulated data were used to train a Multi-Layer Perceptron (MLP) neural network model in MATLAB software. The trained MLP model was able to predict the void fraction independent of the liquid phase density changes with a Mean Absolute Error (MAE) of 1.74.

【 授权许可】

CC BY