An Experimental and Simulation Study for Comparison of the Sensitivity of Different Non-Destructive Capacitive Sensors in a Stratified Two-Phase Flow Regime

Shahsavari, Mohammad Hossein; Veisi, Aryan; Roshani, Gholam Hossein; Eftekhari-Zadeh, Ehsan; Nazemi, Ehsan

doi:10.3390/electronics12061284

Cited by 8 publications

(5 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Fouladi et al utilized an artificial neural network to predict void fractions in different fluids [54]. Additionally, Shahsavari et al focused on the effect of different orientations in stratified two-phase flows [55].…”

Section: Void Fraction Measurement Techniquesmentioning

confidence: 99%

A Novel Smart Optimized Capacitance-Based Sensor for Annular Two-Phase Flow Metering With High Sensitivity

et al. 2023

Self Cite

View full text Add to dashboard Cite

Accurately determining phase fractions in two-phase flows is among the most significant issues in Industries related to the production and processing of petroleum and petrochemicals. There are numerous sensor types and configurations for measuring the void fraction. In this respect, the capacitance-based sensor is commonly recognized as one of the most precise and widely utilized sensors. In this essay, COMSOL Multiphysics software, which has been benchmarked, was used for simulations with various electrode architectures for measuring oil-air two-phase flow in an annular pattern. The initial electrode configurations were helix, double ring, concave and parallel plates. Finite element analysis utilizing COMSOL Multiphysics was executed to compare the electrode configurations. Results exposed disparate sensitivities for different electrode geometries. To get better results, a new electrode geometry called arrow-shaped which was optimized with Artificial intelligence (AI) was proposed and compared with the others. The sensor responses presented demonstrated that the proposed arrow-shaped capacitance-based sensor had 21% higher sensitivity than the best-performing sensor among four other existing sensor designs, including concave, helix, double ring, and parallel plates. These results indicate the superior performance of the arrow-shaped sensor and its potential for use in high-sensitivity applications.

show abstract

Section: Void Fraction Measurement Techniquesmentioning

confidence: 99%

A Novel Smart Optimized Capacitance-Based Sensor for Annular Two-Phase Flow Metering With High Sensitivity

et al. 2023

Self Cite

View full text Add to dashboard Cite

show abstract

“…This device was based on the single-wire capacitance probe and was able to measure the average and the local gas percentage by measuring the water level at various positions of the pipe. Artificial intelligence (AI) is one of the most common methods in the industrial sector [31]. ANN is a capable tool that has been widely utilized in various fields, such as electrical engineering and control engineering [32][33][34][35][36][37][38][39][40][41][42][43][44][45].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2023

Self Cite

View full text Add to dashboard Cite

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.

show abstract

“…The choice of electrode arrangements, such as concave, ring, or helix, depends on the fluid properties during transportation through the tube. Previous investigations have primarily focused on two-phase flows within pipes: stratified, annular, and homogeneous flows [ 12 , 13 , 14 , 15 ].…”

Section: Introductionmentioning

confidence: 99%

Intelligent Measuring of the Volume Fraction Considering Temperature Changes and Independent Pressure Variations for a Two-Phase Homogeneous Fluid Using an 8-Electrode Sensor and an ANN

Qaisi,

Fouladinia,

Mayet

et al. 2023

Sensors

Self Cite

View full text Add to dashboard Cite

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.

show abstract

An Experimental and Simulation Study for Comparison of the Sensitivity of Different Non-Destructive Capacitive Sensors in a Stratified Two-Phase Flow Regime

Cited by 8 publications

References 31 publications

A Novel Smart Optimized Capacitance-Based Sensor for Annular Two-Phase Flow Metering With High Sensitivity

A Novel Smart Optimized Capacitance-Based Sensor for Annular Two-Phase Flow Metering With High Sensitivity

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

Intelligent Measuring of the Volume Fraction Considering Temperature Changes and Independent Pressure Variations for a Two-Phase Homogeneous Fluid Using an 8-Electrode Sensor and an ANN

Contact Info

Product

Resources

About