Introducing the Effective Features Using the Particle Swarm Optimization Algorithm to Increase Accuracy in Determining the Volume Percentages of Three-Phase Flows

Abstract

What is presented in this research is an intelligent system for detecting the volume percentage of three-phase fluids passing through oil pipes. The structure of the detection system consists of an X-ray tube, a Pyrex galss pipe, and two sodium iodide detectors. A three-phase fluid of water, gas, and oil has been simulated inside the pipe in two flow regimes, annular and stratified. Different volume percentages from 10 to 80% are considered for each phase. After producing and emitting X-rays from the source and passing through the pipe containing a three-phase fluid, the intensity of photons is recorded by two detectors. The simulation is introduced by a Monte Carlo N-Particle (MCNP) code. After the implementation of all flow regimes in different volume percentages, the signals recorded by the detectors were recorded and labeled. Three frequency characteristics and five wavelet transform characteristics were extracted from the received signals of each detector, which were collected in a total of 16 characteristics from each test. The feature selection system based on the particle swarm optimization (PSO) algorithm was applied to determine the best combination of extracted features. The result was the introduction of seven features as the best features to determine volume percentages. The introduced characteristics were considered as the input of a Multilayer Perceptron (MLP) neural network, whose structure had seven input neurons (selected characteristics) and two output neurons (volume percentage of gas and water). The highest error obtained in determining volume percentages was equal to 0.13 as MSE, a low error compared with previous works. Using the PSO algorithm to select the most optimal features, the current research’s accuracy in determining volume percentages has significantly increased.

Measuring volume fractions of a three-phase flow without separation utilizing an approach based on artificial intelligence and capacitive sensors

“…In capacitance-based sensors, the relative permittivity (also known as the dielectric constant) plays a crucial role in determining the capacitance of the sensor. The relative permittivity of water, oil, and gas is equal to 81, 2.2, and 1, respectively [ 25 ]. Due to the vast difference between their ε r and based on Eq ( 3 ) it is clear that the measured capacity of water is much higher than two other phases, this can be seen in Fig 7 and Table 1 .…”

“…Moreover, a meticulously constructed MLP model was devised with the capability to precisely prognosticate volume fractions within a two-phase annular fluid [ 24 ]. Also, an MLP ANN was modeled [ 25 ] to measure volumetric proportion in a homogeneous fluid regardless of the specific liquid phase composition. It can be seen that a substantial surge in popularity is witnessed in the expansion of ANN as an instrument in the expansive realms of electrical engineering and control engineering.…”

Using Ant Colony Optimization as a Method for Selecting Features to Improve the Accuracy of Measuring the Thickness of Scale in an Intelligent Control System

“…The use of the extracted characteristics as neural network inputs without monitoring and checking them caused the neural networks to not be successful in determining their target parameters with such high accuracy [5,6,57,58]. Recently, researchers investigated the PSO algorithm to introduce effective characteristics to determine the parameters of the oil field and were able to significantly increase the accuracy of the detection systems [59,60]. By comparing the error of the systems presented in research [59,60] with other research, we will understand the importance of using feature selection methods to increase the accuracy of detection systems.…”

“…Recently, researchers investigated the PSO algorithm to introduce effective characteristics to determine the parameters of the oil field and were able to significantly increase the accuracy of the detection systems [59,60]. By comparing the error of the systems presented in research [59,60] with other research, we will understand the importance of using feature selection methods to increase the accuracy of detection systems. For this reason, we decided to test the performance of another feature selection method in order to increase the accuracy of the detection system.…”