Introducing the effective features using the particle swarm optimization algorithm to increase accuracy in determining the volume percentages of three-phase flows

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.

• Autores: Chen, Tzu-Chia; Alizadeh, Mehdi; Albahar, Marwan; Thanoon, Mohammed; Alammari, Abdullah; Grimaldo Guerrero, John William; nazemi, ehsan; Eftekhari-Zadeh, Ehsan
• Tipo de recurso: Article of investigation
• Fecha de publicación: 2023
• Institución: Corporación Universidad de la Costa
• Repositorio: REDICUC - Repositorio CUC
• Idioma: eng
• OAI Identifier: oai:repositorio.cuc.edu.co:11323/10218
• Acceso en línea: https://hdl.handle.net/11323/10218; https://repositorio.cuc.edu.co/
• Palabra clave: Volume fraction; PSO; MLP neural network; Feature extraction; Wavelet; Frequency domain; Artificial intelligence
• Rights: openAccess
• License: Atribución 4.0 Internacional (CC BY 4.0)