›› 2012, Vol. 20 ›› Issue (2): 346-351.

• SELECTED PAPERS FROM THE 6TH WORLD CONGRESS ON INDUSTRIAL PROCESS TOMOGRA-PHY (WCIPT6) • Previous Articles     Next Articles

The velocity measurement of two-phase flow based on particle swarm optimization algorithm and nonlinear blind source separation

WU Xin-Jie1, CUI Chun-Yang1, HU Sheng1, LI Zhi-Hong2, WU Cheng-Dong3   

  1. 1 College of Physics, Liaoning University, Shenyang 110036, China 2 Key Laboratory of Condition Monitoring and Control for Power Plant Equipment, Ministry of Education, North China Electric Power University, Beijing 102206, China 3 School of Information Science and Engineering, Northeastern University, Shenyang 110006, China
  • Received:2011-12-14 Online:2012-01-10 Published:2012-04-28

基于粒子群优化算法和非线性盲源信号分离测量两相流速度

吴新杰1, 崔春阳1, 胡晟1, 李志宏2, 吴成东3   

  1. 1 College of Physics, Liaoning University, Shenyang 110036, China 2 Key Laboratory of Condition Monitoring and Control for Power Plant Equipment, Ministry of Education, North China Electric Power University, Beijing 102206, China 3 School of Information Science and Engineering, Northeastern University, Shenyang 110006, China

Abstract: In order to overcome the disturbance of noise, this paper presented a method to measure two-phase flow velocity using particle swarm optimization algorithm, nonlinear blind source separation and cross correlation method. Because of the nonlinear relationship between the output signals of capacitance sensors and fluid in pipeline, nonlinear blind source separation is applied. In nonlinear blind source separation, the odd polynomials of higher order are used to fit the nonlinear transformation function, and the mutual information of separation signals is used as the evaluation function. Then the parameters of polynomial and linear separation matrix can be estimated by mutual information of separation signals and particle swarm optimization algorithm, thus the source signals can be separated from the mixed signals. The two-phase flow signals with noise which are obtained from upstream and downstream sensors are respectively processed by nonlinear blind source separation method so that the noise can be effectively removed. Therefore, based on these noise-suppressed signals, the distinct curves of cross correlation function and the transit times are obtained, and then the velocities of two-phase flow can be accurately calculated. Finally, the simulation experimental results are given. The results have proved that this method can meet the meas-urement requirements of two-phase flow velocity.

Key words: particle swarm optimization, nonlinear blind source separation, velocity, cross correlation method

摘要: In order to overcome the disturbance of noise, this paper presented a method to measure two-phase flow velocity using particle swarm optimization algorithm, nonlinear blind source separation and cross correlation method. Because of the nonlinear relationship between the output signals of capacitance sensors and fluid in pipeline, nonlinear blind source separation is applied. In nonlinear blind source separation, the odd polynomials of higher order are used to fit the nonlinear transformation function, and the mutual information of separation signals is used as the evaluation function. Then the parameters of polynomial and linear separation matrix can be estimated by mutual information of separation signals and particle swarm optimization algorithm, thus the source signals can be separated from the mixed signals. The two-phase flow signals with noise which are obtained from upstream and downstream sensors are respectively processed by nonlinear blind source separation method so that the noise can be effectively removed. Therefore, based on these noise-suppressed signals, the distinct curves of cross correlation function and the transit times are obtained, and then the velocities of two-phase flow can be accurately calculated. Finally, the simulation experimental results are given. The results have proved that this method can meet the meas-urement requirements of two-phase flow velocity.

关键词: particle swarm optimization, nonlinear blind source separation, velocity, cross correlation method