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International Society for Industrial Process Tomography

6th World Congress on Industrial Process Tomography

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

Xin-Jie Wu1, Chun-Yang Cui1, Sheng Hu1, Shi Liu2, Cheng-Dong Wu3

1. College of Physics, Liaoning University, Shenyang 110036, China
2. School of energy & power engineering, 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 flow in pipeline, nonlinear blind source separation is used. 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. 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 are separated from 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 mentioned above, so they are pulled out of the noise. According to these signals, the cross correlation function curves are obtained, and the transit times are founded by these curves. Finally the simulation experimental results are given. The results have shown that this method can meet the measurement requirements of two-phase flow velocity.


Keywords particle swarm optimization, nonlinear blind source separation, velocity, cross correlation method


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