LAUSR

Abstract The accurate measurement of dust concentration using electrostatic sensor is serious affected by two-phase flow patterns in practice. In this paper, the electrostatic sensor signals of flow in a pneumatic conveying pipeline were collected, and the electrostatic fluctuation signals of three typical flow patterns of gas–solid two-phase flow in the horizontal pipe were obtained. By combining complementary ensemble empirical mode decomposition (CEEMD) and a back propagation (BP) neural network, an algorithm for flow pattern identification is proposed. This algorithm can adaptively determine the number of layers of the intrinsic mode function (IMF) decomposition and the number of input vectors for the neural network, ensuring the minimum size vector is used. The selected IMF energy feature as the input of the BP neural network can effectively ensure that an accurate flow pattern discrimination rate is obtained. The experimental results show that the algorithm proposed in the paper can guarantee the recognition rate of the flow pattern to reach more than 99%, yet through adaptive adjustment ensure that the size of trained BP neural network input is as small as possible, and the guaranteed algorithm calculation is kept at a minimum.