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.

实际上，使用静电传感器精确测量灰尘浓度会受到两相流模式的影响。本文收集了气力输送管道中流动的静电传感器信号，并获得了水平管中气固两相流的三种典型流型的静电波动信号。通过结合互补整体经验模式分解（CEEMD）和反向传播（BP）神经网络，提出了一种用于流型识别的算法。该算法可以自适应地确定本征模式函数（IMF）分解的层数和神经网络的输入向量数，从而确保使用最小大小的向量。选定的IMF能量特征作为BP神经网络的输入可以有效地确保获得准确的流型识别率。实验结果表明，本文提出的算法可以保证流模式的识别率达到99％以上，但通过自适应调整，可以保证训练后的BP神经网络输入的大小尽可能的小，并且可以保证算法计算保持在最低水平。