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| Flow Pattern Recognition of Gas-liquid Two-phase Flow Based on Convolutional Neural Network and Gated Recurrent Unit |
| ZHANG Li-feng,WANG Zhi,WU Si-cheng |
| Department of Automation, North China Electric Power University, Baoding, Hebei 071003, China |
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Abstract The method for identifying the flow pattern of gas-liquid two-phase flow in a vertical pipeline based on convolutional neural network (CNN) and gated recurrent unit (GRU) is presented in this paper. Based on the reconstructed image by the electrical resistance tomography (ERT) system, the discrete cosine transform (DCT) is performed after the filling processing. The difference between the maximum and minimum DCT coefficients is calculated, and a certain frame length data are selected as the network input to identify the flow pattern. The influence of the length of the input sequence on the accuracy of CNN-GRU, CNN and GRU network classification is analyzed, and the optimal input vector dimensions are determined to be 60, 65 and 50. Using experimental data to train and test the CNN-GRU network, and compare it with the GRU and CNN networks, the results show that the CNN-GRU network has the highest classification accuracy, and the average flow pattern recognition accuracy rate can reach 99.40%.
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