Study on Flow Pattern Identification for Airlift Devices Based on Wavelet Packet and Elman Neural Network

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Journal of Vibration and Shock ›› 2016, Vol. 35 ›› Issue (15) : 149-153.

TANG Chuan-lin WANG Zhi-neng HU Dong ZHOU Feng-lin

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Abstract

Airlift device’s flow pattern has great influence on the performance of gas-liquid flow and the lifting system. However, because of the unpredictable gas-liquid speed and the dynamic interface morphology , section gas ratio, the flow pattern in the lift pipe alternate changes and becomes very difficult to identify. Considering about this problem, a flow pattern identification strategy was put forward based on wavelet packet and Elman neural network: the pressure signal’s frequency band energy eigenvalues were extracted by wavelet packet analysis. Then, with the help of Elman neural network identification technology, the frequency band energy was taken as the network’s input variable, the flow pattern was taken as the output variable. After that, a lot of experimental data were used to train the Elman neural network. At last, the flow pattern were effectively identified by this neural network. Experimental results show that, this method’s identification accuracy reached 92.6%, 6.5% higher than the BP network. All of these proved that this method can effectively identify the flow pattern .

Key words

Airlift device; / Flow pattern; / Identification; / Wavelet packet ; / Elman neural network

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TANG Chuan-lin WANG Zhi-neng HU Dong ZHOU Feng-lin. Study on Flow Pattern Identification for Airlift Devices Based on Wavelet Packet and Elman Neural Network[J]. Journal of Vibration and Shock, 2016, 35(15): 149-153

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