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APPLIED STUDY OF HHT AND NEURAL NETWORKS ON FLOW REGIME IDENTIFICATION FOR OIL-GAS TWO-PHASE FLOW

SUN Bin 1, 2, ZHANG Hongjian1 and YUE Weiting 1(1State Key Laboratory of Industrial Control Technology, Department of Control Science and Engineering, Zhejiang University, Hangzhou 310027,Zhejiang,China; 2Jiamusi University, Jiamusi 154007,Heilongjiang,China)  
For acquiring the flow regime information of two-phase flow,a flow regime identification method using the Hilbert-Huang Transform (HHT) combined with Radial Basis Function neural networks was put forward.In this study,oil-gas two-phase flow in horizontal pipe was taken as the experimental object, differential pressure signals coming from Venturi tube were handled by Hilbert-Huang Transform,and characteristic vector closely associated with the flow regime were obtained.Flow regime was identified by using Radial Basis Function neural networks.While oil flux was in the range of 4.2 to 7.0 m3·h -1 and gas flux was 0 to 30 m3·h -1, this method showed high identification precision for bubble flow, slug flow, churn flow and annular flow et al.The experimental study showed that this method could precisely identify the flow regime and could be used easily.
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