Abstract：Prediction method for two-phase flow regime and its parameter based on particle swarm optimization extreme learning machine (PSO-ELM) and electrical capacitance tomography is presented. Firstly, the weights of extreme learning machine are optimized using particle swarm optimization algorithm, and then particle swarm optimization extreme learning machine algorithm is adopted to identify four typical oil-gas flow regimes. Secondly, the parameters of the four flow regimes are predicted by particle swarm optimization extreme learning machine algorithm. Finally, simulation experiments are carried out and the results show that particle swarm optimization extreme learning machine algorithm needs less hidden layer nodes and has higher accuracy for flow regime identification compared with extreme learning machine algorithm. The correct identification rate is 100%, and the maximum relative error for the four flow regimes is 5.24%.
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2020, Vol. 41 
