基于粒子群优化极限学习机及电容层析成像的两相流流型及其参数预测

doi:10.3969/j.issn.1000-1158.2020.12.07

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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%.

Key words： metrology oil-gas two-phase flow flow regime identification particle swarm extreme learning machine electrical capacitance tomography parameter prediction

Received: 08 April 2019 Published: 08 December 2020

PACS:

TP937

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	ZHANG Li-feng
	ZHU Yan-feng

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ZHANG Li-feng,ZHU Yan-feng. Two-phase Flow Regime and its Parameter Prediction Based on Particle Swarm Optimization Extreme Learning Machine and Electrical Capacitance Tomography[J]. Acta Metrologica Sinica, 2020, 41(12): 1488-1493.

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http://jlxb.china-csm.org:81/Jwk_jlxb/EN/10.3969/j.issn.1000-1158.2020.12.07 OR http://jlxb.china-csm.org:81/Jwk_jlxb/EN/Y2020/V41/I12/1488

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