基于WOA-LSSVM模型的近临界区CO2物性预测

doi:10.3969/j.issn.1000-1158.2023.05.20

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Abstract Aiming at the problem that the existing CO2 state equation is difficult to accurately predict the physical parameters of CO2 in the near-critical region, the combination model (WOA-LSSVM) of the least squares support vector machine (LSSVM) optimized by the whale optimization algorithm (WOA) was adopted to predict the physical properties of CO2 in the critical region. The prediction results show that compared with the REFPROP software and the PSO-LSSVM model, the WOA-LSSVM model has higher accuracy in predicting the physical properties of CO2 in the near-critical region. Compared with REFPROP software, the WOA-LSSVM model reduces the root mean square error of density and viscosity prediction results from 133.67, 9.33 to 35.61, 1.58, and the average relative error from 31.8%, 30.25% to 6.88%, 3.88%, the coefficient of determination is 0.59, 0.62 to 0.86, 0.83. In addition, the proportion of relative errors below 10% increased from 0% to 69.23% and 92.31%.

Key words： metrology prediction of CO2 physical properties whale optimization algorithm least squares support vector machine near-critical region density viscosity

Received: 21 March 2022 Published: 18 May 2023

PACS:	TB99
	TB973

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	Articles by authors
	HE San
	TANG Kai
	ZHANG Mao-chao
	XUE Ya-wen
	XUE Shi-qi

Cite this article:

HE San,TANG Kai,ZHANG Mao-chao, et al. Prediction of CO2 Physical Properties in Near-Critical Region Based on WOA-LSSVM Model[J]. Acta Metrologica Sinica, 2023, 44(5): 803-809.

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http://jlxb.china-csm.org:81/Jwk_jlxb/EN/10.3969/j.issn.1000-1158.2023.05.20 OR http://jlxb.china-csm.org:81/Jwk_jlxb/EN/Y2023/V44/I5/803

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