Comparison and correction of CO<sub>2</sub> properties model in critical region

ZHANG Cong; JIANG Jinbo; PENG Xudong; ZHAO Wenjing; LI Jiyun

doi:10.11949/0438-1157.20190184

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Comparison and correction of CO₂ properties model in critical region

更新时间：2026-07-24

- Comparison and correction of CO₂ properties model in critical region
- CIESC Journal Vol. 70, Issue 8, Pages: 3058-3070(2019)
- 作者机构：
  
  浙江工业大学过程装备及其再制造教育部工程研究中心，浙江杭州 310014
- 作者简介：
- 基金信息：
- DOI：10.11949/0438-1157.20190184
  CLC： TH 117.2
- Received：04 March 2019，
  
  Revised：2019-05-22，
  
  Online First：24 July 2026，
  
  Published：05 August 2019
- 稿件说明：
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章聪, 江锦波, 彭旭东, 赵文静, 李纪云. 近临界区CO₂物性预测模型对比与修正[J]. 化工学报, 2019, 70(8): 3058-3070 DOI: 10.11949/0438-1157.20190184.

ZHANG Cong, JIANG Jinbo, PENG Xudong, ZHAO Wenjing, LI Jiyun. Comparison and correction of CO₂ properties model in critical region[J]. CIESC Journal, 2019, 70(8): 3058-3070 DOI: 10.11949/0438-1157.20190184.
章聪, 江锦波, 彭旭东, 赵文静, 李纪云. 近临界区CO₂物性预测模型对比与修正[J]. 化工学报, 2019, 70(8): 3058-3070 DOI: 10.11949/0438-1157.20190184. DOI：

ZHANG Cong, JIANG Jinbo, PENG Xudong, ZHAO Wenjing, LI Jiyun. Comparison and correction of CO₂ properties model in critical region[J]. CIESC Journal, 2019, 70(8): 3058-3070 DOI: 10.11949/0438-1157.20190184. DOI：

摘要

超临界二氧化碳（SCO

）布雷顿循环系统是未来极具潜力的发电能量转换系统，CO

物性表征模型对布雷顿循环系统中动力设备转轴密封和轴承性能的预测精度影响显著。在总结权威文献中不同温度和压力下CO

物性实验测试数据的基础上，对比分析了经典物性查询软件REFPROP软件中CO

密度、黏度和热导率预测模型的预测精度，获得了预测精度最高的物性预测模型及对应临界点附近误差较大的区域，采用人工神经网络算法获得了近临界区预测精度更高的CO

物性预测模型。结果表明：REFPROP软件中的FEK模型、VS1模型和TC1模型分别对CO

的密度、黏度和热导率具有最高的预测精度，不过其在近临界区的物性预测最大和平均误差仍分别达到40%和8%以上，利用神经网络算法所获得的CO

物性预测模型可使近临界点区的物性预测最大和平均误差分别降至30%和4%以下。

Abstract

The supercritical carbon dioxide (SCO

) Brayton cycle system is a promising power generation energy conversion system in the future. The CO

physical property characterization model has a significant impact on the prediction accuracy of the power equipment shaft seal and bearing performance in the Brayton cycle system. On the basis of summarizing the experimental data of CO

physical properties under different temperatures and pressures in authoritative literature

the prediction accuracy of CO

density

viscosity and thermal conductivity prediction model in the classical physical property query software REFPROP software is compared and

analyzed

and the physical prediction model with the highest prediction accuracy is obtained. The artificial neural network algorithm is used to obtain the prediction model of CO

physical property with higher precision in near critical region. The results show that the FEK model

VS1 model and TC1 model in REFPROP software have the highest prediction accuracy for CO

density

viscosity and thermal conductivity

respectively

but the maximum and average error predictions in the near critical region are still more than 40% and 8%

the CO

physical property prediction model obtained by the neural network algorithm can reduce the maximum and average error prediction of the near critical point area to 30% and 4%

respectively.

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Keywords

references

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⁰

Comparison and correction of CO2 properties model in critical region

DOI：10.11949/0438-1157.20190184

摘要

Abstract

关键词

Keywords

references

Comparison and correction of CO₂ properties model in critical region