混合工质临界性质的推算研究

张楠; 陈龙祥; 胡芃

doi:10.11949/0438-1157.20190443

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混合工质临界性质的推算研究

热力学 | 更新时间：2020-06-24

- 混合工质临界性质的推算研究
- Theoretical study on critical properties of 4 kinds of binary systems
- 化工学报 2019年70卷第S2期页码：1-7
- 作者机构：
  
  1.中国科学技术大学热科学和能源工程系，安徽合肥 230027
  2.中国科学院海西研究院泉州装备制造研究所，福建晋江 362200
- 作者简介：
  
  张楠（1994—），男，博士研究生，nanzh@mail.ustc.edu.cn
  胡芃（1974—），男，博士，副教授，hupeng@ustc.edu.cn
- 基金信息：
  
  国家自然科学基金项目(51576187;51706221)
- DOI：10.11949/0438-1157.20190443
  中图分类号： TK 123
- 收稿：2019-04-28，
  
  修回：2019-05-08，
  
  纸质出版：2019-09-05
- 稿件说明：
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张楠, 陈龙祥, 胡芃. 混合工质临界性质的推算研究[J]. 化工学报, 2019, 70(S2): 1-7

ZHANG Nan, CHEN Longxiang, HU Peng. Theoretical study on critical properties of 4 kinds of binary systems[J]. CIESC Journal, 2019, 70(S2): 1-7
张楠, 陈龙祥, 胡芃. 混合工质临界性质的推算研究[J]. 化工学报, 2019, 70(S2): 1-7 DOI： 10.11949/0438-1157.20190443.

ZHANG Nan, CHEN Longxiang, HU Peng. Theoretical study on critical properties of 4 kinds of binary systems[J]. CIESC Journal, 2019, 70(S2): 1-7 DOI： 10.11949/0438-1157.20190443.

摘要

采用五种不同的方法计算了四种不同二元混合工质的临界温度和临界压力，研究对比不同方法在推算二元混合临界性质时的精度。其中Peng-Robinson（PR）方程和Soave-Redlich-Kwong（SRK）方程，两种状态方程结合Heidemann等提出的临界点判据计算得到的临界参数与实验结果吻合较好。两种经验公式，改进的Chueh-Prausnitz（MCP）方法和Redlich-Kister方法，以及径向基函数神经网络（RBFNN）在计算混合工质的临界性质时也都有着较高的计算精度。对于临界温度的计算，PR方程、SRK方程、MCP方程、Redlich-Kister方程以及径向基函数神经网络计算结果的绝对平均偏差的最大值分别为1.82%、1.73%、0.95%、0.17%和0.20%。对于临界压力的计算，通过PR方程、SRK方程、MCP方程、Redlich-Kister方程以及径向基函数神经网络计算的绝对平均偏差的最大值分别为6.07%、5.04%、3.49%、1.90%以及0.67%。

Abstract

Five different methods were used to calculate the critical temperatures and critical pressures of four kinds of binary mixtures

and the accuracy of different methods in estimating critical properties of binary mixtures were studied. It is found that the critical properties calculated by the Peng-Robinson (PR) equation and the Soave-Redlich-Kwong (SRK) equation combined with critical judgement

which was proposed by Heidemann and Khalil

showed a good agreement with experimental data. And results calculated by the modified Chueh-Prausnitz (MCP) method

the Redlich-Kister method and the Radial Basis Function Neural Networks (RBFNN) were also in good agreement with experimental data. The maximum absolute deviations of the critical temperatures calculated by the PR equation

the SRK equation

the MCP method

the Redlich-Kister method

and the RBFNN are 1.82%

1.73%

0.95%

0.17% and 0.20%

respectively. The maximum absolute deviations of the critical pressures calculated by the PR equation

the SRK equation

the MCP method

the Redlich-Kister method

and the RBFNN are 6.07%

5.04%

3.49%

1.90% and 0.67%

respectively.

关键词

Keywords

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