化工学报 ›› 2019, Vol. 70 ›› Issue (S2): 1-7.doi: 10.11949/0438-1157.20190443

• 热力学 • 上一篇    下一篇

混合工质临界性质的推算研究

张楠1(),陈龙祥2,胡芃1()   

  1. 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
  • 收稿日期:2019-04-28 修回日期:2019-05-08 出版日期:2019-09-05 发布日期:2019-11-07
  • 通讯作者: 胡芃 E-mail:nanzh@mail.ustc.edu.cn;hupeng@ustc.edu.cn
  • 作者简介:张楠(1994—),男,博士研究生,nanzh@mail.ustc.edu.cn
  • 基金资助:
    国家自然科学基金项目(51576187)

Theoretical study on critical properties of 4 kinds of binary systems

Nan ZHANG1(),Longxiang CHEN2,Peng HU1()   

  1. 1. Department of Thermal Science and Energy Engineering, University of Science and Technology of China, Hefei 230027, Anhui, China
    2. Quanzhou Institute of Equipment Manufacturing, Haixi Institutes, Chinese Academy of Sciences, Jinjiang 362200, Fujian, China
  • Received:2019-04-28 Revised:2019-05-08 Online:2019-09-05 Published:2019-11-07
  • Contact: Peng HU E-mail:nanzh@mail.ustc.edu.cn;hupeng@ustc.edu.cn

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%。

关键词: 临界性质, 二元混合物, 状态方程, 神经网络, 热力学性质

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.

Key words: critical properties, binary mixtures, equation of state, neural network, thermodynamic properties

中图分类号: 

  • TK 123

图1

径向基函数神经网络的基本结构"

表1

纯工质的临界参数和偏心因子[33] "

组分 T c/K P c/MPa V c/(L/mol) ω
CO2 304.13 7.3773 467.6 0.22394
R600 425.13 3.796 228.0 0.201
R1234yf 367.85 3.3822 475.55 0.276
R1234ze 382.51 3.6349 489.24 0.313
R32 351.26 5.782 424.0 0.2769
R125 339.17 3.6177 573.58 0.3052

表2

五种方法对各混合工质的计算结果的绝对平均偏差"

混合工质 PR SRK MCP Redlich-Kister RBFNN
δT c/% δP c/% δT c/% δP c/% δT c/% δP c/% δT c/% δP c/% δT c/% δP c/%
CO2+R1234yf 0.29 2.51 0.12 2.11 0.11 0.71 0.07 0.28 0.20 0.49
CO2+R1234ze 0.30 2.26 0.18 1.88 0.17 1.67 0.17 0.51 0.06 0.67
CO2+R600 1.82 6.07 1.73 5.04 0.95 3.49 0.16 1.90 0.05 0.30
R32+R125 0.02 0.31 0.02 0.31 0.04 0.57 0.02 0.22 0.04 0.38

图2

混合物的临界压力随组分1的摩尔分数变化"

图3

混合物的临界温度随组分1的摩尔分数变化"

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