混合工质临界性质的推算研究

doi:10.11949/0438-1157.20190443

摘要/Abstract

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

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.

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

图/表 5

图1 径向基函数神经网络的基本结构

Fig.1 Basic structure of radial basis function neural network

表1 纯工质的临界参数和偏心因子[33]

Table 1 Critical parameters and eccentricity factors of pure working fluids[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

Table 2 Absolute average deviations of calculated results of five methods for four kinds of binary system

混合工质	PR		SRK		MCP		Redlich-Kister		RBFNN
混合工质	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

Fig.2 Critical pressures of mixtures varied with mole fraction of component 1

图3 混合物的临界温度随组分1的摩尔分数变化

Fig.3 Critical temperatures of mixtures varied with mole fraction of component 1

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