【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ChuehPrausnitz(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%。更多还原

【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 SoaveRedlich-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.更多还原