Predicting PR EOS binary interaction parameter using readily available molecular properties

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• 作者：Agelia M. Abudour^a ; Sayeed A. Mohammad^a ; Robert L. Robinson Jr.^a ; Khaled A.M. Gasem^a ; ^b ; ^{gasem@uwyo.edu}
• 关键词：Peng-Robinson equation of state ; Cubic equations of state ; Vapor-liquid equilibrium ; Generalized model ; Binary interaction parameters
• 刊名：Fluid Phase Equilibria
• 出版年：2017
• 出版时间：25 February 2017
• 年：2017
• 卷：434
• 期：Complete
• 页码：130-140
• 全文大小：2337 K
• 卷排序：434

文摘

The proper design, operation, simulation and optimization of numerous chemical processes require the knowledge of phase behavior of coexisting phases over wide ranges of pressure and temperature. A convenient method for describing equilibrium phase behavior is use of analytic equations of state. The most widely used equations of state (EOS) are the cubic van der Waals type equations such as the Soave–Redlich–Kwong (SRK) and Peng–Robinson (PR) EOS. These equations utilize one or two empirical binary interaction parameters (BIPs) for each binary pair in a mixture. These BIPs can have a significant effect on the predicted properties of mixtures and are generally required for accurate predictions.In a recent work, we developed generalized models to estimate the BIPs in the PR EOS for non-ideal, low-pressure systems using a quantitative structure–property relationship (QSPR) modeling approach. In a departure from our earlier work, we present in this study a different modeling approach which utilizes only readily available molecular properties of the components involved in a mixture. This greatly simplifies application of our new models, since they do not require elaborate computational methods for generation of molecular descriptors.To develop the present generalized model, a VLE database consisting of 1010 binary systems comprised of non-ideal, low-pressure binary systems as well as asymmetric, high-pressure binary systems was utilized. Using this database, the BIPs for the PR EOS were first determined by regressing the VLE experimental data for each system. Then, the BIPs were generalized in terms of molecular properties of components in a mixture. Results indicate that the generalized model provides bubble-point pressure predictions with an average absolute deviation (AAD) of 9% when compared with an AAD of 4.7% through direct regressions of the BIPs. Further, the model developed in this study was compared to our previous model by predicting bubble-point pressures of non-ideal, low-pressure binary systems. The predictions from the model developed in this study were comparable to our previous results (9.2% compared to 8.3% AAD), thus demonstrating the usefulness of the simpler approach presented in this work.