سال انتشار: ۱۳۸۳

محل انتشار: نهمین کنگره ملی مهندسی شیمی ایران

تعداد صفحات: ۱۷

نویسنده(ها):

Amir Vahid – Thermokinetic Research Laboratory, Department of Chemical Engineering, Iran University of Science and Technology, Tehran,
Fatemeh sadat Emami – Thermokinetic Research Laboratory, Department of Chemical Engineering, Iran University of Science and Technology, Tehran,
Farzaneh Feyzi – Thermokinetic Research Laboratory, Department of Chemical Engineering, Iran University of Science and Technology, Tehran,

چکیده:

In this research, the capabilities of PRSV (Peng-Robinson-Stryjek-Vera) and PRRF (Peng-Robinson-Rahdar-Feyzi) EOS + excess free energy (G ex ) mixing rules in representing the behavior highly polar solutions are compared. The PRRF equation of state overcomes the shortcomings of PRSV and related EOS in predicting and correlating the phase behavior of polar solutions. The combined EOS + Gex models need to reproduce the excess Gibbs free energy models as closely as possible in order to represent low-pressure vapor-liquid equilibrium behavior of polar mixtures accurately and also to make the vapor-liquid predictions at higher temperatures and pressures accurate using only low-pressure information. The proposed model is applied to correlate and predict the experimental data of vapor-liquid equilibria (VLE) for various binary nonideal and polar solutions. For this purpose six mixing rules (HVO, WS, MHV1, MHV2, LCVM and HVOS) were used with each equation of state (PRSV and PRRF). The performance of the combination of PRRF EOS with these mixing rules is more accurate in all cases. Among the mixing rules considered in this work, only the WS is the best predictive tool. However all of the approximate methods (MHV1, MHV2, LCVM and HVOS) demonstrate good correlative capabilities, and some predictive capabilities, though they are generally less accurate than the WS method for extrapolation. In the G ex part of the proposed model the NRTL and the UNIQUAC models were used, respectively. NRTL model has weak predictive capabilities due to its temperature-dependent parameters. The results show that the UNIQUAC model has better capabilities for prediction of such polar organic mixtures and NRTL could be used as a correlative model.