(321j) Developing a New Equation of State with Non-Quadratic Mixing Rules for Prediction of the Phase Equilibria of Polar and Associated Fluids | AIChE

(321j) Developing a New Equation of State with Non-Quadratic Mixing Rules for Prediction of the Phase Equilibria of Polar and Associated Fluids

Authors 

Emami, F. - Presenter, Iran University of Science and Technology
Rahdar, H. R. - Presenter, Iran University of Science and Technology
Feyzi, F. - Presenter, Iran University of Science and Technology


Feyzi-Riazi equation of state (PRFR EOS) which is a modification of Peng-Robinson equation of state is modified in this research. The proposed modification estimates the specific volume of polar liquids and hydrogen-bonded fluids with greater accuracy while maintaining the ability of PRFR EOS in predicting vapor pressure and critical compressibility factor of pure compounds. The new EOS overcomes the shortcomings of the other equations in predicting and correlating of the phase behavior of polar solutions by using of the conventional van der Waals and non-quadratic mixing rules. The proposed model is applied to correlate and predict the experimental data of vapor-liquid equilibria (VLE) and liquid densities of various binary nonideal and polar solutions. For this purpose eight mixing rules (van der Waals, HVO, WS, MHV1, MHV2, MHV, LCVM and HVOS) were used. The performance of the combination of the new EOS with these mixing rules is more accurate in all cases. Among the mixing rules considered in this work, only the WS and the MHV are the best predictive tools. 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 and MHV methods for extrapolation. In the Gex part of the proposed model the NRTL and the UNIQUAC-type models were used, respectively. NRTL model has weak predictive capabilities due to its temperature-dependent parameters. The results show that the UNIQUAC-type models have better capabilities for prediction of such polar organic mixtures and NRTL could be used as a correlative model.

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