(222bf) Volume-Translated Peng-Robinson Equation of State for Liquid Densities of Diverse Binary Mixtures
AIChE Annual Meeting
2013
2013 AIChE Annual Meeting
Engineering Sciences and Fundamentals
Poster Session: Thermodynamics and Transport Properties (Area 1A)
Monday, November 4, 2013 - 6:00pm to 8:00pm
Two-parameter cubic equations of state (CEOS) are used
widely in process engineering calculations; however, inaccurate liquid density
predictions remain a significant deficiency in these equations. To remedy this
problem, a volume translation of the CEOS is employed frequently. In a recent
work, we presented a volume-translated Peng-Robinson
equation of state (VTPR EOS) that is capable of providing accurate density
predictions for both saturated- and single-phase regions of pure fluids at high
pressures. In the current work, we present an extension of that approach,
employing conventional mixing rules, to predict densities of liquid mixtures
over wide ranges of pressure and temperature. For this purpose, two databases
were compiled for vapor-liquid equilibrium and liquid density measurements of
73 binary systems composed of diverse chemical species. The molecular species
in the databases ranged widely in terms of molecular size, shape, asymmetry and
polarity and, thus, were well suited to test the efficacy of our approach.
Overall, the databases contained more than 5,000 data points for vapor-liquid
equilibrium measurements and over 13,000 data points for liquid density
measurements of mixtures.
The VTPR EOS predictions of mixture liquid densities
yield errors that are three to five times lower than the corresponding
predictions from the untranslated Peng-Robinson
equation of state (PR EOS). Specifically, the overall percentage average
absolute deviations (%AAD) from the VTPR EOS varied from 1.5 to 3 for binary
mixtures. This represents a substantial improvement relative to the untranslated PR EOS, for which errors ranged from 2 to 15
%AAD. These results indicate that extension of the VTPR EOS to liquid mixtures
is capable of providing reliable density predictions for diverse binary
mixtures over a wide range of pressure and temperature.