Volume-translated Peng–Robinson equation of state for saturated and single-phase liquid densities

摘要: Abstract Cubic equations of state (CEOS) are widely used for process design calculations and reservoir simulations in the oil gas industry. However, most CEOS yield poor predictions liquid densities. To remedy this problem, several volume-translation approaches have been presented literature. Currently, these that appear reasonable only saturated or single-phase region. In work, a method is applicable to both regions. The contains one fluid-specific parameter, which has generalized terms readily available molecular properties such as critical compressibility factor, acentric factor dipole moment. Three specific cases were considered ranging from simple linear correlations non-linear neural networks generalizing parameter. network models require 3–6 selected fluid (in addition usual temperature, pressure factor). For model development generalization, database highly accurate data densities was compiled. 65 pure fluids involving classes chemical compounds vary their size, shape, chain-length, asymmetry polarity. developed based on fluids, an additional 20 validating model. Results indicate work capable precise representations density database. Specifically, overall average absolute percentage deviation (%AAD) 0.6 obtained including more than 12,000 points. then generalized, generalization predicted same set with %AAD 0.8. Further, validated by predicting not development. provided 1.0%AAD. approach also extended predict Of tested, 1.8%AAD. Thus, producing compressed diverse molecules.