Modeling of Highly Nonideal Systems: 2. Prediction of High Pressure Phase Equilibria with the Group Contribution NRTL-PR EoS

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The goal of this study is to propose a modification of the NRTL-PR EoS for the prediction of phase equilibria and excess enthalpies in mixtures containing methanol with hydrocarbons; indeed, with these systems, the original equation is confronted to many difficulties arising from the simultaneous prediction of liquid-liquid equilibria together with vapor-liquid and enthalpy data. For this purpose, an additional term is included in the EoS excess Gibbs energy, E EoS g , of the Peng-Robinson equation to account for the self-association of methanol. The resulting NRTL-PRA EoS is successfully used for the prediction of both liquid-liquid and vapor-liquid equilibria, as well as excess enthalpies, in mixtures of methanol with hydrocarbons, light gases and associating compounds. Results are comparable to those obtained with other predictive EoS (VTPR and SAFT), but with the main advantage to predict all thermodynamic properties with a simple cubic equation.

Section: Introductionmentioning

confidence: 99%

The NRTL-PRA group contribution EoS for the simultaneous prediction of LLE, VLE and h of hydrocarbon mixtures with associating compounds

Neau¹,

Raspo²,

Escandell³

2016

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“…The model is based on the EoS/G E approach, in which the Peng-Robinson equation of state is associated with a generalized version of the NRTL model [11].…”

Section: Introductionmentioning

confidence: 99%

A new formulation of the predictive NRTL-PR model in terms of kij mixing rules. Extension of the group contributions for the modeling of hydrocarbons in the presence of associating compounds

Escandell

Neau

Nicolas

2011

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a b s t r a c tA generalized NRTL model was previously proposed for the modeling of non ideal systems and was extended to the prediction of phase equilibria under pressure according to the cubic NRTL-PR EoS. In this work, the model is reformulated with a predictive k ij temperature and composition dependent mixing rule and new interaction parameters are proposed between permanent gases, ethane and nitrogen with hydrocarbons, ethane with water and ethylene glycol. Results obtained for excess enthalpies, liquid-vapor and liquid-liquid equilibria are compared with those provided by the literature models, such as VTPR, PPR78, CPA and SRKm. A wide variety of mixtures formed by very asymmetric compounds, such as hydrocarbons, water and ethylene glycols are considered and special attention is paid to the evolution of k ij with respect to mole fractions and temperature.

“…(11-12) the binary interaction parameters Γ ji are computed by means of a group contribution [10] which makes the NRTL-PR equation a totally predictive model:…”

Section: The Nrtl-pr Modelmentioning

confidence: 99%

“…The purpose of this work is to explore the capability of the NRTL-PR model [10,11] to predict solid-liquid equilibria (SLE) in water-PAH mixtures. This model is based on the EoS/G E approach in which the Peng-Robinson equation of state is associated with the generalized NRTL excess Gibbs energy model [12].…”

Section: Introductionmentioning

confidence: 99%

Prediction of solid polycyclic aromatic hydrocarbons solubility in water, with the NRTL-PR model

Escandell¹,

Raspo²,

Neau³

2014

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The accurate prediction of high pressure phase equilibria is crucial for the development and the design of chemical engineering processes. Among them the modeling of complex systems, such as petroleum fluids with water, has become more and more important with the exploitation of reservoirs in extreme conditions. The aim of this work is to explore the capability of the NRTL-PR model to predict the solubility of solid polycyclic aromatic hydrocarbons in water. For this purpose, we first validate our methodology for fluid phase equilibria predictions of aromatic hydrocarbons and gas (CO 2 , C 2 H 6 ) mixtures. Finally, we consider the prediction of the solid solubility of PAH in water, by fitting group parameters either only on SLE data or on both LLE and SLE data of aromatic hydrocarbon-water binary systems.