Literature DB >> 17125380

Predicting the phase behavior of nitrogen + n-alkanes for enhanced oil recovery from the SAFT-VR approach: examining the effect of the quadrupole moment.

Honggang Zhao1, Pedro Morgado, Alejandro Gil-Villegas, Clare McCabe.   

Abstract

The phase behavior of nitrogen + n-alkanes is studied within the framework of the statistical associating fluid theory for potentials of variable range (SAFT-VR). The effect of the quadrupole moment of nitrogen on the phase behavior is considered through an extension of the SAFT-VR equation that includes an additional contribution to the Helmholtz free energy due to quadrupolar interactions. A significant improvement in the description of the phase diagram of the binary mixtures of nitrogen with different n-alkanes is obtained with the new approach when compared to predictions from the original SAFT-VR EOS (i.e., without the quadrupolar term). The experimental value for the quadrupole moment of nitrogen is used in the new equation; thus, no additional parameters are employed. Given the nonideal nature of the binary mixtures, a binary interaction parameter is needed to describe the full-phase diagram and high-pressure critical lines of these systems; however, this can be fitted to a single system and successfully used to predict the phase behavior of other binary mixtures without additional fitting. Furthermore, only a single, transferable, cross-energy parameter is required when the quadrupolar term is considered, whereas a cross-range parameter is also needed with the original SAFT-VR approach. The inclusion of the quadrupolar term in the equation of state therefore reduces the need to use effective parameters by explicitly including at the molecular level interactions due to the quadrupole moment.

Entities:  

Year:  2006        PMID: 17125380     DOI: 10.1021/jp063444b

Source DB:  PubMed          Journal:  J Phys Chem B        ISSN: 1520-5207            Impact factor:   2.991


  1 in total

1.  Modeling of nitrogen solubility in normal alkanes using machine learning methods compared with cubic and PC-SAFT equations of state.

Authors:  Seyed Ali Madani; Mohammad-Reza Mohammadi; Saeid Atashrouz; Ali Abedi; Abdolhossein Hemmati-Sarapardeh; Ahmad Mohaddespour
Journal:  Sci Rep       Date:  2021-12-22       Impact factor: 4.379

  1 in total

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