A novel
method to evaluate volumetric properties, namely the thermal
expansivity (α
P
) and the isothermal
compressibility (κ
T
) for nonpolar
hydrocarbon systems using refractive index measurements, is presented
in this work. New expressions for α
P
and κ
T
are derived from the Lorentz–Lorenz
equation and the One-Third rule, respectively. A further simplified
expression for α
P
is proposed requiring
only refractive index data and molecular weight for calculation. Densities
and refractive indices of 12 pure nonpolar hydrocarbons, 6 hydrocarbon
mixtures, and 3 crude oils are measured at temperatures from 283.15
K up to 343.15 K and at 0.1 MPa. The measured refractive indices are
used to calculate α
P
for a wide
range of temperatures using the proposed method, and the measured
densities are used to calculate α
P
for comparison. Reported densities and refractive indices of benzene
at 298.15 K and at pressures up to 90 MPa are used for κ
T
evaluations with the proposed method. Values
of α
P
and κ
T
calculated from refractive index measurements are in good
agreement with experimental data and those determined from densities.
This work aims to establish the foundation for experimental methods
to determine volumetric properties of nonpolar hydrocarbon systems
based on refractive index measurements. A high temperature and high
pressure refractometer is expected to have multiple advantages over
conventional techniques for density measurements, which include but
are not limited to smaller amounts of sample needed, simpler calibration,
faster measurement, and cells that are corrosion-resistant (i.e.,
sapphire glass).
Modeling thermodynamic properties can be challenging when the data availability for parameters identification is limited. Fully predictive group contribution (GC) methods have been developed as an alternative to overcome data scarcity. However, in order to provide a higher degree of accuracy, most recent GC approaches require detailed information on the molecule's structure, which is not acquirable for systems with unspecified components. This work intends to establish the foundation to overcome this limitation. The proposed PC-SAFT approach assisted by a homosegmented group contribution scheme permits parameters calculation with accuracy due to one adjustable parameter without requiring meticulous information regarding the molecular structure, for instance, the relative position between carbon-centered groups. This semipredictive approach is especially suitable for cases in which some data are available but are not taken into consideration by current GC models. A genetic algorithm-based routine was developed to determine the group contribution parameters by simultaneously optimizing vapor pressure and saturated liquid density calculations of sixty-nine pure hydrocarbons. Further analysis indicated good predictive capabilities of the model in the extreme case where a single vapor pressure data point was provided to adjust the model parameter, with an average percentage absolute deviation of 3.79% in saturation pressure and 2.40% in saturated liquid density over 18 additional compounds that were not included in the training database. Overall, the results have shown that satisfactory predictions are possible provided a simple quantification of different carbon groups based on the type of bonds they form and a single saturation pressure point. Therefore, given that the proposed approach does not require the relative position between groups in a molecule, the method may extend the applicability of the group contribution concept to some specific industry applications.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.