Entropy scaling based viscosity predictions for hydrocarbon mixtures and diesel fuels up to extreme conditions

Abstract

An entropy scaling based technique using the Perturbed-Chain Statistical Associating Fluid Theory is described for predicting the viscosity of hydrocarbon mixtures and diesel fuels up to high temperatures and high pressures. The compounds found in diesel fuels or hydrocarbon mixtures are represented as a single pseudo-component. The model is not fit to viscosity data but is predictive up to high temperatures and pressures with input of only two calculated or measured mixture properties: the number averaged molecular weight and hydrogen to carbon ratio. Viscosity is predicted less accurately when the mixture contains high concentrations of iso-alkanes and cyclohexanes. However, it is shown that predictions for these mixtures are improved by fitting a 2 third parameter to a single viscosity data point at a chosen reference state. For hydrocarbon mixtures, viscosity is predicted with average mean absolute percent deviations (MAPDs) of 12.2% using the two-parameter model and 7.3% using the three-parameter model from 293 to 353 K and up to 1,000 bar. For two different diesel fuels, viscosity is predicted with an average MAPD of 21.4% using the two-parameter model and 9.4% using the three-parameter model from 323 to 423 K and up to 3,500 bar.

Effect of Composition, Temperature, and Pressure on the Viscosities and Densities of Three Diesel Fuels

“…This observation was developed into a calculation methodology by Novak 76,77 and further generalized by Lötgering-Lin and Gross 46 who use the PC-SAFT EoS 22 with a GC method for calculating the viscosity correlation coefficients needed to predict viscosity. Here we provide a brief overview of Rokni et al's technique and direct the reader to the SI and elsewhere 45 for additional details. In this instance,…”

“…There are also several methods for modeling the viscosity of complex mixtures, including expanded fluid theory [31][32][33][34] , friction theory [35][36][37][38][39] , free volume theory (FVT) 40,41 , hard sphere models [42][43][44] , residual entropy scaling 45,46 , Eyring's absolute rate theory 47,48 , and GC methods [49][50][51][52] .…”

“…Lin and Tavlarides 25 predict the viscosities of twenty surrogate mixtures using friction theory that involves fitting pure component parameters for each compound in the mixture. Rokni, et al 45 A RBVD apparatus is used here to simultaneously measure viscosities and densities at temperatures from 298.2 to 532.6 K and pressures up to 300.0 MPa for three different diesels. The resultant experimental uncertainty of the RBVD data is directly proportional to the availability of a calibration fluid with highly accurate viscosity/density data at HPHT conditions and with a viscosity profile similar to that expected for the fluid of interest.…”

General method for prediction of thermal conductivity for well-characterized hydrocarbon mixtures and fuels up to extreme conditions using entropy scaling

“…Pure compound PC-SAFT parameters are determined by directly fitting to properties (e.g., saturated liquid density and vapor pressure data) or using group contribution (GC) methods [49][50][51]. In this study, the PC-SAFT parameters are determined using the GC parameters of Sauer et al [49] to be consistent with our earlier reported technique to predict viscosity [52]. However, in general, other PC-SAFT GC parameters could be used, such as those reported by Tihic et al [53] or Burgess et al [54].…”

“…Figure 1 shows these coefficients plotted versus MW for n-alkanes and PNAs, and the correlations are listed in Table 3. In our previous study [52]…”