Scaled Transport Coefficient Correlations for Fluids

Rate-based models suitable for equipment or insitu process modeling require a capability for predicting transport coefficients over a sufficient range of temperature and pressure.  This paper illustrates a relatively simple novel approach for correlating and estimating pure component transport coefficients, for self-diffusion, viscosity, and thermal conductivity, over the entire fluid region.  This physics-based approach uses two or three component fitting parameters for the entire fluid region, and should be relatively easy to implement in equipment and oil-gas recovery modeling software.  These pure-component transport coefficients could be used in various fluid mixture transport models that utilize pure component properties.  For example:  liquid diffusion (Bart and Bosse, 2006), viscosity (Novak et.al, 2004), and thermal conductivity (Rowley et.al, 1982). This presentation will illustrate an advantage of using Chapman-Enskog transport coefficient scaling and residual entropy for correlating pure component self-diffusion coefficient, viscosity, and thermal conductivity over the entire fluid region, covering a wide range of temperature and pressure.   It will also be demonstrated that a segment-based approach, using the PC-SAFT equation of state, results in a correlative and predictive model.  Experimental evaluations for viscosity used 3122 data points for eighteen n-alkanes, ranging from methane up to 2390 molecular weight linear polyethylene.  Temperatures ranged from 96 °K to 650 °K and pressures ranged from 10^-4 atm to 4990 atm.  Correlative and predictive AAD’s were 3.9% and 6.7%, respectively, over the entire fluid region.  Thus, one may predict n-alkane component fluid viscosity when little or no data are available.  Preliminary thoughts on extension of the method to other compounds will be presented.