Fluid Viscosity-Residual Entropy Correlation

Abstract

Chemical reaction engineering, process engineering, and product engineering models are used for design and analysis. Often, transport coefficient models are needed in equipment and in-situ models to account for the importance of momentum, heat, and mass transfer. Previous work+ demonstrated a novel component-based reference equation of state approach for correlating self-diffusion coefficient and viscosity over the entire fluid region (liquid, gas, and critical fluid). In this paper, a segment-based approach is used to extend the previous work+ from a limited number of individual component correlations to a predictive fluid viscosity correlation for a class of components consisting of n-alkanes, up to 1300 molecular weight, covering a wide range of components, temperatures, and pressures. A scaled segment viscosity-segment residual entropy correlation (V-S model) was introduced and evaluated here. PC-SAFT segment parameters and residual entropy were used in a correlation model linking viscosity to the PC-SAFT equation of state. Experimental evaluation of this V-S model 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 atmospheres to 4990 atmospheres. The conditions studied are relevant to oil and gas reservoir engineering and other in-situ processes. Based on this work, covering the entire fluid region, the V-S model was found to result in a group correlation squared (R2) of -0.998 and group average absolute deviation (AAD) of 3.9%. Individual viscosity segment correlation parameters (Bseg and Aseg) were fitted to molecular weight and used in the predictive mode. In the predictive mode, a group AAD of 6.7% was obtained for n-alkanes from methane up to 1300 molecular weight linear polyethylene, over the entire fluid region. The scaled segment viscosity-segment residual entropy model introduced here has potential for a much broader range of applications. In addition, this model would be easy to embed in existing in-house and commercial simulators to provide predictive properties and rate-based modeling capability. + Novak, http://www.bepress.com/ijcre/vol9/A63