City Research Online

Purely predictive method for density, compressibility, and expansivity for hydrocarbon mixtures and diesel and jet fuels up to high temperatures and pressures

Rokni, H., Gupta, A., Moore, J., McHugh, M. A., Bamgbade, B. and Gavaises, M. ORCID: 0000-0003-0874-8534 (2019). Purely predictive method for density, compressibility, and expansivity for hydrocarbon mixtures and diesel and jet fuels up to high temperatures and pressures. Fuel, 236, pp. 1377-1390. doi: 10.1016/j.fuel.2018.09.041

Abstract

This study presents a pseudo-component method using the Perturbed-Chain Statistical Associating Fluid Theory to predict density, isothermal compressibility, and the volumetric thermal expansion coefficient (expansivity) of hydrocarbon mixtures and diesel and jet fuels. The model is not fit to experimental density data but is predictive to high temperatures and pressures using only two calculated or measured mixture properties as inputs: the number averaged molecular weight and hydrogen to carbon ratio. Mixtures are treated as a single pseudo-component; therefore binary interaction parameters are not needed. Density is predicted up to 470 K and 3,500 bar for hydrocarbon mixtures and fuels with 1% average mean absolute percent deviation (MAPD). Isothermal compressibility is predicted with 4% average MAPD for hydrocarbon mixtures and 9% for fuels. The volumetric thermal expansion coefficient is predicted with 7% average MAPD for hydrocarbon mixtures and 13% for fuels.

Publication Type: Article
Additional Information: © Elsevier, 2019. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/
Publisher Keywords: PC-SAFT, Diesel fuel, Density, Derivative properties, Pseudo-component, High pressures
Subjects: T Technology > TJ Mechanical engineering and machinery
Departments: School of Mathematics, Computer Science & Engineering > Engineering > Mechanical Engineering & Aeronautics
URI: http://openaccess.city.ac.uk/id/eprint/21722
[img]
Preview
Text - Accepted Version
Available under License Creative Commons Attribution Non-commercial No Derivatives.

Download (2MB) | Preview

Export

Downloads

Downloads per month over past year

View more statistics

Actions (login required)

Admin Login Admin Login