Integration of computational modeling and experimental techniques to design fuel surrogates Academic Article uri icon


  • 2017 Conventional gasoline comprises of a large number of hydrocarbons that makes it difficult to utilize in a model for prediction of its properties. Modeling is needed for a better understanding of the fuel flow and combustion behavior that are essential to enhance fuel quality and improve engine performance. A simplified alternative is to develop surrogate fuels that have fewer compounds and emulate certain important desired physical properties of the target fuels. Six gasoline blends were formulated through a computer aided model based technique Mixed Integer Non-Linear Programming (MINLP). Different target properties of the surrogate blends for example, Reid vapor pressure (RVP), dynamic viscosity (), density (), Research octane number (RON) and liquid-liquid miscibility of the surrogate blends) were calculated. In this study, more rigorous property models in a computer aided tool called Virtual Process-Product Design Laboratory (VPPD-Lab) are applied onto the defined compositions of the surrogate gasoline. The aim is to primarily verify the defined composition of gasoline by means of VPPD-Lab. and RVP are calculated with more accuracy and constraints such as distillation curve and flash point on the blend design are also considered. A post-design experiment-based verification step is proposed to further improve and fine-tune the best selected gasoline blends following the computation work. Here, advanced experimental techniques are used to measure the RVP, RON and distillation temperatures. The experimental results are compared with the model predictions as well as the extended calculations in VPPD-Lab.

published proceedings

  • Journal of Natural Gas Science and Engineering

author list (cited authors)

  • Choudhury, H. A., Intikhab, S., Kalakul, S., Gani, R., & Elbashir, N. O.

complete list of authors

  • Choudhury, HA||Intikhab, S||Kalakul, S||Gani, R||Elbashir, NO

publication date

  • January 1, 2018 11:11 AM