An artificial activity coefficient modeling approach for emulating combustion and physical property variations during distillation of real complex fuel

Real complex fuels consist of hundreds of species and hence are difficult to model in numerical simulations. There have been many methods to formulate simple surrogate suites composed of several compounds. While these surrogates emulate gaseous combustion properties with acceptable fidelity, due to the insufficient number of surrogate components comprehensive distillation behaviors of simple surrogates exhibit considerable discrepancy compared to that of real complex fuels. In this work, we demonstrated a new methodology to effectively represent a complex fuel. Firstly, we obtained information about functional group evolution of a complex fuel (Chinese aviation fuel, RP-3) from distillation experiments and formulated a rather complex surrogate mixture. Then, a Functional Group Matching (FGM) method was applied to convert the complex surrogate to a target 4-component surrogate mixture with an artificial activity coefficient model (AACM) using which distillation of real complex fuels can be accurately replicated. This AACM was validated in batch distillation simulation in comparison with the complex surrogate with conventional phase equilibrium model (UNIQUAC Functional-group Activity Coefficient (UNIFAC)). Results showed that chemical functional groups, combustion property targets (CPTs) and physical properties (density, surface tension, and dynamic viscosity) during a distillation process of the simple surrogate well represented those of the complex surrogate. Also, the computational efficiency of this 4-component surrogate with AACM method was verified, which was thousands times faster than the 24-component mixture with UNIFAC. The AACM for a simple surrogate mixture therefore contained the most authentic physics from the distillation experiments of a real complex fuel with potential to improve the computational efficiency and to couple with the chemical kinetics mechanisms available to the simple surrogate. (c) 2021 The Combustion Institute. Published by Elsevier Inc. All rights reserved.

Automated summary

A new methodology using Functional Group Matching (FGM) method and artificial activity coefficient model (AACM) was proposed to effectively represent a complex fuel by converting it to a target 4-component surrogate mixture, which accurately replicates the distillation process of real complex fuels.