Synergistic effect analysis on sooting tendency based on soot-specialized artificial neural network algorithm with experimental and numerical validation

Abstract

Interaction between components is an important characteristic in a fuel mixture, which has significant influence on sooting tendency and is strongly related to chemical structures of components. It is time-consuming to study mixing effects between multiple fuels on soot formation through experimental methods, so it is essential to develop a simple and reliable statistical method to supplement experimental data. All sooting tendency data was transformed into a dimensionless index called Yield Sooting Index (YSI). A machine learning algorithm, named soot-specialized artificial neural network (S-ANN), was developed in this paper to predict sooting tendency of fuel surrogates. The mean relative error of S-ANN prediction results can be reduced to about 6%, which is better than that of linear method (13.72%). As a widely used index, YSI can characterize the relative scale of fuel sooting tendency but cannot clearly represent the synergistic or neutralization effects of mixture components. Therefore, this work proposes Soot Synergistic/Neutralization Index (SSNI) using the deviation between predicted value and linear fitting value. SSNIs are transformed from the prediction results of S-ANN and can indicate the interactions between fuels in terms of sooting tendency, which can also provide important information for component selection and sooting tendency adjustment during formulation process of surrogates. Two groups of binary mixtures were selected to validate the algorithm and index. From validation experiment, it is indicated that negative synergistic effect exists between n-cetane and iso-cetane; positive synergistic effect exists between iso-cetane and butylcyclohexane. These two synergistic effects are simulated and analyzed through previously developed chemical mechanism. The results reveal that soot-specialized artificial neural network algorithm have a strong ability to predict sooting tendency of fuel mixtures, which can be used as an accurate and low-cost approach to supplement experimental data. SSNI can help to understand the interactions of fuel mixtures and provide valuable guidance for the construction of surrogates.