The ignition delay prediction model of three-component surrogates was established based on the back propagation (BP) neural network . The ambient temperature, ambient pressure , molar fraction of n-heptane, iso-octane and toluene were utilized as the input parameters. The ignition delay was utilized as the output parameter. The training and validation set only contained the 0-D simulation ignition delay of single- and two-component surrogates. But the trained BP neural network also showed a strong predictive ability towards the ignition delay of three component toluene primary reference fuel (TPRF) surrogates. Results show that the BP neural network with two hidden layers performs better than that with single hidden layer. With the optimization of genetic algorithm (GA) and particle swarm optimization (PSO) algorithm, the correlation coefficient is higher than 0.9996. The mean relative error (MRE) and the mean square error (MSE) are also maintained at a low level. The computational costs of 0-D simulation and BP neural network methods are compared. In 0-D simulation, the computational time of one case is 28 s. When the BP neural network is utilized, the computational time of 176 cases is just 3.2 s, which shows a significant improvement in the computation time. Through the mean impact value (MIV) analysis, the significance of each input variable to the output results is investigated. The input parameters of ambient temperature and molar fraction of iso-octane obtain the highest and lowest absolute value of MIV, respectively, indicating the major and minor effects on the ignition delay. Based on the predicted ignition delay of three-component TPRF surrogates, the research octane number (RON) and motor octane number (MON) can also be accurately predicted with the maximum deviance no more than 3 units. For real fuels of fuels for advanced combustion engines (FACE) gasolines, such as FACE A and FACE C gasolines, the surrogate fuel which has the same ignition delay at the specific pressure and temperature can be determined through the construction of the ignition delay look-up table of TPRF surrogates by the BP neural network. Following this method, the ignition delay of the real fuels can be matched accurately and the molar fraction of each component of the corresponding TPRF surrogates can also be acquired.

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