Prediction and Sensitivity Analysis of the Cetane Number of Different Biodiesel Fuels Using an Artificial Neural Network

The cetane number (CN) of biodiesel fuels was predicted using an artificial neural network (ANN). A data set with 156 measured biodiesel CN data points was first collected from the literature. Then, two input sets were introduced for training the ANN including the fatty acid methyl ester (FAME) composition and the functional group of FAMEs. In the composition-based method, the input set includes the mass fractions of the 14 FAME components from C10:00 to C24:00. In the functional group-based method, the input set contains three improved functional group parameters of n(-CH2-)/n(C), n(C C)/n(C), and the position index of C C. For the composition-based method and the functional group-based method, the best mean absolute errors are, respectively, 1.70 and 1.72, and the best mean relative errors are, respectively, 3.13 and 3.24% for the test set. To deeply understand the correlations between the CN and the composition and molecular structure of the FAMEs in biodiesel, an analysis method for calculating the single-factor and double-factor sensitivity coefficients between the input set and the output set was first implemented for the fuel property prediction study. It was found that C18:01, C18:02, and C18:00, as well as n(-CH2-)/n(C) and n(C.C)/ n(C), provide the largest sensitivity coefficients.

Automated summary

The cetane number of biodiesel fuels was predicted using an artificial neural network, with two input sets based on fatty acid methyl ester (FAME) composition and functional groups of FAMEs. The composition-based method showed lower mean absolute errors and relative errors than the functional group-based method in testing. Analysis revealed that C18:01, C18:02, C18:00, n(-CH2-)/n(C), and n(C.C)/n(C) had the largest sensitivity coefficients.