Neural network prediction of cetane number and density of diesel fuel from its chemical composition determined by LC and GC–MS

Abstract

Neural networks were used to correlate and predict the cetane number and the density of diesel fuel from its chemical composition. Cetane number (CN) and density were correlated with 12 hydrocarbon groups in diesel fuel determined by liquid chromatography (LC) and gas chromatography–mass spectrometry (GC–MS). In total, 69 diesel fuels were available for this study: 48 diesel fuels were included in the training data set and 21 in the test data set. Various neural network architectures were trained using the training data set, and the accuracy of the model obtained was examined by using the test data set. For correlating both CN and density in this study, the best neural network architecture was a general regression neural network (GRNN). With the test data set, the mean absolute errors were 1.23 (CN) and 0.002 g/cm 3 for the CN and density, respectively. Predictive equations for CN and density of diesel fuel from its chemical composition were also developed with a standard multiple linear regression method. The comparison of the neural network method with the multiple linear regression method, using this data set, revealed that for complex nonlinear problems such as the correlation of the CN with the hydrocarbon type characterization, the neural network approach could provide a better model. However, for a simpler correlation problem like the density of a diesel fuel, which is approximated well by the sum of the contributions of individual components, the predictive equations produced by multiple linear regression and neural network methods gave similar results.