Predicting the boiling point of diesel fuel using adaptive linear neuron and near infrared spectrum

Monitoring the boiling point of a diesel fuel is an important step to understand the characteristics of the diesel fuel. This study evaluated the feasibility of adaptive linear neuron (Adaline) as a predictive model to predict the boiling point of diesel fuel based on near infrared spectrum. The parameters of learning rate and training cycle that involved in the optimization process were examined and discussed. The best predictive accuracy was achieved by Adaline that used learning rate of 0.001 and 788 adaptation cycles with root mean square error of prediction (RMSEP) of 3.42 ^OC and correlation coefficient of prediction (rp) of 0.9739. Findings show that Adaline with adaptive learning approach is capable of predicting the boiling point of diesel fuel based on near infrared spectrum without using data reduction approach.