Total aromatics of diesel fuels analysis by deep learning and near-infrared spectroscopy

Abstract

This article proposes a method for quickly detecting the total aromatics content of diesel fuels based on a deep learning algorithm and near-infrared spectroscopy. First, extract the features of the diesel fuel spectral data and eliminate redundant information through the deep belief nets. Second, use a new extreme learning machine to build a prediction model. The experimental results show that the root-mean-square error of prediction value is 0.715 and the coefficient of determination value is 0.988, which proves that the method has good performance. Compared with other typical prediction methods, this method has better predictive ability.

Keywords:

• Deep learning
• diesel fuels
• near-infrared spectroscopy
• total aromatics

Disclosure statement

The named authors have no conflict of interest, financial or otherwise.

Additional information

Funding

This work was supported by the Duy Tan University, Vietnam.