Bioethanol fuel quality assessment using dielectric spectroscopy

Abstract

This study first aims to determine the physical characteristics of bioethanol from its dielectric properties during the production process. For this purpose, bioethanol samples were produced from sugarcane molasses under different fermentation conditions such as temperature and reaction time. The main permittivity properties, including dielectric constant and loss factor at various frequencies, were measured as the inputs, whereas flash point and octane number, as significant physical parameters, were considered as output variables. Linear regression (LR) models were developed to predict the relationships between predictors and targets. Next, four learning algorithms – multivariate regression splines (MARS), M5 tree, multilayer perceptron (MLP), and support vector regression (SVR) – were deployed to improve the models’ performance. The results from LR models revealed that the flash point had a direct relationship with the dielectric constant and loss factor. However, the octane number was indirectly proportional to the dielectric properties. Also, analysis of the testing data set of learning algorithms indicated that the best algorithm for predicting the responses was MARS, whereas the SVR model performed with the lowest accuracy. The findings from this paper suggest that dielectric spectroscopy is a valuable approach for estimating the physical features of bioethanol.

Keywords:

• Bioethanol dielectric properties
• octane number
• flash point
• learning algorithms

Acknowledgements

All support is gratefully acknowledged. The authors thank the Renewable Energy Lab of Tarbiat Modares University and Iran Telecommunication Research Centre for their kind cooperation in this research. The first author also thanks McGill University and the academic staff at Macdonald Campus for their valuable support during her research visit in 2018–2019.

Disclosure statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Additional information

Funding

This research work was funded by Ferdowsi University of Mashhad (Grant No. 45019, 2017).