FBG refractometry and electrical impedance analysis in fuel samples characterization

Abstract

This work reports the simultaneous use of electrical impedance spectroscopy and fiber Bragg grating (FBG) refractive index sensing in the estimation of the main components of specific fuel mixtures. Fuel samples containing gasoline, dehydrated ethanol, diesel, and kerosene were analyzed. Electrical impedance spectra and FBG sensor signals were registered for each mixture. Artificial Neural Networks (ANN) were used to estimate the ethanol concentration using the information from both sensors separately and to illustrate the methodology of fusing data from sensors that measure electrical permittivity at different frequency ranges, namely, an electrical impedance sensor and the etched FBG refractometric sensor. The behavior of the ANN to fuse data and the individual analysis of the sensor signals indicated that the joint use of the proposed techniques enhance the fuel estimation quality when compared to the usage of a singleton sensor.