Abstract

An array of quartz crystal sensors modified by chemical materials was implemented for vapor detection. In this study, the vapor sensor array was employed to qualify the fuels. This was done to distinguish some common fuels and also to determine the rate of fuel adulteration. All the measurements were conducted at room temperature. The sensor response consumed only up to one minute for a measurement cycle. After statistical data analysis including Principal Component Analysis and Neural Network methods, it was possible to conclude that the sensor array is able to distinguish the fuel vapors with high reproducibility and to determine the rate of fuel adulteration with linear correlation.

Original languageEnglish
Pages (from-to)6737-6743
Number of pages7
JournalARPN Journal of Engineering and Applied Sciences
Volume10
Issue number16
Publication statusPublished - 18 Nov 2015

Keywords

  • Fuel vapor
  • Neural network
  • Principal component analysis
  • Quartz sensor array

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