Electronic Nose System for Safety Monitoring at Refineries | ||
| Journal of Engineering and Sustainable Development | ||
| Article 1, Volume 16, Issue 4, December 2012, Pages 220-228 | ||
| Author | ||
| Walaa Mohammed Hassan Khalaf | ||
| Abstract | ||
| In this paper an Electronic Nose (ENose) is presented which is designed for both at identifying the gas type and if it is pure or not, and at estimating the concentrations of the components of that mixture of LPG gases (Methane, Hexane, or Hydrogen) and Hydrogen Sulfide produced always inside the refineries. Our system contains 8 sensors, 5 of them are gas sensors (of the class TGS from FIGARO USA, INC., the sensing element of two of them is catalytic (TGS-6810 and TGS-6812), the other two its sensing element is a tin dioxide (SnO2) semiconductor (TGS-825 and TGS-2611) and the last one is an oxygen sensor (KE-50)), the remaining three sensors are auxiliary sensors for measuring a temperature and humidity (HTG–3535), and a pressure sensor (XFAM from Fujikura Ltd.). The proposed hardware–software system uses some least squares principles for classification and regression to identify at first a new gas sample, if it is pure or mixture, and then to estimate their concentrations, respectively. In particular we adopt a training model using the least squares approach to teach the system how to discriminate among different gases. Then we apply another training model using also the least squares but this time for regression, to predict their concentrations. The experimental results demonstrated that the proposed binary mixture of the organic vapor (Methane, Hydrogen, or Hexane) and toxic gas (Hydrogen Sulfide) classifier is effective in the identification of the tested gases. | ||
| Keywords | ||
| Electronic nose; Least square regression; Mixture of gases | ||
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