Tao, Mei and Seals, R. C. (1993) Multicomponent gas-mixture measurements using an array of gas sensors and an artificial neural-network. Journal of Microcomputer Applications, 16 (2). pp. 203-210. (doi:https://doi.org/10.1006/jmca.1993.1018)

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Abstract

An Electronic Nose is being jointly developed between the University of Greenwich and the Institute of Intelligent Machines to detect the gases given off from an oil filled transformer when it begins to break down. The gas sensors being used are very simple, consisting of a layer of Tin Oxide (SnO2) which is heated to approximately 640 K and the conductivity varies with the gas concentrations. Some of the shortcomings introduced by the commercial gas sensors available are being overcome by the use of an integrated array of gas sensors and the use of artificial neural networks which can be 'taught' to recognize when the gas contains several components. At present simulated results have achieved up to a 94% success rate of recognizing two component gases and future work will investigate alternative neural network configurations to maintain this success rate with practical measurements.

Item Type:	Article
Additional Information:	The Journal of Microcomputer Applications is now the Journal of Network and Computer Applications ISSN: 1084-8045
Uncontrolled Keywords:	Electronic Nose, multi-component gas mixture measurements, gas sensors, artificial neural network,
Subjects:	T Technology > TA Engineering (General). Civil engineering (General)
Pre-2014 Departments:	School of Engineering School of Engineering > Department of Engineering Systems
Related URLs:	Organisation
Last Modified:	02 Oct 2019 14:50
URI:	http://gala.gre.ac.uk/id/eprint/1513

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