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Proceedings Paper

Improving selectivity of infrared gas analyzer with neural network
Author(s): Yonghuai Zhang; Junhua Liu
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Paper Abstract

Present applied research on how to improve selectivity of dispersive infrared gas analyzer (DIGA) is mostly confined to the improvement of hardware techniques with new structure, material and technology, which have insoluble deficiencies when non-aim gases bring forth cross absorption during the characteristic absorption spectrum of gas to be measured. The arithmetic of BP neural network can be used to eliminate the cross-interfere absorption and consequently improve selectivity of DIGA. When detecting methane in petroleum fission gas, measuring gas methane measurement scale is 0 to approximately 7600 x 10-6 and interfere gas ethene concentration varies 7600 X 10-6. After using neural network data fusion, the selectivity index of DIGA can be increased from 3.17 to 422 and the relevant fluctuation error of main sensor output decreased from 57.9% to 0.65%. The experimental result indicates the method has practical application value.

Paper Details

Date Published: 2 September 2003
PDF: 5 pages
Proc. SPIE 5253, Fifth International Symposium on Instrumentation and Control Technology, (2 September 2003); doi: 10.1117/12.522205
Show Author Affiliations
Yonghuai Zhang, Xi'an Jiaotong Univ. (China)
Junhua Liu, Xi'an Jiaotong Univ. (China)

Published in SPIE Proceedings Vol. 5253:
Fifth International Symposium on Instrumentation and Control Technology
Guangjun Zhang; Huijie Zhao; Zhongyu Wang, Editor(s)

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