期刊
INSTRUMENTATION SCIENCE & TECHNOLOGY
卷 41, 期 6, 页码 608-618出版社
TAYLOR & FRANCIS INC
DOI: 10.1080/10739149.2013.816965
关键词
infrared; methane gas detection; NDIR; neural network; temperature and humidity compensation
资金
- New Century Talents Scheme Grant of China's Ministry of Education [NCET-09-0643]
- Fundamental Research Funds for the Central Universities
- Natural Science Foundation of China [51175416, 90923001]
- Program of Chang Jiang Scholars and Innovative Research Team in University [IRT1033]
The infrared absorption gas sensor detects CH4, CO, CO2, and other gases accurately and rapidly. However, temperature and humidity have a great impact on the gas sensor's performance. This article studied the response of an infrared methane gas sensor under different temperatures and humidity conditions. After analyzing the compensation methods, a back propagation neural network was chosen to compensate the nonlinear error caused by temperature and humidity. The optimal parameters of the neural network are reported in this article. After the compensation, the mean error of the gas sensor's output was between 0.02-0.08 vol %, and the maximum relative error dropped to 8.33% of the relative error before compensation. The results demonstrated that the back propagation neural network is an effective method to eliminate the influence of temperature and humidity on infrared methane gas sensors.
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