This paper proposes a method to estimate the concentration of
inflammable gases from transient response patterns which a semiconductor gas sensor shows under periodic heating conditions.
The procedure and effectiveness of the method were illustrated for five
selected gases of butane, hydrogen, LP gas, methane, and town gas. The
response patterns obtained were found to be well reproducible and
specific to the kinds of gases. Frequency analysis could be applied
easily to the response patterns because of their periodic
characteristics, allowing one to extract D.C. and A.C. components of
them by fast Fourier transform.
The A.C. components remained almost unchanged irrespective of the
variations of ambient temperature and/or humidity and gas concentration,
proving themselves to be adequate for the concentration-independent
discrimination of gases. The D.C. components, on the other hand,
depended largely on the variations of gas concentration, being useful
for the estimation of gas concentration.
It was shown that the discrimination of the five gases supported by a
three-layered back propagation neural network as well as the estimation
of their concentrations assisted by fuzzy inference were successfully
performed.
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