In this paper, a new
processing sensor data method base on neural networks and principal component
analysis block is presented in order to identify the gas type and to estimate
the gas concentration. Three gases in thirteen different concentrations have
been examined including methanol, ethanol, and 2-propanol. For temperature
modulation, the stair-case voltage was applied to the sensor heater at spans of
40s in 200s. In each of the obtained curves, at any span, transient and steady
state responses were recorded. These recorded properties are analyzed using the
usual methods of pattern recognition. Principal component analysis was used to
increase the selectivity of the sensor and the neural network was used to
recognize the type and estimate the gas concentration. In this study, we have
achieved the separation of gases successfully as well as average estimation
error concentration was calculated to be 0.00358%.
No comments:
Post a Comment