An array of commercial gas sensors and nanotechnology sensors has been integrated to quantify gas concentration of air-pollutants. A variety of chemoresistive gas sensors, commercial (Figaro and Fis) and developed at ENEA laboratories (metal-modified carbon nanotubes) were tested to implement a database useful for applied artificial neural networks (ANNs). The ANN algorithm used is the common perceptron multi-layer feed-forward network based on error back-propagation. Electronic Noses based on various sensor arrays related to mammalian olfactory systems have been largely reported [1,2]. Here, we reported on the perceptron-based ANNs applied to a large database of 3875 datapoints for environmental air monitoring. The ANNs performance has been individually assessed for any targeted gas. The response of the classifier has been measured for NO2, CO, CO2, SO2, and H2S gas. The NO2 characteristics exhibit that real concentrations and predicted concentrations are very close with a normalized mean square error (NMSE) in the test set as low as 6%.
A gas sensor array for environmental air monitoring: A study case of application of artificial neural networks
DE GENNARO, GIANLUIGI
2011-01-01
Abstract
An array of commercial gas sensors and nanotechnology sensors has been integrated to quantify gas concentration of air-pollutants. A variety of chemoresistive gas sensors, commercial (Figaro and Fis) and developed at ENEA laboratories (metal-modified carbon nanotubes) were tested to implement a database useful for applied artificial neural networks (ANNs). The ANN algorithm used is the common perceptron multi-layer feed-forward network based on error back-propagation. Electronic Noses based on various sensor arrays related to mammalian olfactory systems have been largely reported [1,2]. Here, we reported on the perceptron-based ANNs applied to a large database of 3875 datapoints for environmental air monitoring. The ANNs performance has been individually assessed for any targeted gas. The response of the classifier has been measured for NO2, CO, CO2, SO2, and H2S gas. The NO2 characteristics exhibit that real concentrations and predicted concentrations are very close with a normalized mean square error (NMSE) in the test set as low as 6%.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
