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Application of artificial neural networks to a gas sensor-array database for environmental monitoring

TitleApplication of artificial neural networks to a gas sensor-array database for environmental monitoring
Publication TypeArticolo su Rivista peer-reviewed
Year of Publication2012
AuthorsTrizio, L., Brattoli M., De Gennaro G., Suriano Domenico, Rossi R., Alvisi Marco, Cassano Gennaro, Pfister Valerio, and Penza Michele
JournalLecture Notes in Electrical Engineering
Volume109 LNEE
ISBN Number9781461409342
KeywordsAir Pollutants, Carbon dioxide, Carbon nanotubes, Chemical analysis, Chemical sensing, Chemical sensors, Commercial sensors, Database systems, Environmental measurements, Environmental monitoring, Gas concentration, Gas detectors, Gases, Microsystems, Neural networks, Nitrogen oxides, Normalized mean square error, Optimal sets, Pollution, Sensor arrays, Sensors array, Sulfur dioxide, Tin, Tin dioxide

A sensors array based on two different types of chemical sensors such as tin dioxide commercial sensors and carbon nanotubes innovative sensors developed in the ENEA laboratories to monitor gases (e.g., CO, NO 2, SO 2, H 2S and CO 2) of relevance in polluted air has been analyzed. Measurements of chemical sensing of the sensors array have been performed in laboratory to create a database for applying artificial neural networks (ANNs) algorithms to quantify gas concentration of individual air pollutants and binary gas-mixture. A total number of 3,875 data-samples based on 413 distinct gas concentrations measured by 14 gas sensors has been used in the database. The ANN performance has been assessed for each targeted air-pollutant. The lowest normalized mean square error (NMSE) of 6%, 9% and 11% has been achieved for NO 2, SO 2 and CO 2, respectively. In the contrast, NMSE as high as 28% and 39% has been measured for CO and H 2S, respectively. The aim of this study is the selection of an optimal set of gas sensors in the array for enhanced environmental measurements of gas concentration in real-scenario. © 2012 Springer Science+Business Media, LLC.


cited By 0; Conference of 16th Conference on Italian Association of Sensors and Microsystems, AISEM 2011 ; Conference Date: 7 February 2011 Through 9 February 2011; Conference Code:88105

Citation KeyTrizio2012139