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Pattern recognition and anomaly detection by self-organizing maps in a multi month e-nose survey at an industrial site

Licen S.
•
Di Gilio A.
•
Palmisani J.
altro
Barbieri P.
2020
  • journal article

Periodico
SENSORS
Abstract
Currently people are aware of the risk related to pollution exposure. Thus odor annoyances are considered a warning about the possible presence of toxic volatile compounds. Malodor often generates immediate alarm among citizens, and electronic noses are convenient instruments to detect mixture of odorant compounds with high monitoring frequency. In this paper we present a study on pattern recognition on ambient air composition in proximity of a gas and oil pretreatment plant by elaboration of data from an electronic nose implementing 10 metal-oxide-semiconductor (MOS) sensors and positioned outdoor continuously during three months. A total of 80,017 e-nose vectors have been elaborated applying the self-organizing map (SOM) algorithm and then k-means clustering on SOM outputs on the whole data set evidencing an anomalous data cluster. Retaining data characterized by dynamic responses of the multisensory system, a SOM with 264 recurrent sensor responses to air mixture sampled at the site and four main air type profiles (clusters) have been identified. One of this sensor profiles has been related to the odor fugitive emissions of the plant, by using ancillary data from a total volatile organic compound (VOC) detector and wind speed and direction data. The overall and daily cluster frequencies have been evaluated, allowing us to identify the daily duration of presence at the monitoring site of air related to industrial emissions. The refined model allowed us to confirm the anomaly detection of the sensor responses.
DOI
10.3390/s20071887
WOS
WOS:000537110500083
SCOPUS
2-s2.0-85082792187
Archivio
http://hdl.handle.net/11368/2966617
https://www.mdpi.com/1424-8220/20/7/1887
Diritti
open access
FVG url
https://arts.units.it/bitstream/11368/2966617/1/sensors-20-01887.pdf
Soggetti
  • Ambient air

  • Electronic nose

  • Neural networks

  • Oil and gas

  • Self-organizing map

Web of Science© citazioni
13
Data di acquisizione
Mar 27, 2024
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