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Overcoming the Slow Recovery of MOX Gas Sensors through a System Modeling Approach
Department of System Engineering and Automation, University of Málaga, Campus de Teatinos, 29071 Málaga, Spain
Department of Engineering, University of Almería, 04120 La Cañada, Almería, Spain
* Author to whom correspondence should be addressed.
Received: 2 August 2012; in revised form: 2 October 2012 / Accepted: 5 October 2012 / Published: 11 October 2012
Abstract: Metal Oxide Semiconductor (MOX) gas transducers are one of the preferable technologies to build electronic noses because of their high sensitivity and low price. In this paper we present an approach to overcome to a certain extent one of their major disadvantages: their slow recovery time (tens of seconds), which limits their suitability to applications where the sensor is exposed to rapid changes of the gas concentration. Our proposal consists of exploiting a double first-order model of the MOX-based sensor from which a steady-state output is anticipated in real time given measurements of the transient state signal. This approach assumes that the nature of the volatile is known and requires a precalibration of the system time constants for each substance, an issue that is also described in the paper. The applicability of the proposed approach is validated with several experiments in real, uncontrolled scenarios with a mobile robot bearing an e-nose.
Keywords: metal oxide semiconductor sensor; mobile robotic olfaction; gas sensing; electronic nose
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Cite This Article
MDPI and ACS Style
Monroy, J.G.; González-Jiménez, J.; Blanco, J.L. Overcoming the Slow Recovery of MOX Gas Sensors through a System Modeling Approach. Sensors 2012, 12, 13664-13680.
Monroy JG, González-Jiménez J, Blanco JL. Overcoming the Slow Recovery of MOX Gas Sensors through a System Modeling Approach. Sensors. 2012; 12(10):13664-13680.
Monroy, Javier G.; González-Jiménez, Javier; Blanco, Jose Luis. 2012. "Overcoming the Slow Recovery of MOX Gas Sensors through a System Modeling Approach." Sensors 12, no. 10: 13664-13680.