Abstract
Metal oxide (MOX) sensors are widely used for chemical sensing due to their low cost, miniaturization, low power consumption and durability. Yet, getting instantaneous measurements of fluctuating gas concentration in turbulent plumes is not possible due to their slow response time. In this paper, we show that the slow response of MOX sensors can be compensated by deconvolution, provided that an invertible, parametrized, sensor model is available. We consider a nonlinear, first-order dynamic model that is mathematically tractable for MOX identification and deconvolution. By transforming the sensor signal in the log-domain, the system becomes linear in the parameters and these can be estimated by the least-squares techniques. Moreover, we use the MOX diversity in a sensor array to avoid training with a supervised signal. The information provided by two (or more) sensors, exposed to the same flow but responding with different dynamics, is exploited to recover the ground truth signal (gas input). This approach is known as blind deconvolution. We demonstrate its efficiency on MOX sensors recorded in turbulent plumes. The reconstructed signal is similar to the one obtained with a fast photo-ionization detector (PID). The technique is thus relevant to track a fast-changing gas concentration with MOX sensors, resulting in a compensated response time comparable to that of a PID.
Highlights
Air pollution is a major problem affecting the health of people, leading to 4 million deaths each year [1]
By transforming the sensor signal in the log-domain, the system becomes linear in the parameters and these can be estimated by the least-squares techniques
We demonstrate its efficiency on metal oxide (MOX) sensors recorded in turbulent plumes
Summary
Air pollution is a major problem affecting the health of people, leading to 4 million deaths each year [1]. Metal oxide (MOX) gas sensors are miniaturized and inexpensive, with a cost of ~10 USD, which is compatible with a wide-scale deployment They are sensitive to the volatile organic compounds (VOCs) relevant to environmental monitoring. The identification can be performed in a supervised way from a training signal; that is, a reference of the fluctuating gas concentration in turbulent plumes provided by a fast photo-ionization detector (PID). This supervised approach is unsuitable in certain applications as retraining with the PID must be performed regularly to adapt to changing sensor characteristics (e.g., to overcome the drift due to contamination or poisoning of the sensing material) and environmental conditions (e.g., changes in humidity and temperature).
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