Characterising Methane Gas and Environmental Response of the Figaro Taguchi Gas Sensor (TGS) 2611-E00
- 1Laboratoire des Sciences du Climat et de l’Environnement (CEA-CNRS-UVSQ), Institut Pierre-Simon Laplace, Université Paris-Saclay, Site de l’Orme des Merisiers, 91191 Gif-sur-Yvette, France
- 2SUEZ Smart Solutions, 15-27 Rue de Port, 92000 Nanterre, France
Abstract. In efforts to improve methane source characterisation, networks of cheap high frequency in situ sensors are required, with a parts-per-million level methane mole fraction ([CH4]) precision. Low-cost semiconductor-based metal oxide sensors, such as the Figaro Taguchi Gas Sensor (TGS) 2611-E00, may satisfy this requirement. The resistance of these sensors decreases in response to the exposure of reducing gases, such as methane. In this study, we set out to characterise the Figaro TGS 2611-E00, in efforts to eventually yield [CH4] when deployed in the field. We found that different gas sources, containing the same ambient 2 ppm [CH4] level, yielded different resistance responses. For example, synthetically generated air containing 2 ppm [CH4] produced a lower sensor resistance than 2 ppm [CH4] found in natural ambient air, due to possible interference from supplementary reducing gas species in ambient air, though the specific cause of this phenomenon is not clear. TGS 2611-E00 carbon monoxide response is small and incapable of causing this effect. For this reason, ambient laboratory air was selected as a testing gas standard, to naturally incorporate such background effects into a reference resistance. Figaro TGS 2611-E00 resistance is sensitive to temperature and water mole fraction ([H2O]). Therefore, a reference resistance using this ambient air gas standard was characterised for five sensors (each inside its own field logging enclosure) using a large environmental chamber, where logger enclosure temperature ranged between 8 °C and 38 °C and [H2O] ranged between 0.4 % and 1.9 %. [H2O] dominated resistance variability in the standard gas. A linear [H2O] and temperature model fit was derived, resulting in a root-mean-squared error (RMSE) between measured and modelled resistance in standard gas of between ±0.4 kΩ and ±1.0 kΩ for the five sensors, corresponding to a fractional resistance uncertainty of less than ±3 % at 25 °C and 1 % [H2O]. The TGS 2611-E00 loggers were deployed at a landfill site for 242 days before and 96 days after sensor testing. Yet the standard (i.e ambient air) reference resistance model fit based on temperature and [H2O] could not replicate resistance measurements made in the field, where [CH4] was mostly expected to be close to the ambient background, with minor enhancements. This field disparity may have been due to variability in sensor cooling dynamics, a difference in ambient air composition during environmental chamber testing compared to the field or variability in natural sensor response, either spontaneously or environmentally driven. Despite difficulties in replicating a standard reference resistance in the field, we devised an excellent methane characterisation model up to 1 000 ppm [CH4], using the ratio between measured resistance with [CH4] enhancement and a reference resistance in standard gas. A bespoke power-type fit between resistance ratio and [CH4] resulted in a RMSE between modelled and measured resistance ratio of no more than ±1 % Ω Ω−1 for the five sensors. This fit and it corresponding fit parameters were then inverted and the original resistance ratio values were used to derive [CH4], yielding an inverted model [CH4] RMSE of less than ±1 ppm, where [CH4] was limited to 28 ppm. Our methane response model allows other reducing gases to be included if necessary, by characterising additional model coefficients. Our model shows that a 1 ppm [CH4] enhancement above the ambient background results in a resistance drop of between 1.4 % and 2.0 %, for the five tested sensors. With future improvements in deriving a standard reference resistance, the TGS 2611-E00 offers great potential in measuring [CH4] with a parts-per-million precision.
Adil Shah et al.
Adil Shah et al.
Adil Shah et al.
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