A logarithmic multi-parameter model using gas sensor main and cross sensitivities to estimate gas concentrations in a gas mixture for SnO2 gas sensors

Chaiyboun, A.; Traute, Rüdiger; Haas, T. W.; Kiesewetter, Olaf; Doll, Theodor

doi:10.1016/j.snb.2006.11.012

Cited by 9 publications

(2 citation statements)

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“…Some authors have given some models of the response relation between the resistance of metal oxide semiconductor sensor and the concentration of measured gas [9,10], we contrast representative three models of Hirobayashi and Chaiyboun and not add e to C with our model, analyze their errors. Coefficients are figured out according to the former algorithm formula, and we analyze their errors by following …”

Section: Analysis and Conclusionmentioning

confidence: 99%

Characteristic analysis of gas detection basing on metal oxide semiconductor sensor

Zhang

2009

2009 International Conference on Industrial Mechatronics and Automation

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This paper analyzes gas detection characteristic of the metal oxide semiconductor sensor, and puts forward the logarithm model of detecting single gas basing on our experiment data, also we infer the analysis formula model of detecting mixture gas fetched from the single gas detection model. We compare the error of our model with the errors of some models proposed by other researchers, and also we propose some points for attention in the practical application.

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Section: Analysis and Conclusionmentioning

confidence: 99%

Characteristic analysis of gas detection basing on metal oxide semiconductor sensor

Zhang

2009

2009 International Conference on Industrial Mechatronics and Automation

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“…The first research question addressed in this study is to characterize the cross sensitivities of Figaro ® resistances to CH 4 versus other factors known to influence the tin-oxide resistance: carbon monoxide (CO), water vapor, pressure and temperature [13]. To address this question, we characterized in a laboratory facility under controlled conditions the resistances of six Figaro ® sensors for a range of CH 4 , CO, temperature and H 2 O, and examined covariance between their sensitivities, in a first step to diagnose cross-sensitivity effects from non-CH 4 related variables.…”

Section: Introductionmentioning

confidence: 99%

The Potential of Low-Cost Tin-Oxide Sensors Combined with Machine Learning for Estimating Atmospheric CH4 Variations around Background Concentration

et al. 2021

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Continued developments in instrumentation and modeling have driven progress in monitoring methane (CH4) emissions at a range of spatial scales. The sites that emit CH4 such as landfills, oil and gas extraction or storage infrastructure, intensive livestock farms account for a large share of global emissions, and need to be monitored on a continuous basis to verify the effectiveness of reductions policies. Low cost sensors are valuable to monitor methane (CH4) around such facilities because they can be deployed in a large number to sample atmospheric plumes and retrieve emission rates using dispersion models. Here we present two tests of three different versions of Figaro® TGS tin-oxide sensors for estimating CH4 concentrations variations, at levels similar to current atmospheric values, with a sought accuracy of 0.1 to 0.2 ppm. In the first test, we characterize the variation of the resistance of the tin-oxide semi-conducting sensors to controlled levels of CH4, H2O and CO in the laboratory, to analyze cross-sensitivities. In the second test, we reconstruct observed CH4 variations in a room, that ranged from 1.9 and 2.4 ppm during a three month experiment from observed time series of resistances and other variables. To do so, a machine learning model is trained against true CH4 recorded by a high precision instrument. The machine-learning model using 30% of the data for training reconstructs CH4 within the target accuracy of 0.1 ppm only if training variables are representative of conditions during the testing period. The model-derived sensitivities of the sensors resistance to H2O compared to CH4 are larger than those observed under controlled conditions, which deserves further characterization of all the factors influencing the resistance of the sensors.

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Long-term field calibration of low-cost metal oxide VOC sensor: Meteorological and interference gas effects

Hong

Lin

et al. 2023

Atmospheric Environment

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A logarithmic multi-parameter model using gas sensor main and cross sensitivities to estimate gas concentrations in a gas mixture for SnO2 gas sensors

Cited by 9 publications

References 11 publications

Characteristic analysis of gas detection basing on metal oxide semiconductor sensor

Characteristic analysis of gas detection basing on metal oxide semiconductor sensor

The Potential of Low-Cost Tin-Oxide Sensors Combined with Machine Learning for Estimating Atmospheric CH4 Variations around Background Concentration

Long-term field calibration of low-cost metal oxide VOC sensor: Meteorological and interference gas effects

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