A Gas Mixture Prediction Model Based on the Dynamic Response of a Metal-Oxide Sensor

• Published in: Micromachines (Basel) Vol. 10; no. 9; p. 598
• Main Authors: Wen, Wei-Chih, Chou, Ting-I, Tang, Kea-Tiong
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 11.09.2019; MDPI
• Subjects: Accuracy; Binary mixtures; Catalytic oxidation; Classification; Dynamic response; Ethanol; Gas mixtures; Gas sensors; Gases; Metal oxides; metal-oxide sensors; Methods; mixture gas prediction; Polynomials; Prediction models; Selectivity; Sensors; Square waves; temperature modulation; Waveforms; United States > US; metal-oxide sensors; mixture gas prediction; temperature modulation; dynamic response
• ISSN: 2072-666X; 2072-666X
• DOI: 10.3390/mi10090598

Metal-oxide (MOX) gas sensors are widely used for gas concentration estimation and gas identification due to their low cost, high sensitivity, and stability. However, MOX sensors have low selectivity to different gases, which leads to the problem of classification for mixtures and pure gases. In this study, a square wave was applied as the heater waveform to generate a dynamic response on the sensor. The information of the dynamic response, which includes different characteristics for different gases due to temperature changes, enhanced the selectivity of the MOX sensor. Moreover, a polynomial interaction term mixture model with a dynamic response is proposed to predict the concentration of the binary mixtures and pure gases. The proposed method improved the classification accuracy to 100%. Moreover, the relative error of quantification decreased to 1.4% for pure gases and 13.0% for mixtures.