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Novel Method of Temperature Modulation for Enhancing Catalytic Gas Sensor Selectivity

Catalytic gas sensors are among the most widespread gas sensors for combustible gas concentration measurements. However, their selectivity is low. In this research, the results of machine learning techniques application to enhance catalytic gas sensor selectivity are presented. The measurements of sensor signal are performed using the multistage heat pulse method described in our previous works. Contrary to the previous works, the number of heating stages was increased from 2 to 55, which corresponds to the heating voltage range of 125 m V to 1.5 V with a 25 m V step. This change enriches sensor signal with information about gas compositions. Methane and vapors of acetone, ethanol and gasoline are used as target gases. A support vector machine method is used to train two models. The first one was trained based on the plain normalized data. It was used for a microcontroller implementation of the method. The second model used the data transformed by principal component analysis technique. This model was used to visualize the method proposed. The results show that the application of proposed method allows to identify gases by single catalytic sensor. These principles can be used to design selective gas detectors which will react only to target gases.