Gas Identification Algorithm Based on Dynamic Response Analysis of Metal Oxide Sensors Under Temperature Modulation

Many odors, such as poisonous and exhaust gases, are unsuitable for human detection. Therefore, techniques for predicting gas types and concentrations are essential. The metal oxide sensor utilized in this study is a standard gas sensor due to its excellent stability, affordability, and high sensitivity. However, it does have a drawback: lower selectivity towards different gases. To address this, we employ temperature modulation to enhance the sensor’s selectivity. By employing temperature modulation, we can observe the dynamic response of the sensor and capture more features. We utilize low-frequency square wave and triangular wave signals as heating voltages. The former represents rapid heating, while the latter embodies a slower heating process. The objective is to employ these two methods to classify and predict the concentrations of ethanol, methanol, MEK, and ethyl acetate, as well as mixtures of the four gases. Following feature extraction, a neural network is employed for classifying and predicting gas types and concentrations. The results demonstrate a 100% classification accuracy under two different heating voltages. Regarding concentration prediction, using the square wave alone yields an error range of +42.09 to -32.41 and a root mean square error (RMSE) of 4.68%; utilizing the triangular wave alone yields an error range of +13.86 to -18.10 and an RMSE of 4.33%. Employing both waveform types simultaneously results in an error range of +37.97 to -17.48 and an RMSE of 2.91%.