Application Study of Neural Networks in Ultrasonic Catalytic Gas Single Sensor
An ultrasonic catalytic gas sensor is a novel single gas sensor for identifying gas types and analyzing concentra-tion.Existing database recognition and K-value discrimination methods suffer from large calibration workloads and low recognition accuracy.Therefore,this paper proposed a neural network-based method for gas type and concentration recognition.Combining the data collected by the experiment,the optimal feature combination was chosen using methods like random forest(RF)and mutual information(MI).The neural network structure was designed to realize the gas type and concentration recognition.The experi-ment shows that the gas classification model achieves 96.88%accuracy in classifying methanol,ethanol,acetone and hydrogen at 1%to 20%lower explosion limit(LEL)concentration.Optimized by the whale algorithm,the classification model's accuracy im-proves to 97.82%.The gas concentration prediction model has prediction errors of 3.49%,2.5%,10.12%,and 9.76%for meth-anol,ethanol,acetone and hydrogen concentrations,respectively.The results show that the neural network can effectively perform gas classification and concentration recognition for ultrasonic catalytic gas single sensors.
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