Identification of gas mixtures via sensor array combining with neural networks

In this work, a sensor array comprised four sensors has been employed to detect 11 types of mixtures of nitrogen dioxide (NO2) and carbon monoxide (CO), with concentration varying from 0 to 50 ppm. To reduce the effect of sensor noise and ensure high recognition accuracy, average resistance over a period of time was introduced. Then, 12 features including response value, response time and recovery time were extracted from each sample. After that, C-means clustering and back propagation neural network (BPNN) were performed to identify various gases, with classification accuracy of 94.55 % and 100 %, respectively. Genetic algorithm (GA) was also employed to further improve BPNN’s performance. Moreover, a random variable substitution method has been introduced to study which feature of the input sample influence the BPNN model most. Through gray processing, dynamic curves have been transformed into gray images, from which convolutional neural network (CNN) was introduced to automatically extract high-level features, and an identification accuracy of 100 % has been realized. Finally, experiments for sensing gas mixtures of CO and NO2 under various humidity levels have been done to test the impact of humidity on the sensor array. The results demonstrated the proposed method could eliminate the effects of humidity.