Optimization of the Mixed Gas Detection Method Based on Neural Network Algorithm

Real-time mixed gas detection has attracted significant interest for being a key factor for applications of the electronic nose (E-nose). However, mixed gas detection still faces the challenge of long detection time and a large amount of training data. Therefore, in this work, we propose a feasible way to realize low-cost fast detection of mixed gases, which uses only the part response data of the adsorption process as the training set. Our results indicated that the proposed method significantly reduced the number of training sets and the prediction time of mixed gas. Moreover, it can achieve new concentration prediction of mixed gas using only the response data of the first 10 s, and the training set proportion can reduce to 60%. In addition, the convolutional neural network model can realize both the smaller training set but also the higher accuracy of mixed gas. Our findings provide an effective way to improve the detection efficiency and accuracy of E-noses for the experimental measurement.

Automated summary

This study proposes a feasible method to achieve low-cost and fast detection of mixed gases, using only partial response data of the adsorption process as the training set. The results show that the proposed method significantly reduces the number of training sets and the prediction time of mixed gas. Additionally, the convolutional neural network model can achieve both smaller training sets and higher accuracy of mixed gas.