Classification and identification of mixed gases based on the combination of semiconductor sensor array with SSA-BP neural network

Abstract

In this paper, pure , , ZnO and Cu/ZnO gas sensitive materials were synthesized by a simple hydrothermal reaction and used to prepare a gas sensor array. The morphological structure and composition of the synthesized materials were characterized using SEM and XRD, respectively. The sensor array was combined with the back propagation neural network algorithm optimized by the sparrow search algorithm (SSA-BPNN) and applied to effectively identify the types of mixed toxic gases in the room, including formaldehyde, ammonia and xylene. The combination of sensor array with optimized neural network algorithms achieved a good classification result for gas mixture and the classification accuracy can reach 93.45% for different classes of mixtures composed of three gases (formaldehyde, ammonia, and xylene). Therefore, the sensor array combined with the SSA-BP algorithm in this study has done a good work in the qualitative identification of ternary gas mixtures and has some application potential.