A novel high accuracy fast gas detection algorithm based on multi-task learning

Abstract

As an advanced sensor system, electronic nose (E-nose) has been widely used in the field of gas analysis. A novel algorithm that leverages Long Short-Term Memory Attention as a shared framework and integrates it with multi-task learning (MTL-LSTMA) is proposed to enable concurrent prediction of gas category and concentration. Numerous experiments have demonstrated that the MTL-LSTMA model effectively integrates these tasks, fast and simultaneous gas detection for CO, ethylene, and methane gas was achieved (response time of 30 s). All of the classification accuracies exceed 0.98, and the concentration prediction task also exhibits a high degree to match actually. Additionally, we compared results at a variety of response times. It is revealed that MTL-LSTMA model is the best for type identification and concentration prediction of gas mixtures and achieves good results using only the first 30 s of response data.