Fusion Neural Network for Gas Concentration Prediction in Mixed Gas Environments

Abstract

Due to the inherent complexity and nonlinearity of mixed gas data, existing pattern recognition algorithms utilized in electronic noses often encounter difficulties in accurately predicting gas concentrations. Addressing this issue, we propose a fusion neural network that merges Long Short-Term Memory (LSTM) and Temporal Convolutional Network (TCN), which we denote as the LSTM-TCN fusion model. The LSTM module effectively captures long-term dependencies in time-series data, while the TCN targets local correlations, thereby enhancing the prediction accuracy for complex gas concentrations. Experimental validation was conducted using a mixed gas dataset comprising ethylene and carbon monoxide. When compared with traditional models, including LSTM, TCN, and GRU, the proposed LSTM-TCN model demonstrated superior performance, achieving an R2 value as high as 0.9922. This research holds considerable practical significance and shows promising application prospects, contributing novel insights and methods to the study and application of electronic nose technology.