A VMD-LSTNet-Attention model for concentration prediction of mixed gases.

Gases typically exist as mixed states, which normally contain more information and exhibit more intricate features than single gas. Hence, the task of predicting mixed gas concentrations poses a challenging endeavor in the field of gas detection. In this study, a Variational Mode Decomposition method was combined with Long and Short-term Time-series Network-Attention to build a VMD-LSTNet-Attention model, based on which the response signals of electronic nose (E-nose) is processed, achieving a high-precision concentrations prediction of carbon monoxide (CO) and ethylene mixed gases. Firstly, a dataset containing the raw response signals of CO and ethylene was collected using an gas data collection system. Then, VMD algorithm was used to extract and decompose the essential features from the E-nose signals into multiple components. Further, in pursuit of optimal decomposition results, Particle Swarm Optimization (PSO) algorithm was employed to optimize the penalty factor α and determine the most effective decomposition level K. Subsequently, the LSTNet-Attention model was employed to analyze the concentrations of mixed gases, and the predictive R-square values for CO and ethylene reach 0.993 and 0.975, respectively. Finally, the comparison with the traditional deep learning models and the popular models demonstrates the effectiveness of the proposed VMD-LSTNet-Attention model for concentration prediction of mixed gases. • A VMD algorithm was proposed to address the challenges in mixed gases analysis. • A mixed gases dataset was generated to validated the effectiveness of the model. • High-precision concentration prediction has been achieved by signal decomposition. [ABSTRACT FROM AUTHOR]