GA-HIDMS-PSO-BPNN Model-Based Suppression of Cross-Interference in Absorption Spectra for Dual-Gas Sensing

Cross-interference from overlapping absorption spectra of different gases significantly limits the precision and stability of gas concentration detection using traditional absorption spectroscopy. This study proposes a novel model integrating the global search capability of the genetic algorithm (GA), the local optimization ability of the heterogeneous improved dynamic multiswarm particle swarm optimization (HIDMS-PSO), and the nonlinear modeling of the backpropagation neural network (BPNN), termed the GA-HIDMS-PSO-BPNN model, to solve the cross-interference problem in tunable diode laser absorption spectroscopy (TDLAS) for a dual-gas sensor detecting CH4 and CO. In a 1500 s stability test, the model demonstrates reliable performance, achieving standard deviations of 24.7753 ppm at 5000 ppm of CH4 and 0.3075 ppm at 30 ppm of CO. These results verify the effectiveness of the model in improving the accuracy and reliability of the dual-gas sensor. It provides a robust approach for maintaining high-precision concentration measurements under cross-interference conditions and offers an efficient solution for multicomponent gas analysis.