利用机器学习实现H2S和CO2气体混叠光谱浓度预测OA

To address the spectral overlap between H2S and CO2 absorption lines and improve the accura-cy of online H2S measurements in flue gas,an H2S concentration prediction method based on machine learning algorithms is proposed.A comprehensive training dataset was constructed by integrating 580 sets of demodulated data from direct absorption spectra generated using the HITRAN database with experimen-tally acquired second-harmonic signal data.Several models,including Gaussian Process Regression(GPR),traditional linear regression,support vector machines,and neural networks,were employed to si-multaneously predict the concentrations of H2S and CO2.The results indicate that the GPR model achieved the best performance,with mean relative errors of 0.816%for H2S and 0.673%for CO2,outper-forming the other models.Long-term stability tests yielded root mean square errors of 14.181×10-6 for H2S and 0.101%for CO2,demonstrating excellent measurement stability.Overall,the proposed GPR-based machine learning approach effectively resolves the spectral interference between H2S and CO2,en-abling high-precision and stable monitoring of H2S,a key indicator of high-temperature corrosion in coal-fired boilers.This method provides a practical and reliable technical solution for real-time flue gas compo-nent analysis in industrial environments.