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The interference between overlapping gas absorption lines often occurs in the measurement of multi-component gas mixture with using tunable diode laser absorption spectroscopy (TDLAS). This is also the main problem of the technology in some applications. For instance, in the early application of multi-component gas mixture measurement in coal mines, we found that the absorption lines of carbon monoxide (CO) and methane (CH4) seriously overlapped. The absorption signal of trace CO gas was annihilated and could not be effectively demodulated, especially in the presence of high concentration of CH4. This problem could not be solved just by accurately selecting the spectral lines due to the band absorption of CH4. Therefore, in this paper, we introduce the support vector regression (SVR) model to deal with the interference between CO and CH4 absorption lines. The spectral signals of 14 groups of mixed gases with different concentrations of CO and CH4 are used as the training sets, and the five-fold cross-validation is adopted to prevent the model from overfitting. After 15 iterations in 30 seconds, the optimal regression model of CO and CH4 can be obtained respectively. Furthermore, it is worth noting that based on the experimental data, the linear kernel function is selected to construct the two gas SVR models, and the parameters of the SVR models are optimized by the sequential minimal optimization(SMO) algorithm. With the assistance of the SVR models, the absorption spectra of the two gases can be demodulated effectively, and finally the accurate measurement results are obtained. The measurement results show that the absolute error of trace CO and CH4 concentration(volume fraction of gas) are less than 2 × 10–6 and 0.2 × 10–2 respectively. Meanwhile, the correlation coefficient between the measured values and the actual values of CO and CH4 are 0.998 and 0.9995, respectively. In addition, the dynamic stability for each of the two regression models is fully verified by the experiment of the inflation process. Consequently, this method can eliminate the interference between the overlapping spectra, and can fully meet the requirements for accurately measuring the gas mixture. We hope that the SVR model can provide an effective solution for the real-time monitoring of multi-component gas mixture, and thus greatly improving the adaptability of TDLAS technology in the future.

Tunable diode laser absorption spectroscopy (TDLAS) is often used to detect gas concentrations in many fields. But because of the variation of ambient temperature, the measured harmonic signal amplitudes are affected and may lead to the monitoring errors. The impact of temperature on the measurement as well as the temperature compensation method is emphasized. So in order to modify the inversion results and adapt industrial measurement, combustion diagnostics, the numerical fitting empirical modified equation and theoretical modified equation from HITRAN database are discussed and compared in this paper. In experiment, the 21% oxygen as the safety monitoring gas and absorption wavelength at 760.77 nm are employed. Meanwhile, the first harmonic signals are also used to decrease the laser intensity fluctuations. We thus obtained the unmodified and modified results with the tube furnace in the temperature range 300–900 K (interval 50 K). Experimental results show that these two modified methods have some effective influence on the temperature changes and can be applied to gas monitoring correction to improve the accuracy and feasibility of the TDLAS technology. In addition, the methods also provide evidence for the real-time gas monitoring in the application of combustion diagnosis.