TDLAS Spectral Line Identification and Quantification Based on Hill-Climbing Algorithm with Progressive Window Expansion

The field application environment of TDLAS gas sensors is inherently complex, and the acquired spectral signals are frequently contaminated by interfering backgrounds. Such interferences compromise the measurement accuracy of gas concentration. To address this issue, this paper proposes an approach for gas characteristic spectral line extraction and quantitative analysis based on the hill-climbing algorithm with progressive window expansion. In this approach, the full measurement range of the TDLAS sensor is divided into segments, and the characteristic spectral line parameters corresponding to a known concentration are pre-stored in each segment. The proposed hill-climbing algorithm with progressive window expansion is then employed to estimate the spectral parameters. In the comparative experiments, using the same TDLAS sensor subject to nonlinear laser drift and a contaminated gas cell, the proposed method and the existing traditional method were respectively employed to process the same raw spectral data acquired by the sensor and to detect the same batch of methane gases at different concentrations. Compared with the traditional method, the proposed method improves the fitting correlation coefficient of spectral background prediction from 0.9985 to 0.9997, and enables more accurate gas concentration prediction simultaneously while extracting the gas characteristic absorption lines. The prediction errors of the proposed method for all concentrations fall within the allowable error limits for methane sensors in Chinese coal mines (i.e., absolute error ≤ 0.05% for the 0–1% range, and relative error ≤ 5% of the actual value for the 1–100% range). In the 6 h detection of the 0.05% methane gas sample, according to the statistics, the average detection value of the proposed method is 0.0491%, which is very close to the true detection value of 0.05%, and the detection variance is only 0.0124%. The comparative experimental results show that the proposed method can successfully extract the gas characteristic lines from raw spectral signals contaminated by interfering backgrounds, while achieving accurate concentration prediction, and its concentration prediction error is significantly lower than that of the conventional method. The proposed method enriches the technical arsenal for spectral signal analysis in TDLAS gas sensors, offers a valuable reference for designing spectral analysis strategies when such sensors are deployed in industrial field applications, and contributes, to a certain extent, to enhancing the sensor’s reliability and stability.