基于TDLAS技术的CH4气体检测与温度补偿方法

CH₄气体的精准检测对防止矿井瓦斯爆炸，确保安全生产至关重要。目前基于可调谐半导体激光吸收光谱技术（TDLAS）存在因温度变化导致气体浓度测量误差较大。探究了基于TDLAS的CH₄气体检测系统与温度补偿方法，分析温度对CH₄气体吸收谱线的影响，通过算法补偿模型消除环境温度对CH₄气体检测的影响。依据TDLAS技术原理及相关理论，对系统发射单元、吸收池、信号接收单元、数据处理单元进行设计，搭建了基于TDLAS技术的CH₄气体浓度检测系统，实验检测了不同环境温度（10~50 ℃）时0.04%CH₄气体浓度，分析温度变化对CH₄气体在波长为1.653 μm处吸收谱线强度和半宽度的影响。为消除温度对CH₄气体检测的影响并提高补偿效果，采用粒子群优化算法（PSO）优化BP神经网络（BPNN）的最佳权值和阈值，建立CH₄气体的PSO-BP温度补偿模型，克服了BP神经网络收敛速度慢、易陷入局部最优的缺点。结果表明：（1）基于TDLAS的CH₄气体检测浓度随环境温度升高而下降，整个实验温度内相对误差范围为4.25%~12.13%，不同环境温度下CH₄气体检测浓度与温度之间的关系可用一元三次多项式表示；（2）CH₄气体的吸收强度和半宽度随着温度的升高而下降，与温度变化之间的关系为单调递减函数，温度对CH₄气体吸收谱线强度的相对变化率大于吸收谱线半宽度的相对变化率，CH₄气体吸收谱线的强度更容易受温度变化的影响；（3）BP神经网络和PSO-BP模型测试样本的绝对平均误差（MAE）分别为12.88%和1.81%，平均绝对百分比误差（MAPE）分别为2.3%和0.3%，均方根误差（RMSE）分别为15.96%和2.69%，相关系数R2分别为0.980 6和0.999 6。通过建立PSO-BP温度补偿模型，补偿效果大部分分布在±1.0%的误差范围内，MAE，MAPE，RMSE和R2等评价指标均大幅度提升，对提高TDLAS技术在矿井CH₄的精准检测具有一定的参考意义。

Research on CH4 Gas Detection and Temperature Correction Based on TDLAS Technology

Accurate detection of CH₄ is essential to prevent gas explosion and ensure safe production. However, the gas detection technology based on tunable diode laser absorption spectroscopy (TDLAS) has a large error due to temperature change. This paper explored the CH₄ detection based on TDLAS technology and the temperature compensation method, analyzed the impact of temperature on CH₄ absorption line, and finally eliminated the impact of environmental temperature on the CH₄ detection through algorithm compensation model. This study used TDLAS technology’s principle and theory to design the transmitter unit, absorption cell, signal receiver unit and data processing unit. A CH₄ detection system based on TDLAS technology was established, the concentration of CH₄ at different ambient temperatures (10~50 ℃) was measured, and the effect of temperature change on the intensity and half width at half-maximum of CH₄ absorption line at 1.653 μm was analyzed. In order to eliminate the influence of temperature on CH₄ detection and improve the compensation effect, the particle swarm optimization (PSO) was employed to optimize the optimal weight and the threshold of back propagation neural network (BPNN). The PSO-BP temperature compensation model of CH₄ was established, which overcame the characteristics of slow convergence rate and easy to fall into local optimum of the BPNN The result indicated that: (1) Based on TDLAS technology, the CH₄ detection concentration dropped with the increasement of ambient temperature, the relative error range within the whole experimental temperature was 4.25%~12.13%. The relationship between CH₄ detection concentration and temperature under different ambient temperatures can be expressed as a cubic polynomial; (2) The absorption intensity and half width at half-maximum of CH₄ gas decrease with the increase of temperature relationship between it and temperature was a monotonous decreasing function. The relative change rate of temperature on the absorption line intensity of CH₄ gas was greater than the half width. The absorption line intensity of CH₄ gas was more susceptible to the temperature change; (3) The absolute mean error (MAE) of the BPNN and PSO-BP model test samples were 12.88% and 1.81%, the mean absolute percentage error (MAPE) were 2.3% and 0.3%, the root mean square (RMSE) were 15.96% and 2.69%, and the correlation coefficient R2 were 0.980 6 and 0.999 6, respectively. By establishing the PSO-BP temperature compensation model, the compensation effect was mostly distributed within the error range of ±1.0%, and MAE, MAPE, RMSE, R2 and another evaluation indexes were greatly improved. It has a certain reference significance to improve the accurate detection of CH₄ in the mine with TDLAS technology.