对于多组分混合气体定量分析而言, 基于特征光谱的定量分析技术具有不可比拟的优势, 而定量检测效率与精度取决于其采用的光谱数据处理算法的优劣。 优化光谱分析算法参数与改进光谱数据处理方式是提高定量分析速度与精度的重要手段。 针对井下多组分气体定量分析建模过程中支持向量机(SVM)参数难以确定, 并且随组分数增多而呈指数增长的光谱数据运算量的问题, 提出了一种改进型粒子群优化-支持向量机(PSO-SVM)算法。 该算法主要针对多组分气体混合光谱数据量大, 光谱特征信息存在交叠的问题进行研究。 通过粒子变异约束PSO算法的收敛路径, 再通过粒子信息共享提高模型优化效率, 最后利用设置动态不敏感区提高模型精度。 设计了一种井下多组分气体快速定量检测系统。 该系统由CPU控制信号调制模块驱动红外光源, 信号光经过滤尘除湿后的气室照射在探测器上。 在压力与温度传感器补偿的基础上, 由信号处理模块将探测得到的光信号量化传入CPU, 最终, 结合改进型PSO-SVM算法实现各组分气体浓度的定量分析。 在完成井下实际样气采集、 预处理的基础上, 对浓度范围0～100%的CH4和浓度范围0～10%的C2H6, C3H8, SO2和CO2共5种组分的混合气体进行了测试, 获得了800组红外光谱数据, 其中训练集400组, 验证集400组。 采用SVM建立了多组分气体的定量分析模型, 利用改进型PSO对SVM中的参数进行了优化, 并将获得的最优参数重建了定量分析模型。 对采集的红外光谱数据分别由本算法与传统BP网络算法进行各组分气体浓度反演, 实验结果显示, 由于变异粒子对其产生的约束, 使最优值收敛范围变小, 从而提高了收敛速度, 该算法建模时间仅为传统方法的1/10; 由于通过气体光谱特性给出不敏感区, 使特征光谱计算时交叉敏感效率降低, 从而提高了模型预测的准确度, 平均误差约为传统方法的1/5。 由此可见, 该算法在全局优化及快速收敛方面得到了显著提升, 改进型PSO结合SVM用于井下多组分气体定量分析是可行的。 改进型PSO-SVM算法对于多组分气体混合红外光谱数据的分离具有很好的适用性, 其有一定的实际应用价值。

For quantitative analysis of multi-component gas mixtures, there are incomparable advantages for the quantitative analysis technology of characteristic spectrum. However, the efficiency and accuracy of quantitative detection depends on the capabilities of the spectral data processing algorithms. Optimizing the parameters of spectral analysis algorithms and improving the processing of spectral data are important means to improve the speed and accuracy of quantitative analysis. According to the problem in selecting parameter of support vector machine(SVM) when detecting quantitatively the concentration of multi-component gas in underground mine, an improved Particle Swarm Optimization-Support Vector Machine (PSO-SVM) algorithm was proposed. The algorithm is mainly used to research the problem that the multi-component gas mixed spectrum data is large and the spectral feature information overlaps. The algorithm constrains the convergence path of the PSO algorithm through particle variation, and it improves model optimization efficiency through particle information sharing, and it uses the setting of dynamic insensitive areas to improve model accuracy. A rapid quantitative detection system for multi-component gas was designed in underground mine. The infrared light source was drived by signal modulation module controlled by PC, and the signal light was irradiated on the detector by the air chamber with dust and steam filter. On the basis of the pressure and temperature sensor compensation, the detected optical signals were transmitted to the CPU by signal processing module. Finally, quantitative analysis of the gas concentrations for the various components was achieved by the improved PSO-SVM algorithm. On the basis of the actual sample gas collection and pretreatment in the underground, five kinds of gas components of CH4, C2H6, C3H8, SO2 and CO2 were tested. The concentration range of CH4 was 0～10%, and the concentration range of other gases was 0～10%. Infrared spectral data of these five gases were collected with Fourier infrared spectrometer. 800 groups of these gases were divided into 400 groups for calibration set and 400 groups for validation set. The quantitative analysis model of multi-component gas was established by SVM. The parameters of SVM were optimized by improved PSO, and the quantitative parameters were reconstructed by the obtained optimal parameters. The infrared spectral data collected by the algorithm and the traditional BP network algorithm were used to invert the gas concentration of each component. The experimental results show that the convergence range of the optimal value is reduced due to the constraint of the mutated particle, which improves the convergence speed. The modeling time of the algorithm is only 1/10 of that of the traditional method; Since the insensitive area is given by the spectral characteristics of the gas, the cross-sensitivity effect of the characteristic spectrum is reduced, which improves the prediction accuracy of the model. It improves the accuracy of model predictions, with an average error of about 1/5 of traditional methods. It is feasible to use improved PSO combined with SVM for quantitative analysis of multi-component gas in underground. The improved PSO-SVM algorithm has good applicability for the separation of multi-component gas mixed infrared spectral data, and it has certain practical application value.