燃煤工业指标的在线精确分析对于指导燃煤工业优化生产、 降低燃煤煤耗至关重要。 利用激光诱导击穿光谱(LIBS)分析燃煤煤质时, 因受我国复杂多样煤种所导致的“基体效应”, 测量精度有待提高。 实验中对激光诱导燃煤等离子体光谱至燃煤工业分析指标转化过程中的光谱预处理和定标建模方法进行了优化选择。 实验结果表明, 利用单/多峰Lorentzian光谱拟合计算谱线强度相比于传统计算方法, 谱线强度RSD均值可由12.1%降至9.7%; 对于核函数参数寻优, 相比于网格参数(Grid)和遗传算法(GA), 粒子群算法(PSO)的平均绝对误差(MAE)最小; 采用PSO参数寻优式支持向量机(SVM)回归建模的预测均方根误差(RMSEP)小于偏最小二乘回归分析法(PLS); 采用单/多峰Lorentzian光谱拟合方法和PSO参数寻优式SVM回归建模, 对燃煤工业分析指标预测的平均绝对误差(AAE)为: 灰分为16%～30%时AAE为1.37%, 灰分大于30%时AAE为1.77%, 发热量为9～24 MJ·kg-1时AAE为0.65 MJ·kg-1, 挥发分低于20%时AAE为1.09%, 挥发分大于20%时AAE为1.02%。

Online accurate proximate analysis of coal is vitally important to the optimization of industrial production and reduction in coal consumption. However, due to the “matrix effect” caused by the complex and diverse coal species in China, the measurement accuracy needs to be improved by using laser-induced breakdown spectroscopy (LIBS). In our experiment, both the spectral pretreatment method and the calibration model for the conversion of laser induced coal plasma spectra to the coal proximate analysis results were optimized. Experimental results showed that, compared with the traditional method, the proposed single- or multiple-peak Lorentzian spectral fitting for spectral line intensity calculation reduced the mean RSD from 12.1% to 9.7%. For kernel function parameters optimization, the mean absolute error (MAE) of the particle swarm optimization (PSO) was smaller than that of the grid parameter (Grid) and the genetic algorithm (GA). The root mean square error (RMSEP) of support vector machine (SVM) regression model based on PSO parameter optimization was less than that of partial least squares regression (PLS). By combining the single- or multiple-peak Lorentzian spectral fitting method with the PSO based SVM for regression modeling, the average absolute errors (AAE) of predicted proximate analysis results were certified to be: 1.37% for coal ash content of 16%~30%, 1.77% for coal ash content of 30% or more, 0.65 MJ·kg-1 for calorific value of 9~24 MJ·kg-1, 1.09% for volatile matter of 20% or less, and 1.02% for volatile matter of 20% or more.