作为煤质评价的重要指标之一, 热值的快速、 准确测量对电厂燃煤锅炉的优化燃烧和经济运行至关重要。 采用激光诱导击穿光谱(LIBS)技术结合BP神经网络定量分析模型和聚类分析, 以35个煤粉样品作为研究对象进行热值的定量分析。 基体效应对LIBS光谱数据的显著影响, 针对基于某类煤粉样品所建立的定标曲线不能直接用于不同煤种的定量分析, 采用K-means聚类方法根据热值、 灰分、 挥发分把样品分为三类对训练集和预测集样品进行优化选择。 通过谱线强度和热值变量相关性分析, 同时考虑特征谱线的物理意义, 最终提取12条元素谱线的峰值强度作为输入参数, 建立BP神经网络模型对燃煤热值进行预测。 定标结果表明, 建立的神经网络模型具有良好的定量分析能力, 定标曲线拟合度R2为0.996, 热值预测值的相对误差低于3.42%, 多次重复测量的相对标准偏差在4.23%以内。 对聚类分析中3类样品具有不同的预测能力, 采用峰值强度作为输入参数时, 能够在一定程度上减弱试验参数波动和基体效应造成的影响。 定量分析结果的重复性和准确性可以通过对不同类别的煤种分别建立BP神经网络模型来进一步改善。 LIBS技术结合BP神经网络可以对煤粉热值进行定量分析, 在现场在线/快速检测领域具有很好的应用价值和潜力。

As a major indicator of coal property, the high accuracy and fast calorific value quantitative analysis plays a significant role in combustion efficiency and economic operation. Laser-induced breakdown spectroscopy (LIBS) was proposed to calibrate the calorific value of 35 coal samples combined with BP neural networks and cluster analysis. The calibration curve established on a certain type of coal samples can not be applied directly to the quantitative analysis for different types of coal considering the influence of matrix effect on the LIBS spectral data. Three different groups were classified with K-means clustering method according to the calorific value, ash content and volatile matter. The training set and the prediction set were optimized. Based on the correlation analysis of analytical line intensity and calorific value, taking into account the physical meaning of the analytical line, the peak intensity of 12 elements was taken as the input parameter of BP neural network model which was established for calorific value analysis of coal samples. The performance of the BP network and the comparison result of 3 different groups samples were studied. The result indicate that the R2 value of calibration curve is 0.996, relative error (RE) and the relative standard deviation (RSD) of calorific value is less than 3.42% and 4.23%, respectively, performing good results of repeated measurement. The calibration curve has different prediction ability for three group coal samples. The influence of experimental parameter fluctuation and matrix effect was reduced in a certain degree using peak intensity as the input parameter. The repeatability and accuracy of quantitative analysis results can be further improved by establishing BP neutral network for various type of coal samples specifically. The analytical results of calorific value based on LIBS technology combined with BP neural network were well predicted, which was potentially proven as a promising technologyy for fast on-line analysis.