The application of laser-induced breakdown spectroscopy (LIBS) for directly measuring coal particle flow is an optimal choice for the actual industrial operations. With the objective of facilitating the detection of particle flow, we established a LIBS detection system coupled with the coal particle circulation bench, which can continuously and automatically provide particle flow samples for laser ablation. A quantitative analysis method for particle flow combining feature-based transfer learning was proposed, so a dual-mode optical LIBS module was designed and integrated into this system to obtain the spectral signals from different forms of coal samples (pellet and particle flow) through the same optical configuration. The spectral characteristics and the correlation between pellet and particle flow were firstly analyzed. Then a spectral correction method based on polynomial fitting was proposed to enhance the correlation between the pellet spectra and particle flow spectra. Finally, the feature space mapping method was introduced for improving the effect of feature transfer, and the model was trained on highly stable pellet spectra to perform a direct quantitative analysis of coal particle flow. The results demonstrated that the root mean square error (RMSE) for the analysis of calorific value, volatile matter, and ash content of particle flow was 0.757 MJ/kg, 2.630 %, and 3.034 %, respectively. This work provides a practical application scheme for on-line analysis of coal particle flow.

中文翻译：

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基于颗粒煤校准样品和基于特征的迁移学习，通过激光诱导击穿光谱在线分析煤颗粒流


激光诱导击穿光谱 （LIBS） 用于直接测量煤颗粒流的应用是实际工业作的最佳选择。以方便颗粒流检测为目的，我们建立了结合煤颗粒循环工作台的 LIBS 检测系统，该系统可以连续自动提供颗粒流样本进行激光烧蚀。提出了一种结合基于特征的迁移学习的颗粒流定量分析方法，因此设计了双模光学 LIBS 模块并将其集成到该系统中，以通过相同的光学配置获得来自不同形式的煤样（颗粒和颗粒流）的光谱信号。首先分析了颗粒与颗粒流的光谱特性和相关性。然后，提出了一种基于多项式拟合的光谱校正方法，以增强颗粒光谱与颗粒流光谱之间的相关性。最后，引入特征空间映射方法以提高特征转移的效果，并在高度稳定的球团光谱上训练模型，对煤颗粒流进行直接定量分析。结果表明，颗粒流热值、挥发分和灰分含量分析的均方根误差 （RMSE） 分别为 0.757 MJ/kg、2.630 % 和 3.034 %。本工作为煤颗粒流在线分析提供了实际应用方案。