Accuracy Enhancement of LIBS-XRF Coal Quality Analysis Through Spectral Intensity Correction and Piecewise Modeling - Bai, Yu; Li, Jiaxuan; Zhang, Wanfei; Zhang, Lei; Hou, Jiajia; Zhao, Yang; Chen, Fei; Wang, Shuqing; Wang, Gang; Ma, Xiaofei; Liu, Zhenrong; Luo, Xuebin; et al. (Frontiers in Physics, 2022)
The combination of laser-induced breakdown spectroscopy (LIBS) and energy dispersive X-ray fluorescence spectroscopy (EDXRF) in the coal quality analysis was reported formerly. But in the practical test of the prototype instrument in the real power plant, the XRF signals suffered from intensity fluctuations over long-time measurements. The long-term signal fluctuations cause lower efficiency on the establishment of calibration model and relatively larger root mean square error of prediction (RMSEP) for unknown samples. Therefore, the spectral intensity correction was performed in the measurements: a randomly selected sample was measured several times in the whole measurements, including the modeling samples and the unknown samples, recording the signal fluctuations and search for a set of factors suitable for the intensity correction of a full-spectrum based partial least square calibration model. In addition, as the signals of the coal samples of the power plan showed potential of classification, the piecewise models were also established in case of further enhancement of the model or prediction accuracy. The RMSEPs of calorific value, ash, volatile and sulfur were lowered from 0.68 MJ/kg, 1.62 %, 0.32 % and 0.24 % to 0.51 MJ/kg, 1.34 %, 0.16 % and 0.14 % respectively. The piecewise modeling achieved samilar RMSEP for volatile and sulfur prediction but with more accurate model. The spectral intensity correction showed abillity of reducing the long-term signal fluctuation and piece model also showed more efficiency in the model establishments of volatile and ash determination.