Development of a coal quality analyzer for application to power plants based on laser-induced breakdown spectroscopy
Development of a coal quality analyzer for application to power plants based on laser-induced breakdown spectroscopy
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基于激光诱导击穿光谱技术的发电厂煤质分析仪的开发
DOI：
10.1016/j.sab.2015.09.021
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发表时间：
2015
期刊：
影响因子：
--
通讯作者：
Jia Suotang
中科院分区：
文献类型：
--
作者：
Zhang Lei;Gong Yao;Li Yufang;Wang Xin;Fan Juanjuan;Dong Lei;Ma Weiguang;Yin Wangbao;Jia Suotang;Zhang Lei;Yin Wangbao;Jia Suotang
对于发电厂来说，快速确定煤炭性质以优化燃烧过程至关重要。在这项工作中，设计了一种完全软件控制的基于激光诱导击穿光谱（LIBS）的煤质分析仪，包括 LIBS 装置、采样设备和控制模块，可应用于发电厂，以提供快速、精确的煤质分析结果。提出一种闭环反馈脉冲激光能量稳定技术，利用检测到的激光能量信号将Nd:YAG激光输出能量稳定在预设区间，提高测量稳定性，并应用于长达一个月的监测实验。结果表明，激光能量稳定性从±5.2%大幅降低到±1.3%。为了表明目标分析物浓度与相应血浆光谱之间的复杂关系，采用支持向量回归（SVR）作为非线性回归方法。结果表明，这种 SVR 方法与主成分分析 (PCA) 相结合，通过使用煤样校准集，可以显着提高交叉验证的精度。灰分含量、挥发分含量和发热量的预测均方根误差分别从 2.74% 下降到 1.82%、1.69% 下降到 1.22%、1.23 MJ/kg 下降到 0.85 MJ/kg。同时，预测样本的平均相对误差分别从8.3%降低到5.48%、5.83%降低到4.42%、5.4%降低到3.68%。 SVR 与基于 PCA 的校准模型相结合所获得的更高的准确度为煤炭特性的前瞻性预测开辟了途径。
It is vitally important for a power plant to determine the coal property rapidly to optimize the combustion process. In this work, a fully software-controlled laser-induced breakdown spectroscopy (LIBS) based coal quality analyzer comprising a LIBS apparatus, a sampling equipment, and a control module, has been designed for possible application to power plants for offering rapid and precise coal quality analysis results. A closed-loop feedback pulsed laser energy stabilization technology is proposed to stabilize the Nd: YAG laser output energy to a preset interval by using the detected laser energy signal so as to enhance the measurement stability and applied in a month-long monitoring experiment. The results show that the laser energy stability has been greatly reduced from ± 5.2% to ± 1.3%. In order to indicate the complex relationship between the concentrations of the analyte of interest and the corresponding plasma spectra, the support vector regression (SVR) is employed as a non-linear regression method. It is shown that this SVR method combined with principal component analysis (PCA) enables a significant improvement in cross-validation accuracy by using the calibration set of coal samples. The root mean square error for prediction of ash content, volatile matter content, and calorific value decreases from 2.74% to 1.82%, 1.69% to 1.22%, and 1.23 MJ/kg to 0.85 MJ/kg, respectively. Meanwhile, the corresponding average relative error of the predicted samples is reduced from 8.3% to 5.48%, 5.83% to 4.42%, and 5.4% to 3.68%, respectively. The enhanced levels of accuracy obtained with the SVR combined with PCA based calibration models open up avenues for prospective prediction in coal properties.