激光诱导击穿光谱的飞灰碳含量定量分析方法  

Quantitative analysis method of unburned carbon content of fly ash by laser-induced breakdown spectroscopy

作　　者：马维喆 董美蓉[1,3,4] 黄泳如 童琪 韦丽萍 陆继东 Ma Weizhe;Dong Meirong;Huang Yongru;Tong Qi;Wei Liping;Lu Jidong(School of Electric Power,South China University of Technology,Guangzhou 510640,China;Vkan Certification&Technology Co.,Ltd.,Guangzhou 510663,China;Guangdong Province Engineering Research Center of High Efficient and Low Pollution Energy Conversion,Guangzhou 510640,China;Guangdong Province Key Laboratory of Efficient and Clean Energy Utilization,Guangzhou 510640,China)

机构地区：[1]华南理工大学电力学院,广东广州510640 [2]威凯检测技术有限公司,广东广州510663 [3]广东省能源高效低污染转化工程技术研究中心,广东广州510640 [4]广东省能源高效清洁利用重点实验室,广东广州510640

出　　处：《红外与激光工程》2021年第9期193-202,共10页Infrared and Laser Engineering

基　　金：国家自然科学基金面上基金(51976064);广东省基础与应用基础研究基金(2020A1515010646)。

摘　　要：燃煤飞灰碳含量是影响锅炉工作效率的重要特性指标之一,文中开展激光诱导击穿光谱技术(LIBS)实现飞灰未燃碳的定量分析方法研究,为LIBS应用于飞灰含碳量的快速/在线检测奠定基础。根据所探测的LIBS特征光谱,将线性和非线性化学计量学方法,包括多元线性回归(MLR)和偏最小二乘回归(PLSR)线性分析分析方法,以及非线性的极限学习机(ELM)和支持向量机回归(SVR)模型应用于飞灰未燃碳的预测分析中,结合交叉验证法对模型进行验证。对比线性和非线性模型的结果可以看出,非线性模型的预测结果明显优于线性模型,其中采用基于K-CV参数优化的非线性SVR模型具有比较理想的分析结果,有助于提高飞灰碳含量分析的精确度和准确度,采用三折叠交叉验证法对模型进行验证,得到模型的决定系数R^(2)均为0.99,相对偏差的平均值ARD分别为1.54%、3.45%、3.51%,相对标准误差RSD的平均值分别为7.53%、2.89%、7.18%。The unburned carbon content of fly ash is an important index for the working efficiency of the coalfired boiler. In this work, laser-induced breakdown spectroscopy(LIBS) was applied to realize the quantitative analysis of unburned carbon in fly ash. Based on the detection of LIBS characteristic spectrum, the common chemometrics methods include linear model, such as multiple linear regression(MLR), partial least-squares regression(PLSR) and nonlinear model, such as extreme learning machine(ELM) model, support vector machine regression(SVR) model were proposed to the prediction analysis of unburned carbon in fly ash, and the crossvalidation method was used to verify the model. The results show that the prediction results from nonlinear models are better than that of linear models, among which the SVR model based on K-CV parameter optimization is helpful to improve the prediction accuracy and accuracy of the content of unburned carbon in the fly ash. Based on the three-fold cross validation method, the R2 of the model is 0.99, ARD is 1.54%, 3.45% and 3.51%, and the average value of RSD is 7.53%, 2.89%, 7.18%, respectively.

关 键 词：光谱分析 激光诱导击穿光谱 燃煤飞灰 未燃碳 化学计量学方法 

分 类 号：O433.4[机械工程—光学工程] O539[理学—光学]