Quantitative Analysis of LIBS Coal Heat Value Based on K-CV Parameter Optimization Support Vector Machine
DONG Mei-rong1, 2, 3, WEI Li-ping1, 2, 3, LU Ji-dong1, 2, 3*, LI Wen-bing1, 2, 3, LU Sheng-zi1, 2, 3, HUANG Jian-wei1, 2, 3, LI Shi-shi1, 2, 3, LUO Fa-sheng1, 2, 3, NIE Jia-lang1
1. School of Electric Power, South China University of Technology, Guangzhou 510640, China
2. Guangdong Province Engineering Research Center of High Efficient and Low Pollution，Guangzhou 510640, China
3. Guangdong Province Key Laboratory of Efficient and Clean Energy Utilization, Guangzhou 510640, China
Abstract：Heat value is one of the important parameters of coal quality, and it greatly affects the operation of coal-fired boilers. In order to overcome the shortcomings of traditional detection methods, laser induced breakdown spectroscopy (LIBS) was applied to quantitative analysis of heat value in coal. The structure of coal is complex. It contains many types of elements, including major, minor and trace elements, which would result the complexity of LIBS spectral information from coal. It is premise and foundation to effectively extract LIBS spectral information and achieve accurate quantitative measurement by using LIBS. In recent years, as the development of artificial intelligence technology, relevant analytical techniques have also been applied to the proximate analysis and heat value prediction of coal. In order to realize the effective extraction of LIBS spectral information in coal samples and overcome the problems such as transient fitting and poor convergence that are easily caused by conventional analytical methods, the K-fold cross validation (K-CV) parameter-optimized combined with Support Vector Machine (SVM) regression was proposed to quantitatively analyze the heat value in coal. The SVM method is an approximate realization of structural risk minimization, which can be used for pattern classification and nonlinear regression. 44 coal samples with different heat values commonly used in the power plant were selected as experimental objects, 33 of which were selected as training sets, and the remaining 11 were test sets. The correlation between the parameters of the SVM regression model-penalty factor C, the kernel function parameter g and the model accuracy were firstly analyzed based on the laser-induced coal spectrum and the best search scope for C and g were determined. Then the SVM regression model was established based on the LIBS full-spectrum and some typical elements (non-metallic elements and metal elements) feature spectra, respectively. The optimal parameters C, g of the heat value SVM regression model is obtained by using the training set spectral data, combined with the K-CV method. The spectral features of the prediction set as input are to test the reliability of the model. The calibration model established by the full spectrum, non-metallic element characteristic spectrum, as well as non-metal and metal element characteristic spectrum, respectively, could all reached 0.99, with the mean square error of 0.12 , 0.17 and 0.06 (MJ·kg-1)2, the forecasted average relative deviations were 1.2%, 1.23% and 0.69%. The results showed that the SVM regression method based on K-CV parameter-optimized could be used for quantitative analysis of coal heat value using LIBS technology, and could obtain higher analysis accuracy. At the same time, by comparing the quantitative analysis models using different spectral features, the quantification model of heat value by using the characteristic spectrum of non-metal plus metal elements, can effectively improve the accuracy of LIBS in the rapid detection of heat value in coal. This method can achieve accurate prediction of heat value in coal.
董美蓉，韦丽萍，陆继东，黎文兵，陆盛资，黄健伟，李诗诗，骆发胜，聂嘉朗. 基于K-CV参数优化支持向量机的LIBS燃煤热值定量分析[J]. 光谱学与光谱分析, 2019, 39(07): 2202-2209.
DONG Mei-rong, WEI Li-ping, LU Ji-dong, LI Wen-bing, LU Sheng-zi, HUANG Jian-wei, LI Shi-shi, LUO Fa-sheng, NIE Jia-lang. Quantitative Analysis of LIBS Coal Heat Value Based on K-CV Parameter Optimization Support Vector Machine. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2019, 39(07): 2202-2209.
[1] ASTM D5865—10 Standard Test Method for Gross Calorific Value of Coal and Coke. ASTM International, 2011.
[2] ZHAO Zhong-hui, FANG Quan-guo(赵忠辉, 方全国). Coal Quality Technology(煤质技术), 2017，(4): 18.
[3] Wang Z, Yuan T B, Hou Z Y, et al. Frontiers of Physics, 2014, 9(4): 419.
[4] Yao S C, Xu J L, Bai K J, et al. Plasma Science and Technology, 2015, 17(11): 938.
[5] Zhang L, Ma W, Dong L, et al. Applied Spectroscopy, 2011, 65(7): 790.
[6] Zhang L, Hu Z Y, Yin W B, et al. Frontiers of Physics, 2012, 7(6): 690.
[7] Haider A, Rony M A, Lubna R S, et al. Optics & Laser Technology, 2011, 43(8): 1405.
[8] DONG Mei-rong, LU Ji-dong, YAO Shun-chun, et al(董美蓉, 陆继东, 姚顺春, 等). Journal of Engineering Thermophysics(工程热物理学报), 2012, 33(1): 175.
[9] Haider A, Rony M A, Abedin K M. Energy & Fuels, 2013, 27(7): 3725.
[10] Yao S, Lu J, Dong M, et al. Applied Spectroscopy, 2011, 65(10): 1197.
[11] Yao S, Mo J, Zhao J, et al. Applied Spectroscopy, 2018, 72(8): 1225.
[12] Lu Z, Mo J, Yao S, et al. Energy & Fuels, 2017, 31(4): 3849.
[13] Li W, Lu J, Dong M, et al. Energy & Fuels, 2017, 32(1): 24.
[14] Yuan T, Wang Z, Lui S L, et al. Journal of Analytical Atomic Spectrometry, 2013, 28(7): 1045.
[15] LI Xiong-hui, WANG Zhe, LIU Han-qiang, et al(李雄威, 王 哲, 刘汉强, 等). Infrared and Laser Engineering(红外与激光工程), 2017, 46(7): 734001.
[16] WANG Di, LU Ji-dong, DONG Mei-rong, et al(王 帝, 陆继东, 董美蓉, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2016, 36(8): 2607.
[17] TAN Peng, LI Xin, ZHANG Xiao-pei, et al(谭 鹏, 李 鑫, 张小培，等). Journal of China Coal Society(煤炭学报)，2015, 40(11): 2641.
[18] JIANG Wen-hao, WEI Hong-qi, QU Tian-zhang, et al(江文豪, 韦红旗, 屈天章, 等). Thermal Power Generation(热力发电), 2011, 40(3): 14.
