光谱学与光谱分析 |
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The Study of Spectral Characteristic of Coal Ash from Different Sources with Laser-Induced Breakdown Spectroscopy |
SHEN Yue-liang1, LU Ji-dong2*, ZHANG Bo2 |
1. Institute of Electric Power Science, Guangdong Electric Grid Company, Guangzhou 510080, China 2. College of Electric Power, South China University of Science and Technology, Guangzhou 510640, China |
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Abstract The samples with different carbon content are collected for quantitative analysis. One of the normal methods is the ignition of different coals according to the notice of fast ashing method instead of collecting coal ash in boiler. But there are some differences between fast ashing method in laboratory and actual boiler. It is necessary that the spectral deviation of coal ash from these two sources is studied as a guidance of quantitative analysis in carbon content. In present work, the intensity of the characteristic lines and plasma temperature were compared with different carbon content from these two processes. As a result, Fe, Mg, Al line strength of ash with fast ashing method is stronger and plasma temperature is lower than coal ash in boiler. Principal component analysis was processed, the results show that the difference of Fe, Mg, Al and Si content is the primary factor, and minerals in coal ash with fast ashing method may influence the spectral characteristic. The influence of mineral elements and mineral content on spectra for quantitative analysis with fast ashing method should be noticed.
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Received: 2015-09-30
Accepted: 2016-01-24
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Corresponding Authors:
LU Ji-dong
E-mail: jdlu@scut.edu.cn
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[1] LI Xiao-jiang(李小江). Standardization and Metrology of Electric Power(电力标准化与计量), 1996, (3): 28. [2] MIAO Peng(苗 鹏). On-Line Monitoring System of Fly Ash Carbon Content Based on Burned Weight Loss Method(基于烧失法的飞灰含碳量在线监测系统研究). Beijing Jiaotong University(北京交通大学), 2012. [3] WANG Tao, ZHANG Chun-long, WU Nan, et al(王 涛,张春龙,吴 楠,等). Journal of Green Science and Technology(节能), 2012, 4: 65. [4] Noda M, Deguchi Y, Iwasaki S, et al. Spectrochimica Acta Part B: Atomic Spectroscopy, 2002, 57(4): 701. [5] Kurihara M, Ikeda K, Izawa Y, et al. Applied Optics, 2003, 42(30): 6159. [6] Ctvrtnickova T, Mateo M P, Yaez A, et al. Spectrochimica Acta Part B: Atomic Spectroscopy, 2009, 64(10): 1093. [7] Stankova A, Gilon N, Dutruch L, et al. Fuel, 2010, 89(11): 3468. [8] Ctvrtnickova T, Mateo M P, Yanez A, et al. Spectrochimica Acta Part B: Atomic Spectroscopy, 2010, 65(8): 734. [9] Zhang L, Ma W, Dong L, et al. Applied Spectroscopy, 2011, 65(7): 790. [10] 2001 GBT. National Standards of the People’s Republic of China: Industrial Analysis Method of Coal(中华人民共和国国家标准煤的工业分析方法), 2002. [11] YAO Shun-chun, LU Ji-dong, PAN Shen-hua,et al(姚顺春,陆继东,潘圣华, 等). Chinese Journal of Lasers(中国激光), 2010,(4): 1114. [12] YAO Shun-chun, LU Ji-dong, PAN Shen-hua, et al(姚顺春,陆继东,潘圣华,等). Proceedings of the CSEE(中国电机工程学报), 2009,(23): 80. [13] QIAN Jue-shi(钱觉时). Characterization of Powder Coal Ash and Powder Coal Ash Concrete(粉煤灰特性与粉煤灰混凝土). Beijing: Science Press(北京:科学出版社), 2002. [14] Miziolek, Andrzej, Vincenzo Palleschi,et al. Laser Induced Breakdown Spectroscopy. Cambridge University Press, 2006. [15] XU Lu, SHAO Xue-guang(许 禄,邵学广). Method of Chemical Metrology(化学计量学方法). Beijing: Science Press(北京:科学出版社), 1995. [16] Huffman G P, Huggins F E, Dunmyre G R. Fuel, 1981, 60(7): 585. |
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