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

Weizhe Ma; Meirong Dong; Yongru Huang; Qi Tong; Liping Wei; Jidong Lu

doi:10.3788/IRLA20200441

[1] Feng He. Prediction of unburned carbon in flue dust and evaluation of measurement uncertainty. Industrial Control Computer, 33, 21-23(2020).

[2] Qiming Cheng, Xiaoqing Hu, Yingfei Wang, et al. Summary of measurement methods of carbon in fly ash. Journal of Shanghai University of Electric Power, 27, 519-524(2011).

[3] Z Wang, T B Yuan, Z Y Hou, et al. Laser-induced breakdown spectroscopy in China. Frontiers of Physics, 9, 419-438(2014).

[4] Y Deguchi, M Noda, Y Fukuda, et al. Industrial applications of temperature and species concentration monitoring using laser diagnostics. Journal of Bacteriology, 190, 2637-2641(2002).

[5] M Noda. Detection of carbon content in a high-temperature and high-pressure environment using laser-induced breakdown spectroscopy. Spectrochim Acta Part B, 57, 701-709(2002).

[6] M Kurihara, K Ikeda, Y Izawa, et al. Optimal boiler control through real-time monitoring of unburned carbon in fly ash by laser-induced breakdown spectroscopy. Applied Optics, 42, 6159-6165(2003).

[7] L G Blevins, C R Shaddix, S M Sickafoose, et al. Laser-induced breakdown spectroscopy at high temperatures in industrial boilers and furnaces. Applied Optics, 42, 6107-6118(2003).

[8] Yufang Li, Lei Zhang, Yao Gong, et al. Development of a laser on-line cement raw material analysis equipment. Spectroscopy and Spectral Analysis, 36, 1494-1499(2016).

[9] Xinhua Zhang, Mingjun She, Yanshu Zhang, et al. The progress and potential of the application of laser technology in mud logging. Petroleum Drilling Techniques, 46, 111-117(2018).

[10] D Body, B L Chadwick. Optimization of the spectral data processing in a LIBS simultaneous elemental analysis system. Spectrochimica Acta Part B Atomic Spectroscopy, 56, 725-736(2001).

[11] D Body, B L Chadwick. Simultaneous elemental analysis system using laser induced breakdown spectroscopy. Review of Scientific Instruments, 72, 1625-1629(2001).

[12] B L Chadwick, D Body. Development and commercial evaluation of laser-induced breakdown spectroscopy chemical analysis technology in the coal power generation industry. Applied Spectroscopy, 56, 70-74(2002).

[13] M R Dong, J D Lu, S C Yao, et al. Application of LIBS for direct determination of volatile matter content in coal. Journal of Analytical Atomic Spectrometry, 26, 2183-2188(2011).

[14] Meirong Dong, Liping Wei, Jidong Lu, et al. Quantitative analysis of LIBS coal heat value based on K-CV parameter optimization support vector machine. Spectroscopy and Spectral Analysis, 39, 2202-2209(2019).

[15] M R Dong, L P Wei, J D Lu, et al. A comparative model combining carbon atomic and molecular emissions based on partial least squares and support vector regression correction for carbon analysis in coal using LIBS. Journal of Analytical Atomic Spectrometry, 34, 480-488(2019).

[16] Z Wang, J Feng, L Z Li, et al. A multivariate model based on dominant factor for laser-induced breakdown spectroscopy measurements. Journal of Analytical Atomic Spectrometry, 26, 2289-2299(2011).

[17] C Yan, J Qi, J Ma, et al. Determination of carbon and sulfur content in coal by laser induced breakdown spectroscopy combined with kernel-based extreme learning machine. Chemometrics and Intelligent Laboratory Systems, 167, 226-231(2017).

[18] Z Z Wang, R W Liu, Y Deguchi, et al. Detection improvement of unburned carbon content in fly ash flow using LIBS with a two-stage cyclone measurement system. Energy & Fuels, 33, 7805-7812(2019).

[19] S C Yao, J L Xu, J B Zhao, et al. Characterization of fly ash laser-induced plasma for improving the on-line measurement of unburned carbon in gas–solid flow. Energy & Fuels, 31, 4681-4686(2017).

[20] S C Yao, J D Lu, J P Zheng, et al. Analyzing unburned carbon in fly ash using laser-induced breakdown spectroscopy with multivariate calibration method. Journal of Analytical Atomic Spectrometry, 27, 473-478(2012).

[21] Shunchun Yao, Jidong Lu, Shenghua Pan, et al. Coal suitability of the measurement of unburned carbon by laser-induced breakdown spectroscopy. Proceedings of The Chinese Society for Electrical Engineering, 29, 80-83(2009).

[22] Shunchun Yao, Jidong Lu, Meirong Dong, et al. Simultaneous measurements of coal ash compositionby laser-induced breakdown spectroscopy in different optical collection. Proceedings of The Chinese Society for Electrical Engineering, 33, 54-60(2013).

[23] Ruomu Hu, Zhenzhen Wang, Renwei Liu, et al. Quantitative analysis of unburned carbon in fly ash by laser-induced breakdown spectroscopy. Acta Photonica Sinica, 47, 39-46(2018).

[24] Yueliang Shen, Shunchun Yao, Gang Pan, et al. Influence of binder on laser-induced breakdown spectroscopy measurement of unburned carbon in fly ash. Chinese Journal of Lasers, 41, 242-246(2014).

[25] Lei Zhang, Weiguang Ma, Lei Dong, et al. Development of an apparatus for on-line analysis of unburned carbon in fly ash using laser-induced breakdown spectroscopy (LIBS). Appl Spectrosc, 65, 790-796(2011).

[26] Renwei Liu, Y Deguchi, Weigang Nan, et al. Unburned carbon measurement in fly ash using laser-induced breakdown spectroscopy with short nanosecond pulse width laser. Advanced Powder Technology, 30, 1210-1218(2019).

[27] J Huang, M Dong, S Lu, et al. Estimation of the mechanical properties of steel via LIBS combined with canonical correlation analysis (CCA) and support vector regression (SVR). Journal of Analytical Atomic Spectrometry, 33, 720-729(2018).

[28] S Lu, S Shen, J Huang, et al. Feature selection of laser-induced breakdown spectroscopy data for steel aging estimation. Spectrochimica Acta Part B Atomic Spectroscopy, 150, 49-58(2018).

[29] Huiwen Wang, Jie Meng. Predictive modeling on multivariate linear regression. Journal of Beijing University of Aeronautics and Astronautics, 33, 500-504(2007).

[30] Wang Huiwen. Partial LeastSquares RegressionMethod Applications[M]. Beijing: National Defense Industry Press, 1999: 150197. (in Chinese)

[31] Guangbin Huang, Dianhui Wang, Yuan Lan. Extreme learning machines: A survey. International Journal of Machine Learning and Cybernetics, 2, 107-122(2011).

[32] X H Li, S B Yang, R W Fan, et al. Discrimination of soft tissues using laser-induced breakdown spectroscopy in combination with k nearest neighbors (kNN) and support vector machine (SVM) classifiers. Optics & Laser Technology, 102, 233-239(2018).

[33] L Yu, H Liu. Efficient feature selection via analysis of relevance and redundancy. Journal of Machine Learning Research, 5, 1205-1224(2004).

[34] I Guyon, A Elisseeff. An introduction to variable and feature selection. Journal of Machine Learning Research, 3, 1157-1182(2003).

[35] N C Dingari, I Barman, A K Myakalwar, et al. Incorporation of support vector machines in the LIBS toolbox for sensitive and robust classification amidst unexpected sample and system variability. Analytical Chemistry, 84, 2686-2694(2012).

[36] L J Song, W K Huang, X Han, et al. Real-time composition monitoring using support vector regression of laser-induced plasma for laser additive manufacturing. IEEE Transactions on Industrial Electronics, 64, 633-642(2016).

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