Abstract
This paper presents a novel method of identifying coal type based on mechanistic methods. The ratio of the resonance line spectrum of a luminous flame and the continuous spectrum at the same wavelength eliminates the influence of temperature on spectral intensity. The atomic line spectra of Na and K are typical and significant over continuous flame spectra. The concentrations of elemental Na and K in the flame are exclusively relative to coal type and composition. Using an experimental furnace and charge-coupled device (CCD) optical spectrometer apparatus, the continuous spectra and atomic line spectra of Na and K elements were sampled from coal flames in real time. An empirical fitting method was used to simplify the formulas of absorption strength and flame temperature calculation, and rational solutions were obtained by using an iterative algorithm. Due to the change in reaction rate and absorption by soot particles, the relative contents of Na and K in a flame vary with the temperature and absorption strength. Arrhenius’s equation for temperature compensation was adopted. Compensation for soot density in the furnace was also satisfied by an exponential expression. At any one sampling position, the compensation parameters were identical for all coal types. After compensation for temperature and density of soot particles, the relative strength of the Na and K signals and the ratio between them uniquely matched the coal type burnt in various conditions. The results were replicated and verified in various conditions, and the response time of the system was of the order of seconds.
摘要
目的
为解决煤种频繁变化造成的燃烧闭环优化难 题,研究在燃烧器出口实时辨识锅炉入炉燃烧 煤种的方法。
创新点
1. 通过火焰发射光谱机理研究获取可排除燃烧 工况与测量采样等干扰因素的算法规则;2. 提 出基于煤粉火焰光谱中Na 和K 元素原子发射光 谱相对强度特征的煤种辨识方法。
方法
1. 通过光纤光谱仪获取锅炉各层燃烧器入口火 焰光谱信号;2. 利用同波长下原子光谱与连续 辐射光谱的相对关系,消去火焰温度、工况与 环境的影响;3. 以补偿后Na 和K 元素原子发射 光谱强度的特征比值表征不同煤种在火焰中的 元素含量特征,实现煤种的在线辨识。
结论
1. 利用煤粉火焰光谱特征实现入炉煤种的实时 辨识具有良好的工况稳定性与可复现性;2. 从 算法机理中消除环境影响,降低了测量系统校 验的复杂性。
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References
Beatrice, C., Bertoli, C., Cirillo, N.C., et al., 1995. Two-color pyrometry measurements of soot loading in a diesel engine burning model fuels of varying quality. Combustion Science and Technology, 110-111(1):321–339. http://dx.doi.org/10.1080/00102209508951929
Cheng, Z.H., Cai, X.S., Mao, W.P., 2004. Investigate into the characteristic emission line of flame. Journal of Engineering Thermophysics, 25(3):519–522 (in Chinese).
Drake, E., 1996. Atomic, Molecular, and Optical Physics Handbook. AIP Press, Woodbury, USA.
Fu, T.R., Cheng, X.F., Shi, C.L., et al., 2006. The set-up of a vision pyrometer. Measurement Science and Technology, 17(4):659–665. http://dx.doi.org/10.1088/0957-0233/17/4/008
Fu, T.R., Zhao, H., Zeng, J., et al., 2010. Optimization research of three-color pyrometry based on measurement uncertainties. Proceedings of the 9th Asian Thermophysical Properties Conference, No.109276.
Huang, Y., Yan, Y., Riley, G., 2000. Vision-based measurement of temperature distribution in a 500-kW model furnace using the two-colour method. Measurement, 28(3): 175–183. http://dx.doi.org/10.1016/S0263-2241(00)00010-5
Kim, S.S., Kang, Y.S., Lee, H.D., et al., 2012. Release of potassium and sodium species during combustion of various rank coals, biomass, sludge and peats. Journal of Industrial and Engineering Chemistry, 18(6):2199–2203. http://dx.doi.org/10.1016/j.jiec.2012.06.018
Kramida, A., Ralchenko, Y., Reader, J., et al., 2012. NIST Atomic Spectra Database (Version 5.0). National Institute of Standards and Technology, Gaithersburg, USA.
Li, Y., Xiao, J., Zhang, M.Y., 2005. Modeling and prediction of migration mechanism of alkali metals during coalfired process. Journal of Fuel Chemistry and Technology, 33(5):556–560 (in Chinese).
Lu, S.S., Cheng, X.F., 2003. A fast solution for the primary color measurement method for luminous flames temperature. Acta Metrologica Sinica, 24(4):293–306 (in Chinese).
Mavrodineanu, R., Boiteux, H., 1965. Flame Spectroscopy. Wiley, New York, USA.
Parameswaran, T., Hughes, P.M.J., 2007. Coal flame performance monitoring with flame emission spectroscopy. Third International Conference on Clean Coal Technologies for our Future.
Romero, C., Li, X.C., Keyvan, S., et al., 2005. Spectrometerbased combustion monitoring for flame stoichiometry and temperature control. Applied Thermal Engineering, 25(5-6):659–676. http://dx.doi.org/10.1016/j.applthermaleng.2004.07.020
Schürmann, H., Monkhouse, P.B., 2007. In situ parametric study of alkali release in pulverized coal combustion: effects of operating conditions and gas composition. Proceedings of the Combustion Institute, 31(2):1913–1920. http://dx.doi.org/10.1016/j.proci.2006.07.040
Stasio, S., Massoli, P., 1994. Influence of the soot property uncertainties in temperature and volume-fraction measurements by two-color pyrometry. Measurement Science and Technology, 5(12):1453–1465. http://dx.doi.org/10.1088/0957-0233/5/12/006
Takuwa, T., Naruseb, I., 2007a. Detailed kinetic and control of alkali metal compounds during coal combustion. Fuel Processing Technology, 88(11-12):1029–1034. http://dx.doi.org/10.1016/j.fuproc.2007.06.010
Takuwa, T., Naruseb, I., 2007b. Emission control of sodium compounds and their formation mechanisms during coal combustion. Proceedings of the Combustion Institute, 31(2):2863–2870. http://dx.doi.org/10.1016/j.proci.2006.07.170
Tan, C., Xu, L.J., Cao, Z., 2009. On-line fuel identification using optical sensing and support vector machines technique. Instrumentation and Measurement Technology Conference, p.1144–1147. http://dx.doi.org/10.1109/IMTC.2009.5168450
van Eyk, P.J., Ashman, P.J., Nathan, G.J., 2011. Mechanism and kinetics of sodium release from brown coal char particles during combustion. Combustion and Flame, 158(12):2512–2523. http://dx.doi.org/10.1016/j.combustflame.2011.05.005
Wen, Z.C., Wang, Z.H., Zhou, J.H., et al., 2009. Quantum chemical study on the catalytic mechanism of Na/K on NO-char heterogeneous reactions during the coal reburning process. Journal of Zhejiang University-SCIENCE A, 10(3):423–433. http://dx.doi.org/10.1631/jzus.A0820345
Xu, L.J., Yan, Y., Cornwell, S., et al., 2004. On-line fuel identification using digital signal processing and fuzzy inference techniques. IEEE Transactions on Instrumentation and Measurement, 53(4):1316–1320. http://dx.doi.org/10.1109/TIM.2004.830573
Xu, L.J., Tan, C., Li, X.M., et al., 2012. Fuel-type identification using joint probability density arbiter and softcomputing techniques. IEEE Transactions on Instrumentation and Measurement, 61(2):286–296. http://dx.doi.org/10.1109/TIM.2011.2164836
Zhao, H., Ladommatos, N., 1998. Optical diagnostics for soot and temperature measurement in diesel engines. Progress in Energy and Combustion Science, 24(3):221–255. http://dx.doi.org/10.1016/S0360-1285(97)00033-6
Zhou, H., Li, L.T., Zhang, H.L., et al., 2015. Research on the slagging characteristics of blended coals in a pilot-scale furnace. Journal of Zhejiang University-SCIENCE A (Applied Physics & Engineering), 16(3):204–216. http://dx.doi.org/10.1631/jzus.A1400172
Zizak, G., 2000. Flame Emission Spectroscopy: Fundamentals and Applications. Lecture given at the ICS Training Course on Laser Diagnostics of Combustion Processes, University of Cairo, Egypt.
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Project supported by the National Basic Research Program (973 Program) of China (No. 2015CB251501) and the National Natural Science Foundation of China (No. 51476137)
ORCID: Hao ZHOU, http://orcid.org/0000-0001-9779-7703
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Yin, F., Luo, Zh., Li, Y. et al. Coal type identification based on the emission spectra of a furnace flame. J. Zhejiang Univ. Sci. A 18, 113–123 (2017). https://doi.org/10.1631/jzus.A1500306
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DOI: https://doi.org/10.1631/jzus.A1500306