光谱学与光谱分析 |
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Multi-Spectral Measurement of Basic Oxygen Furnace Flame Temperature |
WANG Yong-qing, CHEN Yan-ru, ZHAO Qi*, CHEN Fei-nan, CHEN Jing-jing |
School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China |
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Abstract A multi-wavelength analysis method is introduced to measure the temperature of basic oxygen furnace flame. In this study, USB4000 spectrometer was applied to obtain radiation spectrum of flame within wavelength range 200~1 100 nm, from which the flame temperature and monochromatic emissivity was derived by Levenberg-Marquart modeling method. Wavelet neural network was applied to process the spectral measurement data, which could cancel the assumption model of emissivity and wavelengths. It is a kind of valid method to acquire the true temperature and spectral emissivity. Each neuron in the hidden layer of a feed-forward network is a combination of the sigmoidal activation function (SAF) and morlet wavelet activation function (WAF). The output of the hidden neuron is the product of the output from these two activation functions.
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Received: 2012-06-07
Accepted: 2012-08-28
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Corresponding Authors:
ZHAO Qi
E-mail: zhaoqi@njust.edu.cn
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[1] DAI Jing-min(戴景民). Theory and Practice of Multi-Spectral Thermometry(多光谱辐射测温理论与应用). Beijing: Higher Education Press(北京:高等教育出版社),2002. 10. [2] Alexis Coppalle,Pierre Vervisch. Combustion and Flame,1983,49:101. [3] Michael F Modest. Radiative Heat Transfer,Second Edition. San Diego:Academic Press,2003. 13. [4] Chang H,Charalampopoulos T T. Proc. R. Soc. Lond. A,1990,430: 577. [5] Fu Tairan,Cheng Xiaofang,Yang Zangjian. Applied Optics,2008, 47(32): 6112. [6] YANG Yong-jun,CAI Jing, et al(杨永军,蔡 静, 等). Temperature Measurement Under Special Conditions(特殊条件下的温度测量). Beijing:China Metrology Publishing House(北京:中国计量出版社),2009. [7] Press W H,Teukolsky S A. Vetterling W T,et al. Numerical Recipes in C. Cambridge University Press,1988. [8] Ahmad Banakar,Mohammad Fazle Azeem. Applied Soft Computing,2008,8: 1463. [9] LI Zuo-yong,PENG Li-hong(李祚泳,彭荔红). Journal of Infrared and Millimeter Waves(红外与毫米波学报),2002,21(4):293. [10] CHEN Cong,LU Qi-peng,PENG Zhong-qi(陈 丛,卢启鹏,彭忠琦). Acta Optica Sinica(光学学报),2012,32(5): 0530001. |
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