氧气顶炉口火焰温度多光谱分析

Multi-Spectral Measurement of Basic Oxygen Furnace Flame Temperature

采用多波长分析方法测量氧气顶回转炉口火焰温度的研究。在该方法中,采用USB4000型光纤光谱仪测量火焰在可见光波长范围内的火焰辐射光谱,结合Levenberg-Marquardt最优化算法,得到火焰温度和单色辐射率变化规律。本文还提出了采用小波神经网络处理多光谱测温数据,该方法可以取消发射率与波长之间的假设模型,是一种获得目标真温及发射率行之有效的方法。网络中隐含层的神经元是由S函数和Morlet小波函数的乘积组合构成,在逼近火焰温度中具有良好的表现。

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.