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.