基于小波神经网络的氧气顶回转炉口火焰温度多光谱测量

氧气顶回转炉(BOF)口火焰温度的分布是对炉内钢水温度和成分含量判定的一项重要依据。通过对炉口350~1100 nm光谱数据的分析，炉口火焰光谱为“带状”辐射重叠在连续的或“黑体”辐射上，在可见光波段有明显的辐射能力。以在南京钢铁公司炼钢炉前在线采集的400炉光谱数据为研究对象，应用小波分析和神经网络的两大类模型交叉结合的方式对炉口火焰温度进行建模预测，并对预测结果做出分析。结果表明，紧致型小波神经网络在预测中取得更佳的效果，基于多光谱测温理论的小波神经网络预测的结果与副枪测量的温度误差能够在理想的范围内。

Multi-Spectral Measurement of Basic Oxyogen Furnace Flame Temperature Using Wavelet-Networks

Distribution of basic oxygen furnace (BOF) flame is an important basis for determining the content of molten steel temperature and composition. Analyzing 350~1100 nm spectral data from the furnace mouth, furnace flame atomic emission spectra overlap in a continuous or "black body" radiation, which are in a clear visible radiation. Data collected from nanjing iron and steel company′s steel-making furnace as sample data are used to implement the algorithms. The sample contains 400 data pairs. A model is applied based on the theory of wavelet analysis and neural networks to predict the temperature of the furnace flame and the results are analyzed in detail. It is shown that the method of neural networks with compact structure can give better hit rates of prediction; the temperature predicted by the model is inosculated to the temperature obtained by converter sub-lance comparatively.