Abstract： Converter steelmaking endpoint is that the composition and temperature of the molten steel reaches a certain requirement by controlling while steel tapping. The changes of spectral information at elemental characteristic spectral wavelength displayed by atomic emission spectrometry can reflect the variation trend of ingredient content. In this article, a endpoint prediction model system based on temperature and spectral information is described. The temperature and Mn, Si spectral information in converter steelmaking process can be obtained through this system. The endpoint prediction model is established by using multivariate regression analysis, and the significance test and accuracy analysis of this model is done. Studies show that the hit rate of endpoints in this model is about 80%, and the converter steelmaking endpoints can be accurately predicted. If more elements spectral information is added into the prediction model, the hit rate of endpoint prediction will be further improved.
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