Industrial cyber-physical system driven intelligent prediction model for converter end carbon content in steelmaking plants

Abstract

Industry 4.0 provides an opportunity for realizing converter intelligent steelmaking. Based on a cyber-physical system (CPS) framework for steelmaking plants, we developed a non-contact intelligent prediction model for determining molten steel carbon content. We achieved this by leveraging big data in-depth learning and optical information from converter mouth flame characteristics to realize accurate control of end-point carbon content in the process of intelligent steelmaking. The spectral information of a converter port flame was gathered using a USB2000+ spectrometer, and its main characteristics were extracted via factor analysis. The sampling frequency of the converter flue gas analysis mass spectrometer was set to obtain the carbon content of continuous molten steel during the late stages of steelmaking consistent with the spectral information collection frequency. A large sample dataset was constructed to continuously predict the carbon content of the molten steel based on the flame spectrum information of the furnace orifice. Furthermore, the parameters of a support vector regression algorithm were optimized, and the quantitative relationships among the sample data were intelligently mined to obtain a dynamic prediction model of the carbon content of 150 furnace samples. To improve the universality of the model, the initial steelmaking conditions were applied as the optimization criteria of the carbon content prediction model generated by matching different furnace samples. A support vector machine algorithm was applied to construct a model optimization classifier to obtain a universal prediction model. The results indicate that in the late stages of smelting, the spectral information of the flame in the furnace exhibited a high correlation with the carbon content of the molten steel. After testing, the prediction accuracy of this model was more than 90%. With the help of big data deep learning and CPS framework, we can build robust and accurate steelmaking intelligent control systems.