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Automatic temperature control of blast furnace based on data analysis model

The heating process of the blast furnace is a complex and huge controlled object, which has the characteristics of nonlinear, multivariable, distributed parameters, fast and slow processes intertwined. It is impossible to achieve satisfactory control results only by traditional control. With the development of artificial intelligence in recent years, neural networks, expert technology, fuzzy and predictive control provide new ideas for the heating control of the blast furnace. In this paper, based on the deep learning framework of artificial intelligence, the LSTM algorithm is proposed. Based on the massive production data, through the model simulation comparison, the results show that the LSTM network modeling has stronger generalization ability, smaller error, and higher accuracy. The LSTM neural network algorithm can effectively deal with the characteristics of time series data, provide guidance for the production practice, and lay a good foundation for the predictive control of blast furnaces.