A Context-Aware Enhanced GRU Network With Feature-Temporal Attention for Prediction of Silicon Content in Hot Metal

Blast furnace ironmaking is one of the most complicated industrial processes. As an essential reference index for blast furnace operation, the prediction of silicon content in hot metal is important. Most previous works focus on the dynamics and nonlinearity of the process without comprehensively considering the correlation between the process variables and the silicon content. Besides, the timing mismatch of input and output variables caused by the inertia of the process still cannot be handled effectively. To solve these problems, in this article, the proposed model puts extra attention on input variables to strengthen the information of key variables, and introduces causal convolution-based self-attention to incorporate local context into attention mechanism in the temporal dimension, which realizes local awareness enhancement and variables soft alignment. With a series of theoretical and practical verification, the hybrid model shows significant improvement at hit rate and mean-square error.