Attention mechanism-based deep learning for heat load prediction in blast furnace ironmaking process

Heat load prediction is essential to discover blast furnace (BF) anomalies in time and take measures in advance to reduce erosion in the ironmaking process. However, owing to the redundancy of the high dimensional data and the multi-granularity features of the state monitoring data, the general prediction model is hard to accurately predict the heat load, especially the rapid change caused by physical and chemical reactions. Therefore, this paper puts forward an attention-based one-dimension convolution neural network (1DCNN) combined with a bidirectional long short-term memory (BiLSTM) network for heat load prediction. Firstly, the two-stage data pre-processing realizes dimension reduction and key variable selection. Secondly, fine-grained features are extracted by 1DCNN, and the BiLSTM extracts the coarse-grained features for prediction output. Moreover, an attention branch is added to the 1DCNN to extract the critical fine-grained features when the heat load changes rapidly. Finally, experiments are carried out with the actual industrial data from a BF ironmaking process. The efforts show that the proposed prediction model presents better performances in the result of different metrics and has higher accuracy than the traditional prediction algorithms.