关键词: 烧结料层, 透气性, 预测模型, 注意力机制, 神经网络, 变换神经网络模型

Abstract:

In the sintering process， the air permeability of the sinter layer significantly impacts sinter quality. Therefore， it is essential to construct a model for accurately air permeability prediction of the sinter layer. Due to the inadequacy of traditional coding?decoding models in handling time series dependencies，time?series transformer-long short?term memory network （TST-LSTM） model is proposed. This model leverages the decoding component of the transformer model and combines the advantages of LSTM to achieve realtime prediction of air permeability of the sinter layer. Comparative analysis with simulation results from traditional backpropagation neural network （BPNN）， support vector regression （SVR）， and long shortterm memory （LSTM） models demonstrates that TST-LSTM exhibits superior and more stable prediction performance. The proposed method is validated through simulation predictions based on actual sintering processes.

Key words: sinter layer, air permeability, prediction model, attention mechanism, neural network, transformer neural network model