BTPNet: A Probabilistic Spatial-Temporal Aware Network for Burn-Through Point Multistep Prediction in Sintering Process

Burn-through point (BTP) is a very key factor in maintaining the normal operation of the sintering process, which guarantees the yield and quality of sinter ore. Due to the characteristics of time-varying and multivariable coupling in the actual sintering process, it is difficult for traditional soft-sensor models to extract spatial-temporal features and reduce multistep prediction error accumulation. To address these issues, in this study, we propose a probabilistic spatial-temporal aware network, called BTPNet, which is used to extract spatial-temporal feature for accurate BTP multistep prediction. The BTPNet model consists of two parts: an encoder network and a decoder network. In the encoder network, the multichannel temporal convolutional network (MTCN) is employed to extract the temporal features. Meanwhile, we also propose a novel architectural unit called variables interaction-aware module (VIAM) to extract the spatial features. In the decoder network, to reduce the accumulated errors of the last step prediction, a probabilistic estimation (PE) method is proposed to improve the performance of multistep prediction. Finally, the experimental results on a real sintering process demonstrate the proposed BTPNet model outperforms state-of-the-art multistep prediction models.