DSTED: A Denoising Spatial–Temporal Encoder–Decoder Framework for Multistep Prediction of Burn-Through Point in Sintering Process

Abstract

Sinter ore is the main raw material of the blast furnace, and burn-through point (BTP) has a direct influence on the yield, quality, and energy consumption of the ironmaking process. Since iron ore sintering is a very complex industrial process with strong nonlinearity, multivariable coupling, random noises, and time variation, traditional soft-sensor models are hard to learn the knowledge of the sintering process. In this article, a multistep prediction model, called denoising spatial–temporal encoder–decoder, is developed to predict BTP in advance. First, the mechanism analysis is carried out to determine the relevant-BTP variables, and the BTP prediction is defined as a sequence-to-sequence modeling problem. Second, motivated by the random noises of industrial data, a denoising gated recurrent unit (DGRU) is designed to alleviate the impact of noise by adding a denoising gate into the GRU. In this case, the encoder with DGRU can better extract the latent variables of original sequence data. Then, spatial–temporal attention is embedded into the decoder to simultaneously capture the time-wise and variable-wise correlations between the latent variables and the target variable BTP. Finally, the experimental results on the real-world dataset of a sintering process demonstrated that the integrated multistep prediction model is effective and feasible.