A 3-D Convolution-Based Burn-Through Point Multistep Prediction Model for Sintering Process

Abstract

Predicting burn-through point (BTP) in advance is a quite critical task for the sintering process. However, sintering is a complex physicochemical reaction process, and the strong spatial-temporal correlations of data make the multi-step prediction task very challenging. The previous BTP multi-step prediction model only extracts spatial features in the high-level layers, leaving the spatial features in the low-level layers not learned. Specifically, the previous model only considers the relationships between the process variables and BTP, ignoring the spatial coupling relations among process variables. Further, the existing loss function is mainly based on Euclidean distance, which cannot learn dynamic information of multi-step prediction sequence. To tackle these problems, in this study, we propose a 3D convolution-based BTP multi-step prediction model (CBMP) to simultaneously capture spatial-temporal features. First, the 3D convolution is employed to capture the spatial-temporal features from low-level to high-level layers at the same time. Secondly, a spatial-temporal recalibration block is proposed to further refine the extracted features to increase the contributions of informative features and suppress the less useful ones. Finally, we design a time-aware multi-step prediction loss function to dynamically weigh the similarity between the actual sequence and the predicted sequence. The experimental results on two real-world BTP datasets demonstrate the effectiveness and feasibility of the proposed model on the BTP multi-step prediction task.