Spatiotemporal Graph Attention Network for Soft Sensor Modeling of Suspended Magnetization Roasting Process

Data-driven soft sensor modeling is a significant research area that aims to extract reliable and comprehensive features from massive data in production processes, thereby realizing the practical value of data for guiding actual production. However, existing research primarily focuses on extracting features in a single dimension, overlooking the multidimensional dependencies among equipment in the production process. To address this issue, this article proposes a spatiotemporal graph attention network (ST-GAT). The network constructs a spatial directed graph model to capture the connectivity among multiple equipment while considering the temporal variations of equipment data and their impact on the target variable. ST-GAT performs spatiotemporal feature extraction on both the equipment themselves and the equipment connected to them, aiming to represent the data of each production equipment more comprehensively. Furthermore, to effectively utilize equipment features relevant to the target prediction, an adaptive weighting module (AW) is introduced to evaluate the importance of each equipment feature for the target variable, thereby improving the utilization of important features. In addition, to overcome the limitations of traditional single activation functions, a variable activation function (VAF) is proposed, which adaptively selects activation functions to enhance the network’s autonomous learning capability. Finally, experiments are conducted on a real dataset from the suspended magnetization roasting (SMR) production process. Through multiple modeling experiments and comprehensive performance evaluations, ST-GAT achieves the best prediction results on the SMR process dataset, demonstrating the crucial role of modeling spatiotemporal dependencies among equipment and further validating the effectiveness of ST-GAT.