STGNets: A spatial–temporal graph neural network for energy consumption prediction in cement industrial manufacturing processes

Abstract

Energy consumption is an essential indicator for conserving energy and reducing emissions in industrial manufacturing processes. However, the industrial production chain is a multi-variable, time-varying, nonlinear system, which poses significant challenges for energy consumption prediction, particularly under complex physical–chemical processes, even when the system is in a steady state. Furthermore, the poor accuracy cannot meet practical production requirements due to current prediction systems cannot integrate spatial–temporal information effectively. To solve this issue, this research provides a novel technique for final online goods quality prediction based on deep spatial–temporal graph neural networks (GNN). Our approach can capture hidden spatial information relationships and manage long-time sequences in the processing data by using a learnable dependency matrix and a stacked dilated convolution component. Moreover, these two primary components are organically merged into a single end-to-end optimization framework. The experimental results on the real-world test dataset demonstrate that our technique outperforms rival machine learning techniques. Our research exemplifies how graph deep learning architecture may be utilized to solve real-world problems in industries.