文献类型：期刊文献

英文题名：A Multi-Source Attention Graph Neural Network for modeling long and short-term dependencies in chemical process forecasting

作者：Long, Jian[1,2];Wang, Bin[1];Peng, Haifei[1];Zhang, Hengmin[1,3]

机构：[1]East China Univ Sci & Technol, Key Lab Smart Mfg Energy Chem Proc, Shanghai 200237, Peoples R China;[2]East China Univ Sci & Technol, Int Joint Res Ctr Green Energy Chem Engn, Shanghai 200237, Peoples R China;[3]Shanghai Key Lab Data Sci, Shanghai 200438, Peoples R China

年份：2026

卷号：71

外文期刊名：ADVANCED ENGINEERING INFORMATICS

收录：;EI(收录号：20260620033263);WOS:【SCI-EXPANDED(收录号:WOS:001686122200001)】；

语种：英文

外文关键词：Chemical process; Adaptive dynamic graph learning; Multi-source attention mechanism; Graph neural network

摘要：Chemical process data exhibit both long-term physical dependencies and short-term dynamic variations due to complex interactions among variables. To simultaneously model these heterogeneous dependencies, this paper proposes a Multi-Source Attention Graph Neural Network (MSAGNN) for soft sensing in chemical processes. MSAGNN adopts a dual-path graph recurrent architecture, where a static graph encodes prior physical relationships, and an adaptive graph structure learning module dynamically captures time-varying correlations from data. A multi-source attention mechanism is further introduced to integrate node and neighborhood information and enhance the representation of spatial-temporal dependencies. The proposed MSAGNN is evaluated on three representative industrial processes, including the Debutanizer Column (DC), the Tennessee Eastman (TE) process, and the Fluid Catalytic Cracking (FCC) unit. Experimental results show that MSAGNN consistently achieves lower RMSE, MAE, and MAPE, and higher R2 than state-of-the-art deep learning and graph-based models, demonstrating its superior prediction accuracy and robustness. Visualization of the learned dynamic graphs and attention scores indicates that MSAGNN can reveal meaningful variable interactions, confirming the effectiveness and interpretability of the proposed approach for complex chemical processes.