文献类型：期刊文献

英文题名：Gated Recurrent Knowledge-Guided Attention Network with Adaptive Graph Structure Learning for Forecasting in Chemical Processes

作者：Luo, Kai[1];Peng, Haifei[1];Wang, Bin[1];Guo, Wenze[1];He, Renchu[2];Long, Jian[1]

机构：[1]East China Univ Sci & Technol, Key Lab Smart Mfg Energy Chem Proc, Shanghai 200237, Peoples R China;[2]China Univ Petr, Coll Artificial Intelligence, Dept Automat, Beijing 102249, Peoples R China

年份：2025

卷号：64

期号：41

起止页码：19924

外文期刊名：INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH

收录：;EI(收录号：20254219333157);WOS:【SCI-EXPANDED(收录号:WOS:001576237900001)】；

基金：This work was supported by National Natural Science Foundation of China (62394345, 62136003, 62373155), the Programme of Introducing Talents of Discipline to Universities (the 111 Project) under Grant B17017 and Fundamental Research Funds for the Central Universities (222202517006).

语种：英文

外文关键词：Forecasting - Graph neural networks - Graph structures - Graph theory - Graphic methods - Knowledge graph - Network topology

摘要：The prediction of key variables in chemical processes presents significant challenges due to the intricate dynamic relationships among the variables. Most existing graph-based methods for chemical processes rely on a static topological graph structure derived from predefined variable relationships and only consider pairwise relationships between variables, which are inconsistent with practical scenarios. To overcome these limitations, this paper proposes the gated recurrent knowledge-guided attention network with adaptive graph structure learning (GRKAT-GSL), an end-to-end recurrent graph neural network for knowledge-guided graph attention calculation based on the learned topological graph structure. First, variables are embedded using different convolution kernels to extract features, which are then utilized to construct a topological graph as the input to the graph recurrent neural network. Then, a graph attention calculation method, the knowledge-guided attention mechanism (KGAM), is proposed to calculate the relationships between connected nodes in the graph to guide the transmission of information between nodes in the recurrent network. Experimental results on the Tennessee Eastman (TE), fluid catalytic cracking (FCC), and METR-LA data sets demonstrate that GRKAT-GSL outperforms other methods. Furthermore, the analysis of the experimental results indicates that the proposed method is capable of capturing complex relationships in chemical processes.