Abstract

Complex industrial processes exhibit intricate interdependencies and non-linear relationships that traditional modeling approaches struggle to capture effectively. Graph Neural Networks (GNNs) have emerged as a transformative paradigm for representing and analyzing such complex systems by leveraging graph-structured data representations. This paper presents a comprehensive review of GNN applications in industrial process modeling, examining various architectures, methodologies, and their effectiveness in capturing process dynamics, predicting system behavior, and optimizing operational parameters. We analyze the advantages of GNN-based approaches over conventional modeling techniques and discuss implementation challenges, performance metrics, and future research directions in the context of Industry 4.0 and smart manufacturing systems.