Graph-Conditioned Industrial Process Forecasting with Engineering and Data-Derived Relations

Accurate process variable forecasting is essential for monitoring, control, and optimization in complex industrial systems. Although existing spatiotemporal forecasting methods have achieved remarkable performance by modeling temporal dependencies and variable interactions, integrating engineering knowledge and data-derived dependencies into both historical encoding and future-target representations remains a modeling challenge. This paper proposes a graph-conditioned forecasting framework for industrial processes. Specifically, a dual-prior temporal–relational encoding aggregation module is developed to integrate an engineering-specified static graph with lagged conditional predictive relationships, enabling unified modeling of physical interactions and causality-informed relationships among process variables. A graph-conditioned query generation mechanism combines future temporal embeddings with variable embeddings augmented by averaged shortest-path-distance embeddings from the data-estimated graph to incorporate structural information into future forecasting queries, while a topology-aware query–state alignment decoder computes an adaptive residual gate to fuse structural priors with historical dynamic states to enhance forecasting representations. In addition, a flow-guided trajectory refinement strategy uses historical process context to refine the initial multivariate forecast trajectories. Compared with the best-performing baseline in each evaluated setting, the framework reduces MSE by approximately 15.4% and 4.9% on the sulfur recovery unit dataset for forecast horizons of 12 and 96 time steps, respectively. On the debutanizer column dataset, the corresponding reductions are approximately 1.9% and 3.0% for horizons of 8 and 12 time steps.