Variable Structure Learning-Based Spatio-Temporal Graph Convolutional Networks for Chemical Process Quality Prediction with SHAP-Enhanced Interpretability

Product quality control in chemical processes faces challenges from dynamic non-stationary data, underutilized variable spatial correlations, and overreliance on prior knowledge. This paper addresses these issues by proposing an enhanced Spatio-Temporal Graph Convolutional Networks (STGCN) for chemical process soft sensing. In this method, the spatio-temporal graph attention mechanism is integrated into the Graph Convolutional Networks, enabling dynamic weighting of neighboring nodes to improve spatiotemporal feature mining and accelerate convergence. Unlike traditional STGCN models that rely on predefined graph structures and prior domain knowledge, this paper proposes the Variable Structure Learning-based Spatio-Temporal Graph Convolutional Networks (VSL-STGCN), which autonomously learns variable relational structures via end-to-end gradient descent and uses SHAP algorithm to select critical variables, reducing computational burden and overfitting risks. Finally, the proposed VSL-STGCN is validated on two real chemical processes, outperforming baseline models in prediction accuracy. Based on the experimental results, the proposed VSL-STGCN achieves about 15% lower RMSE and about 10% higher R2 compared to baseline STGCN models. The learned adjacency matrix aligns with actual process mechanisms, ensuring interpretability.