A knowledge-refined hybrid graph model for quality prediction of industrial processes

Abstract

The complexity of industrial processes has spurred the application of soft sensor techniques for predicting key quality variables based on easy-measurable process variables. Currently, data-driven soft sensors based on Artificial Intelligence techniques have become the mainstream. However, these soft sensing models deeply rely on the quality of training data, where the domain knowledge is often ignored. Meanwhile, a significant amount of labeled data is not fully utilized. To address these issues, this paper proposes a supervised framework based on a knowledge-refined hybrid graph network, which contributes to the artificial intelligence application of nonlinear dynamic soft sensors. The problems of applying traditional artificial intelligence models in soft sensor have been addressed by reconstructing the input module of graph neural networks with knowledge-guided approaches. Both spatial and temporal correlations of process data are captured and the hybrid network significantly improves the reliability and interpretability of the soft sensing model. By incorporating labeled data into the model, the representation of quality information is also enhanced. Finally, the proposed framework was applied to an industrial debutanizer column, and the experimental results fully demonstrate the effectiveness and superiority of the method.