工业过程关键指标预测的知识协同进化增强图卷积网络方法OA北大核心CSTPCD

Efforts have been made in the field of industrial process key variable prediction to integrate process knowl-edge with big data in order to achieve higher accuracy,reduce the risk of overfitting,and improve interpretability.However,existing approaches face challenges such as the high cost of constructing accurate prior knowledge and the inability to extract knowledge from abundant data,which limits their applicability to real industrial processes.To address these issues,this study proposes an enhanced graph convolutional network of knowledge-based co-evolution(KBCE-GCN)method for industrial process key variable prediction.Initially,a coarse-grained process knowledge is constructed from an easily ac-cessible process flow diagram,requiring minimal construction cost.Subsequently,graph exploration is introduced in GCN model training to update the knowledge.Finally,a knowledge filtering mechanism is designed to reduce the complexity of the knowledge and maintain consistency.The proposed KBCE-GCN method is validated using a benchmark debutanizer column process.The experimental results demonstrate excellent prediction accuracy and the acquisition of high-quality new knowledge.