发表机构
King Abdullah University of Science and Technology (KAUST)（阿卜杜拉国王科技大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出保守混合图网络（CHGN），其学习路由等参数并插入固定输运方程以保证质量平衡，在过程网络任务上零样本迁移性能优于GNN基准，可揭示工厂隐式机制。
AI 中文摘要
工业过程网络在运行时不维持单一有效拓扑：流被节流或旁路，单元在闲置、过渡和活动状态间切换。这类系统的模型通常在测得的状态轨迹上训练，而生成轨迹的运行机制仍为隐式，无约束图网络可拟合此类轨迹，却未为恢复的路由赋予稳定物理意义。我们用保守混合图网络（CHGN）解决这两个问题，该网络将路由、状态分配和移除率作为数据驱动替代项学习，并插入固定输运方程，使任何预测路由的质量平衡天然成立。在10-20节点网络上训练的CHGN无需重新训练即可零样本迁移到25-40节点的未见图，在相同协议下，其RMSE达2.1e-3，而GNN基准的RMSE为6e-2至9e-2；门控MAE为7.9e-3，状态准确率为94.3%（固定训练拓扑上分别为1.2e-2和96.4%）。在流体混合中试装置上，CHGN对保留的物理故障的预测优于持久性基准，但无法预测手动干预（其控制阀门动作未被观测）。因此，该模型无需重新训练即可跨过程拓扑迁移，并揭示控制工厂行为的隐式机制以供检查。
英文摘要
Industrial process networks do not maintain a single effective topology while operating: streams are throttled or bypassed, and units move between idle, transition, and active regimes. Models of such systems are typically trained on measured state trajectories while the operating mechanisms that generated them remain latent, and an unconstrained graph network can fit such a trajectory without assigning stable physical meaning to the recovered routing. We address both problems with the Conservative Hybrid Graph Network (CHGN), which learns routing, regime assignment, and removal rates as data-driven surrogates and inserts them into a fixed transport equation, so that the mass balance holds by construction for any predicted routing. CHGN trained on networks of 10-20 nodes transfers zero-shot to unseen graphs of 25-40 nodes without retraining, reaching an RMSE of 2.1e-3 against 6e-2 to 9e-2 for GNN baselines under the same protocol, with a gate MAE of 7.9e-3 and regime accuracy of 94.3% (1.2e-2 and 96.4% respectively on the fixed training topology). On a fluid-mixing pilot plant, CHGN improves on a persistence baseline for held-out physical faults but does not predict manual interventions, for which the governing valve actions are unobserved. The model therefore transfers across process topologies without retraining and exposes the latent mechanisms governing plant behaviour to inspection.
