用图结构增强神经网络，实时估算掺氢天然气浓度
A Graph-Enhanced DeepONet Approach for Real-Time Estimating Hydrogen-Enriched Natural Gas Flow under Variable Operations
- 双分支网络分别处理工况与稀疏传感器数据
- 图结构融合管网拓扑，提升大规模网络估计精度
- 适合需要实时监控掺氢天然气的能源系统
将绿氢掺入天然气是实现可再生能源整合与燃料脱碳的可行路径。准确估算掺氢天然气（HENG）管道网络中的氢气比例对运行安全与效率至关重要，但受复杂动态影响，仍具挑战。现有数据驱动方法多采用端到端架构进行状态估计，但在不同工况下适应性差，限制了实际应用。为此，本文提出一种图增强DeepONet框架，用于实时估算HENG流量，尤其关注氢气比例。首先，采用双网络架构——支路网络与主干网络，分别表征运行工况与稀疏传感器测量数据，以估计目标位置与时间点的HENG状态。其次，提出图增强支路网络，融合管道拓扑信息，提升大规模管网中的估计精度。实验结果表明，该方法在多种工况下对HCNG流量的估计精度优于传统方法。
原文摘要 · Abstract (English)
Blending green hydrogen into natural gas presents a promising approach for renewable energy integration and fuel decarbonization. Accurate estimation of hydrogen fraction in hydrogen-enriched natural gas (HENG) pipeline networks is crucial for operational safety and efficiency, yet it remains challenging due to complex dynamics. While existing data-driven approaches adopt end-to-end architectures for HENG flow state estimation, their limited adaptability to varying operational conditions hinders practical applications. To this end, this study proposes a graph-enhanced DeepONet framework for the real-time estimation of HENG flow, especially hydrogen fractions. First, a dual-network architecture, called branch network and trunk network, is employed to characterize operational conditions and sparse sensor measurements to estimate the HENG state at targeted locations and time points. Second, a graph-enhance branch network is proposed to incorporate pipeline topology, improving the estimation accuracy in large-scale pipeline networks. Experimental results demonstrate that the proposed method achieves superior estimation accuracy for HCNG flow under varying operational conditions compared to conventional approaches.
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