A Graph-Enhanced DeepONet Approach for Real-Time Estimating Hydrogen-Enriched Natural Gas Flow under Variable Operations
Sicheng Liu, Hongchang Huang, Bo Yang, Mingxuan Cai, Xu Yang, Xinping Guan
TL;DR
This work tackles real-time estimation of hydrogen fraction in hydrogen-enriched natural gas (HENG) pipeline networks under variable operations. It introduces a graph-enhanced DeepONet that uses branch nets to encode partial initial/boundary conditions and a trunk net for location/time, combined with a graph neural network to leverage pipeline topology, enabling topology-aware, scalable state estimation. The framework maps inputs $(\mathbf{U},\mathbf{T})$ to the hydrogen fraction $\widehat{w}$, with training guided by an appropriate loss to minimize discrepancy with true states, and demonstrates improved accuracy and efficiency over conventional approaches. The approach supports safer and more efficient operation of HENG networks, facilitating renewable energy integration and decarbonization in practical pipeline systems.
Abstract
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.