Physics-informed neural networks for state reconstruction of hydrogen energy transportation systems

Hydrogen energy, as one of the promising future energy forms, has attracted attentions from academia and industry due to its cost-effective and low-carbon nature. Compared with oil and gas transportation, its transportation is more challenging due to its complex blending mechanism. Inferring the internal states during transportation is essential for condition monitoring and operational planning of hydrogen-blending natural gas pipelines. Considering the nonlinear spatiotemporal dynamics and limited sensor information, reconstructing infinite-dimensional pipeline state variables is challenging. This paper addresses the state reconstruction of nonlinear infinite-dimensional hydrogen-blending natural gas pipeline systems using physics-informed neural networks. The proposed design combines neural networks with nonlinear partial differential equations that govern the pipeline systems. With limited measurements, the trained model is capable of predicting the state evolutions of pressure, flow, and mass flux ratio of hydrogen during transient transportation at any location. The proposed design is demonstrated through detailed numerical simulations and sensitivity analyses.