Hydrogen Leakage Location Prediction in a Fuel Cell System of Skid-Mounted Hydrogen Refueling Stations

Leiqi Zhang, Qiliang Wu, Min Liu, Hao Chen, Dianji Wang, Xuefang Li and Qingxin Ba ()
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Leiqi Zhang: Research Institute of State Grid Zhejiang Electric Power Co., Ltd., Hangzhou 310014, China
Qiliang Wu: Research Institute of State Grid Zhejiang Electric Power Co., Ltd., Hangzhou 310014, China
Min Liu: Research Institute of State Grid Zhejiang Electric Power Co., Ltd., Hangzhou 310014, China
Hao Chen: Institute of Thermal Science and Technology (Institute for Advanced Technology), Shandong University, Jinan 250061, China
Dianji Wang: Institute of Thermal Science and Technology (Institute for Advanced Technology), Shandong University, Jinan 250061, China
Xuefang Li: Institute of Thermal Science and Technology (Institute for Advanced Technology), Shandong University, Jinan 250061, China
Qingxin Ba: Institute of Thermal Science and Technology (Institute for Advanced Technology), Shandong University, Jinan 250061, China

Energies, 2025, vol. 18, issue 2, 1-17

Abstract: Hydrogen safety is a critical issue during the construction and development of the hydrogen energy industry. Hydrogen refueling stations play a pivotal role in the hydrogen energy chain. In the event of an accidental hydrogen leak at a hydrogen refueling station, the ability to quickly predict the leakage location is crucial for taking immediate and effective measures to prevent disastrous consequences. Therefore, the development of precise and efficient technologies to predict leakage locations is vital for the safe and stable operation of hydrogen refueling stations. This paper studied the localization technology of high-risk leakage locations in the fuel cell system of a skid-mounted hydrogen refueling station. The hydrogen leakage and diffusion processes in the fuel cell system were predicted using CFD simulations, and the hydrogen concentration data at various monitoring points were obtained. Then, a multilayer feedforward neural network was developed to predict leakage locations using simulated concentration data as training samples. After multiple adjustments to the network structure and hyperparameters, a final model with two hidden layers was selected. Each hidden layer consisted of 10 neurons. The hyperparameters included a learning rate of 0.0001, a batch size of 32, and 10-fold cross-validation. The Softmax classifier and Adam optimizer were used, with a training set for 1500 epochs. The results show that the algorithm can predict leakage locations not included in the training set. The accuracy achieved by the model was 95%. This approach addresses the limitations of sensor detection in accurately locating leaks and mitigates the risks associated with manual inspections. This paper provides a feasible method for locating hydrogen leakage in hydrogen energy application scenarios.

Keywords: skid-mounted hydrogen refueling station; fuel cell system; hydrogen leakage; leakage location; neural network; Softmax classifier; Adam optimizer (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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