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

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 l...

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Main Authors: Leiqi Zhang, Qiliang Wu, Min Liu, Hao Chen, Dianji Wang, Xuefang Li, Qingxin Ba
Format: Article
Language:English
Published: MDPI AG 2025-01-01
Series:Energies
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Online Access:https://www.mdpi.com/1996-1073/18/2/228
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author Leiqi Zhang
Qiliang Wu
Min Liu
Hao Chen
Dianji Wang
Xuefang Li
Qingxin Ba
author_facet Leiqi Zhang
Qiliang Wu
Min Liu
Hao Chen
Dianji Wang
Xuefang Li
Qingxin Ba
author_sort Leiqi Zhang
collection DOAJ
description 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.
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spelling doaj-art-aaedaace3a384697a4fb07bd2997320e2025-01-24T13:30:42ZengMDPI AGEnergies1996-10732025-01-0118222810.3390/en18020228Hydrogen Leakage Location Prediction in a Fuel Cell System of Skid-Mounted Hydrogen Refueling StationsLeiqi Zhang0Qiliang Wu1Min Liu2Hao Chen3Dianji Wang4Xuefang Li5Qingxin Ba6Research Institute of State Grid Zhejiang Electric Power Co., Ltd., Hangzhou 310014, ChinaResearch Institute of State Grid Zhejiang Electric Power Co., Ltd., Hangzhou 310014, ChinaResearch Institute of State Grid Zhejiang Electric Power Co., Ltd., Hangzhou 310014, ChinaInstitute of Thermal Science and Technology (Institute for Advanced Technology), Shandong University, Jinan 250061, ChinaInstitute of Thermal Science and Technology (Institute for Advanced Technology), Shandong University, Jinan 250061, ChinaInstitute of Thermal Science and Technology (Institute for Advanced Technology), Shandong University, Jinan 250061, ChinaInstitute of Thermal Science and Technology (Institute for Advanced Technology), Shandong University, Jinan 250061, ChinaHydrogen 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.https://www.mdpi.com/1996-1073/18/2/228skid-mounted hydrogen refueling stationfuel cell systemhydrogen leakageleakage locationneural networkSoftmax classifier
spellingShingle Leiqi Zhang
Qiliang Wu
Min Liu
Hao Chen
Dianji Wang
Xuefang Li
Qingxin Ba
Hydrogen Leakage Location Prediction in a Fuel Cell System of Skid-Mounted Hydrogen Refueling Stations
Energies
skid-mounted hydrogen refueling station
fuel cell system
hydrogen leakage
leakage location
neural network
Softmax classifier
title Hydrogen Leakage Location Prediction in a Fuel Cell System of Skid-Mounted Hydrogen Refueling Stations
title_full Hydrogen Leakage Location Prediction in a Fuel Cell System of Skid-Mounted Hydrogen Refueling Stations
title_fullStr Hydrogen Leakage Location Prediction in a Fuel Cell System of Skid-Mounted Hydrogen Refueling Stations
title_full_unstemmed Hydrogen Leakage Location Prediction in a Fuel Cell System of Skid-Mounted Hydrogen Refueling Stations
title_short Hydrogen Leakage Location Prediction in a Fuel Cell System of Skid-Mounted Hydrogen Refueling Stations
title_sort hydrogen leakage location prediction in a fuel cell system of skid mounted hydrogen refueling stations
topic skid-mounted hydrogen refueling station
fuel cell system
hydrogen leakage
leakage location
neural network
Softmax classifier
url https://www.mdpi.com/1996-1073/18/2/228
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