Research on intelligent prediction of hydrogen pipeline leakage fire based on Finite Ridgelet neural network

Zhao, Bin; Li, Shasha; Gao, Dayong; Xu, Lizhi; Zhang, Yuanyuan

doi:10.1016/j.ijhydene.2022.05.124

Cited by 9 publications

(1 citation statement)

References 38 publications

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“…The model was able to predict the hydrogen concentration distribution with an accuracy of R 2 = 0.9732 and a response time of 3.32 s. 227 Energy & Fuels Also, using neural networks, the parameters in the finite Ridgelet neural network can be optimized using a modified firefly algorithm with a position update mechanism, which in turn predicts the hydrogen leakage level in the space where the FCVs and HRSs are located. 228 The establishment of a hydrogen leakage database can provide rich data support for diagnosis, and the data sets of hydrogen concentration at different wind speeds and different distances are used to train the support vector regression model. In the scenario where the relative error of the database is in the range from 0% to 25%, a relative error of 9.8% in prediction can be realized.…”

Section: Sensor Fusion and Multimodal Leakage Diagnosismentioning

confidence: 99%

Comprehensive Review of Hydrogen Leakage in Relation to Fuel Cell Vehicles and Hydrogen Refueling Stations: Status, Challenges, and Future Prospects

Hu,

Gao,

Cheng

et al. 2024

Energy Fuels

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As fuel cell vehicles (FCVs) are increasingly put on the market and hydrogen refueling stations (HRSs) are built accordingly, fatal accidents caused by explosion due to hydrogen leakage are reported and have become a critical issue. The explosion results from complicated processes associated with the so-called hydrogen jet and diffusion when the hydrogen leaks from FCVs or HRSs, demonstrating its sophisticated characteristics and presenting significant technical challenges. Recently, particularly in the past a few years, researchers have established a variety of theoretical models to reveal the relevant mechanisms and introduced a series of monitoring/diagnostic approaches to detect and control the relevant hazards. This comprehensive review summarizes the major research outcomes and particularly the state-of-the-art progresses in relation to hydrogen leakage in FCVs and HRSs, including (1) subsonic jets, (2) underexpanded jets, (3) hydrogen diffusion behavior, (4) hazard reduction methods for confined and free spaces, (5) four types of widely used hydrogen detection technologies, and (6) the application of hydrogen leakage diagnostic methods in different systems. An insight of the review is that research on hydrogen leakage related to FCVs and HRSs should be combined with the relevant realistic characteristics. The characteristics of hydrogen jets generated in real gaps are discussed, and hazard reduction methods are proposed based on the characteristics of hydrogen diffusion in different confined and free spaces. It is suggested that, in order to integrate and evaluate multiple sensor data points and more accurately determine the leakage location and the leakage level, artificial intelligence technologies could be introduced to resolve the issues encountered currently.

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Section: Sensor Fusion and Multimodal Leakage Diagnosismentioning

confidence: 99%