Prediction model for the evolution of hydrogen concentration under leakage in hydrogen refueling station using deep neural networks

He, Xu; Kong, Depeng; Yu, Xirui; Ping, Ping; Wang, Gongquan; Peng, Rongqi; Zhang, Yue; Dai, Xinyi

doi:10.1016/j.ijhydene.2022.12.102

Cited by 18 publications

(3 citation statements)

References 31 publications

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“…The results show that the theoretical framework can be used for leak prediction and the optimal design of hydrogen storage systems. He et al 223 proposed a physics-informed Convolutional Long Short-Term Memory Network to predict the concentration distribution after hydrogen leakage occurred in FCVs and HRSs. The concentration distribution after hydrogen leakage was simulated numerically using FLACS, and the data were obtained as grayscale images.…”

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%

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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show abstract

“…Moreover, He [21] introduced a physics-informed surrogate model for predicting hydrogen leak consequences, leveraging the power of neural networks to offer real-time risk warnings. Lastly, the development of an AI-enabled optical sensor for detecting low levels of H 2 by Swedish and Dutch researchers [22] marks a significant breakthrough, emphasizing the transformative potential of AI in ensuring safety across various sectors.…”

Section: Conventional Techniquesmentioning

confidence: 99%

Artificial Intelligence-Driven Innovations in Hydrogen Safety

Patil,

Calay,

Mustafa

et al. 2024

Hydrogen

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This review explores recent advancements in hydrogen gas (H2) safety through the lens of artificial intelligence (AI) techniques. As hydrogen gains prominence as a clean energy source, ensuring its safe handling becomes paramount. The paper critically evaluates the implementation of AI methodologies, including artificial neural networks (ANN), machine learning algorithms, computer vision (CV), and data fusion techniques, in enhancing hydrogen safety measures. By examining the integration of wireless sensor networks and AI for real-time monitoring and leveraging CV for interpreting visual indicators related to hydrogen leakage issues, this review highlights the transformative potential of AI in revolutionizing safety frameworks. Moreover, it addresses key challenges such as the scarcity of standardized datasets, the optimization of AI models for diverse environmental conditions, etc., while also identifying opportunities for further research and development. This review foresees faster response times, reduced false alarms, and overall improved safety for hydrogen-related applications. This paper serves as a valuable resource for researchers, engineers, and practitioners seeking to leverage state-of-the-art AI technologies for enhanced hydrogen safety systems.

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“…With the advancement of computer vision technology, leakage segmentation has become crucial for intelligent industrial production [1][2][3]. Sauce-packet leakage segmentation is to determine whether a sauce-packet has leakage [4]. Unlike other segmentation scenarios, sauce-packet leakage segmentation faces issues of overexposure or insufficient illumination, resulting in blurred images.…”

Section: Introductionmentioning

confidence: 99%

ISLS: An Illumination-aware Sauce-packet Leakage Segmentation Method

You,

Lin,

Feng

et al. 2024

Preprint

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The segmentation of abnormal regions is vital in smart manufacturing. However, the existing segmentation system for detecting sauce-packet leakage on intelligent sensors encounters an issue of imaging blurring caused by uneven illumination. This issue adversely affects segmentation performance, thereby impeding the rapid production of industrial assembly lines. To alleviate this issue, we propose the two-stage Illumination-aware Sauce-packet Leakage Segmentation (ISLS) method for intelligent sensors. The ISLS comprises two main stages: Illumination-aware region enhancement and leakage region segmentation. In the first stage, YOLO-Fastestv2 is employed to capture the Region of Interest (ROI), which reduces redundancy computations. Additionally, we propose an image enhancement to relieve the impact of uneven illumination, enhancing the texture details of ROI. In the second stage, we propose a novel feature extraction network. Specifically, we propose the Multi-scale Feature Fusion Module (MFFM) and the Sequential Self-Attention Mechanism (SSAM) to capture discriminative representations of leakage. The multi-level features are fused by MFFM with a small number of parameters, which capture leakage semantics at different scales. The SSAM realizes the enhancement of valid features and the suppression of invalid features by adaptive weighting of spatial and channel dimensions. Furthermore, we generated a self-built dataset of sauce-packets, including 606 images with various leakage areas. Comprehensive experiments demonstrate that our ISLS method shows better results than several state-of-the-art methods, with additional performance analyses deployed on intelligent sensors to affirm the effectiveness of our proposed method. Our code is available at https://github.com/LSJ5106/SauceDetect.

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Prediction model for the evolution of hydrogen concentration under leakage in hydrogen refueling station using deep neural networks

Cited by 18 publications

References 31 publications

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

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

Artificial Intelligence-Driven Innovations in Hydrogen Safety

ISLS: An Illumination-aware Sauce-packet Leakage Segmentation Method

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