Performance analysis of optimized machine learning models for hydrogen leakage and dispersion prediction via genetic algorithms DOI
Junseo Lee, Sehyeon Oh, Byung-Chol Ma

et al.

International Journal of Hydrogen Energy, Journal Year: 2024, Volume and Issue: 97, P. 1287 - 1301

Published: Dec. 5, 2024

Language: Английский

Experimental and numerical studies on hydrogen leakage and dispersion evolution characteristics in space with large aspect ratios DOI
Qiming Xu, Guohua Chen, Mulin Xie

et al.

Journal of Cleaner Production, Journal Year: 2024, Volume and Issue: 438, P. 140467 - 140467

Published: Jan. 1, 2024

Language: Английский

Citations

15
Prediction of hydrogen leakage location and intensity in hydrogen refueling stations based on deep learning DOI

Guodong Yang,

Depeng Kong, Xu He

et al.

International Journal of Hydrogen Energy, Journal Year: 2024, Volume and Issue: 68, P. 209 - 220

Published: April 26, 2024

Language: Английский

Citations

15
Hydrogen leakage source positioning method in deep belief network based on fully confined space Gaussian distribution model DOI
Jiaming Zhou, Jinming Zhang, Junling Zhang

et al.

International Journal of Hydrogen Energy, Journal Year: 2024, Volume and Issue: 63, P. 435 - 445

Published: March 20, 2024

Language: Английский

Citations

13
Deep learning-based dispersion prediction model for hazardous chemical leaks using transfer learning DOI

Xiaoyi Han,

Jiaxing Zhu,

Haosen Li

et al.

Process Safety and Environmental Protection, Journal Year: 2024, Volume and Issue: 188, P. 363 - 373

Published: May 28, 2024

Language: Английский

Citations

11
Deep learning-based hydrogen leakage localization prediction considering sensor layout optimization in hydrogen refueling stations DOI
Shilu Wang,

Yubo Bi,

Jihao Shi

et al.

Process Safety and Environmental Protection, Journal Year: 2024, Volume and Issue: 189, P. 549 - 560

Published: June 27, 2024

Language: Английский

Citations

9
Hydrogen Leakage Location Prediction in a Fuel Cell System of Skid-Mounted Hydrogen Refueling Stations DOI Creative Commons

Leiqi Zhang,

Qiliang Wu, Min Liu

et al.

Energies, Journal Year: 2025, Volume and Issue: 18(2), P. 228 - 228

Published: Jan. 7, 2025

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

Language: Английский

Citations

1
Artificial Intelligence-Driven Innovations in Hydrogen Safety DOI Creative Commons
Ravindra R. Patil, Rajnish Kaur Calay, Mohamad Y. Mustafa

et al.

Hydrogen, Journal Year: 2024, Volume and Issue: 5(2), P. 312 - 326

Published: June 8, 2024

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

Language: Английский

Citations

8
Performance assessment and optimisation of a concentric two-piston compressor system for hydrogen storage DOI
Yi Guo, Qi Wang,

Junhao Cao

et al.

Energy Conversion and Management, Journal Year: 2024, Volume and Issue: 310, P. 118470 - 118470

Published: April 26, 2024

Language: Английский

Citations

4
4E analysis of novel direct expansion cycle-organic Rankine cycle-integrated hydrogen refueling station system using liquid hydrogen cold energy DOI
Hyun Seung Kim

Journal of Cleaner Production, Journal Year: 2024, Volume and Issue: 462, P. 142682 - 142682

Published: May 25, 2024

Language: Английский

Citations

4
Deep learning-based source term estimation of hydrogen leakages from a hydrogen fueled gas turbine DOI
A. Li, Zi–Qiang Lang,

Chuantao Ni

et al.

International Journal of Hydrogen Energy, Journal Year: 2024, Volume and Issue: 86, P. 875 - 889

Published: Sept. 3, 2024

Language: Английский

Citations

4