Deep learning-based hydrogen leakage localization prediction considering sensor layout optimization in hydrogen refueling stations

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

Published: June 27, 2024

Language: Английский

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

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

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Potential applications of innovative AI-based tools in hydrogen energy development: Leveraging large language model technologies

International Journal of Hydrogen Energy, Journal Year: 2025, Volume and Issue: 102, P. 918 - 936

Published: Jan. 12, 2025

Language: Английский

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Artificial Intelligence-Driven Innovations in Hydrogen Safety

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

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Deep learning-based source term estimation of hydrogen leakages from a hydrogen fueled gas turbine

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

Published: Sept. 3, 2024

Language: Английский

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Hydrogen Refueling Siting: A Case Study from China on the Influence of Commercial Entities

Published: Jan. 1, 2025

This study introduces machine-learning techniques to analyze the impact of surrounding commercial entities on hydrogen refueling station (HRS) site selection. Through field research and data fusion, we establish a large-scale multi-entity dataset use random forest (RF) algorithm quantify importance influencing factors, thereby overcoming subjectivity bias in existing studies. The cross-validation results suggest that RF model has high stability generalizability for HRS In addition, excels classification tasks maintains consistent performance across different datasets. provides valuable insights into selection by incorporating entity leveraging techniques.

Language: Английский

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Hydrogen leakage location prediction for fuel cell vehicles in parking lots: A combined study of CFD simulation and CNN-BiLSTM modeling

International Journal of Hydrogen Energy, Journal Year: 2025, Volume and Issue: 109, P. 115 - 128

Published: Feb. 10, 2025

Language: Английский

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Artificial intelligence and robotics in the hydrogen lifecycle: A systematic review

International Journal of Hydrogen Energy, Journal Year: 2025, Volume and Issue: 113, P. 801 - 817

Published: March 1, 2025

Language: Английский

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Experimental study and prediction model on mass variation when hydrogen released in an enclosure with two vents

International Journal of Hydrogen Energy, Journal Year: 2025, Volume and Issue: unknown

Published: March 1, 2025

Language: Английский

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Real-time monitoring using digital platforms for enhanced safety in hydrogen facilities – Current perspectives and future directions

International Journal of Hydrogen Energy, Journal Year: 2024, Volume and Issue: 98, P. 487 - 499

Published: Dec. 10, 2024

Language: Английский

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