Hydrogen Safety in Practice

2016 ◽  
pp. 397-401
Author(s):  
Junichiro Yamabe ◽  
Nobuhiro Kuriyama
Keyword(s):  
Hydrogen Safety

Hydrogen safety retrofit of ASDEX Upgrade pellet centrifuge - Explosion prevention on fuelling devices

2021 ◽  
Vol 164 ◽  
pp. 112178
Author(s):  
B. Ploeckl ◽  
M. Sochor ◽  
A. Herrmann ◽  
S. Kilian ◽  
P.T. Lang ◽  
...  
Keyword(s):  
Hydrogen Safety

Hydrogen recombination scaling experiments at CNL’s hydrogen safety test facility

2021 ◽  
Vol 377 ◽  
pp. 111152
Author(s):  
L.B. Gardner ◽  
B. Ibeh ◽  
J. Murphy ◽  
J. Allain ◽  
S. Yeung ◽  
...  
Keyword(s):  
Test Facility ◽  
Safety Test ◽  
Hydrogen Recombination ◽  
Hydrogen Safety

Using hydrogen safety best practices and learning from safety events

2011 ◽  
Vol 36 (3) ◽  
pp. 2729-2735 ◽  
Author(s):  
S.C. Weiner ◽  
L.L. Fassbender ◽  
K.A. Quick
Keyword(s):  
Best Practices ◽  
Hydrogen Safety

Development and testing of a miniaturized hydrogen safety sensor prototype

2010 ◽  
Vol 148 (2) ◽  
pp. 469-477 ◽  
Author(s):  
Praveen K. Sekhar ◽  
Eric. L. Brosha ◽  
Rangachary Mukundan ◽  
Mark A. Nelson ◽  
Todd L. Williamson ◽  
...  
Keyword(s):  
Hydrogen Safety

Hydrogen Safety

Hydrogen Fuel ◽  
2008 ◽  
pp. 525-568 ◽  
Author(s):  
Fotis Rigas ◽  
Spyros Sklavounos
Keyword(s):  
Hydrogen Safety

scholarly journals Hydrogen Safety Prediction and Analysis of Hydrogen Refueling Station Leakage Accidents and Process Using Multi-Relevance Machine Learning

2021 ◽  
Vol 12 (4) ◽  
pp. 185
Author(s):  
Wujian Yang ◽  
Jianghao Dong ◽  
Yuke Ren
Keyword(s):  
Machine Learning ◽  
Gradient Descent ◽  
Prediction Algorithm ◽  
Hydrogen Energy ◽  
Neural Network Optimization ◽  
Gradient Descent Algorithm ◽  
Source Data ◽  
Using Data ◽  
Internal Relationships ◽  
Hydrogen Safety

Hydrogen energy vehicles are being increasingly widely used. To ensure the safety of hydrogenation stations, research into the detection of hydrogen leaks is required. Offline analysis using data machine learning is achieved using Spark SQL and Spark MLlib technology. In this study, to determine the safety status of a hydrogen refueling station, we used multiple algorithm models to perform calculation and analysis: a multi-source data association prediction algorithm, a random gradient descent algorithm, a deep neural network optimization algorithm, and other algorithm models. We successfully analyzed the data, including the potential relationships, internal relationships, and operation laws between the data, to detect the safety statuses of hydrogen refueling stations.

Developments in gas sensor technology for hydrogen safety

2014 ◽  
Vol 39 (35) ◽  
pp. 20474-20483 ◽  
Author(s):  
T. Hübert ◽  
L. Boon-Brett ◽  
V. Palmisano ◽  
M.A. Bader
Keyword(s):  
Gas Sensor ◽  
Sensor Technology ◽  
Hydrogen Safety
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