Hydrogen Safety Prediction and Analysis of Hydrogen Refueling Station Leakage Accidents and Process Using Multi-Relevance Machine Learning
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...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-10-01
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| Series: | World Electric Vehicle Journal |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2032-6653/12/4/185 |
| _version_ | 1797499769411600384 |
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| author | Wujian Yang Jianghao Dong Yuke Ren |
| author_facet | Wujian Yang Jianghao Dong Yuke Ren |
| author_sort | Wujian Yang |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T03:52:11Z |
| format | Article |
| id | doaj.art-63c798513e014193930210b47615b06f |
| institution | Directory Open Access Journal |
| issn | 2032-6653 |
| language | English |
| last_indexed | 2024-03-10T03:52:11Z |
| publishDate | 2021-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | World Electric Vehicle Journal |
| spelling | doaj.art-63c798513e014193930210b47615b06f2023-11-23T11:02:48ZengMDPI AGWorld Electric Vehicle Journal2032-66532021-10-0112418510.3390/wevj12040185Hydrogen Safety Prediction and Analysis of Hydrogen Refueling Station Leakage Accidents and Process Using Multi-Relevance Machine LearningWujian Yang0Jianghao Dong1Yuke Ren2School of Computer and Computing Science, Zhejiang University City College, Hangzhou 310015, ChinaSchool of Computer and Computing Science, Zhejiang University City College, Hangzhou 310015, ChinaSchool of Computer and Computing Science, Zhejiang University City College, Hangzhou 310015, ChinaHydrogen 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.https://www.mdpi.com/2032-6653/12/4/185data acquisitionenergy safetyhydrogen safety |
| spellingShingle | Wujian Yang Jianghao Dong Yuke Ren Hydrogen Safety Prediction and Analysis of Hydrogen Refueling Station Leakage Accidents and Process Using Multi-Relevance Machine Learning World Electric Vehicle Journal data acquisition energy safety hydrogen safety |
| title | Hydrogen Safety Prediction and Analysis of Hydrogen Refueling Station Leakage Accidents and Process Using Multi-Relevance Machine Learning |
| title_full | Hydrogen Safety Prediction and Analysis of Hydrogen Refueling Station Leakage Accidents and Process Using Multi-Relevance Machine Learning |
| title_fullStr | Hydrogen Safety Prediction and Analysis of Hydrogen Refueling Station Leakage Accidents and Process Using Multi-Relevance Machine Learning |
| title_full_unstemmed | Hydrogen Safety Prediction and Analysis of Hydrogen Refueling Station Leakage Accidents and Process Using Multi-Relevance Machine Learning |
| title_short | Hydrogen Safety Prediction and Analysis of Hydrogen Refueling Station Leakage Accidents and Process Using Multi-Relevance Machine Learning |
| title_sort | hydrogen safety prediction and analysis of hydrogen refueling station leakage accidents and process using multi relevance machine learning |
| topic | data acquisition energy safety hydrogen safety |
| url | https://www.mdpi.com/2032-6653/12/4/185 |
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