Safety Evaluation of Hydrogen Pipeline Transport: an Approach Based on Machine Learning
The issue of global warming imposes a change of paradigm in the energy sector to mitigate the human impact on the environment. In this perspective, hydrogen can be produced through water electrolysis and used in fuel-cell systems with near-zero pollutant emissions. Nevertheless, the distribution sys...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
AIDIC Servizi S.r.l.
2023-11-01
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| Series: | Chemical Engineering Transactions |
| Online Access: | http://www.cetjournal.it/index.php/cet/article/view/13759 |
| _version_ | 1797438155770560512 |
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| author | Abhishek Subedi Alessandro Campari Biraj Singh Thapa Nicola Paltrinieri |
| author_facet | Abhishek Subedi Alessandro Campari Biraj Singh Thapa Nicola Paltrinieri |
| author_sort | Abhishek Subedi |
| collection | DOAJ |
| description | The issue of global warming imposes a change of paradigm in the energy sector to mitigate the human impact on the environment. In this perspective, hydrogen can be produced through water electrolysis and used in fuel-cell systems with near-zero pollutant emissions. Nevertheless, the distribution system represents one of the main bottlenecks for a future transition to a hydrogen economy. The possibility of transporting hydrogen through the existing pipeline network is economically attractive. Nevertheless, most pipeline steels are prone to hydrogen-induced damage, and their mechanical properties are degraded by hydrogen gas to an extent that could result in sudden component failures. Hydrogen embrittlement can be responsible for undesired releases with potentially catastrophic consequences. This study evaluates the safety of existing European natural gas pipelines for hydrogen transport through machine learning tools. The material susceptibility to hydrogen embrittlement is predicted under different working conditions in order to prevent loss of material integrity and eventual releases. This study aims at bridging the gap between safety and material science, as it can optimize predictive maintenance of hydrogen pipelines, thus promoting the widespread utilization of hydrogen in the forthcoming years. |
| first_indexed | 2024-03-09T11:33:09Z |
| format | Article |
| id | doaj.art-fe1fc0a6971f47559b220ea98b90a81f |
| institution | Directory Open Access Journal |
| issn | 2283-9216 |
| language | English |
| last_indexed | 2024-03-09T11:33:09Z |
| publishDate | 2023-11-01 |
| publisher | AIDIC Servizi S.r.l. |
| record_format | Article |
| series | Chemical Engineering Transactions |
| spelling | doaj.art-fe1fc0a6971f47559b220ea98b90a81f2023-11-30T23:49:10ZengAIDIC Servizi S.r.l.Chemical Engineering Transactions2283-92162023-11-0110510.3303/CET23105021Safety Evaluation of Hydrogen Pipeline Transport: an Approach Based on Machine LearningAbhishek SubediAlessandro CampariBiraj Singh ThapaNicola PaltrinieriThe issue of global warming imposes a change of paradigm in the energy sector to mitigate the human impact on the environment. In this perspective, hydrogen can be produced through water electrolysis and used in fuel-cell systems with near-zero pollutant emissions. Nevertheless, the distribution system represents one of the main bottlenecks for a future transition to a hydrogen economy. The possibility of transporting hydrogen through the existing pipeline network is economically attractive. Nevertheless, most pipeline steels are prone to hydrogen-induced damage, and their mechanical properties are degraded by hydrogen gas to an extent that could result in sudden component failures. Hydrogen embrittlement can be responsible for undesired releases with potentially catastrophic consequences. This study evaluates the safety of existing European natural gas pipelines for hydrogen transport through machine learning tools. The material susceptibility to hydrogen embrittlement is predicted under different working conditions in order to prevent loss of material integrity and eventual releases. This study aims at bridging the gap between safety and material science, as it can optimize predictive maintenance of hydrogen pipelines, thus promoting the widespread utilization of hydrogen in the forthcoming years.http://www.cetjournal.it/index.php/cet/article/view/13759 |
| spellingShingle | Abhishek Subedi Alessandro Campari Biraj Singh Thapa Nicola Paltrinieri Safety Evaluation of Hydrogen Pipeline Transport: an Approach Based on Machine Learning Chemical Engineering Transactions |
| title | Safety Evaluation of Hydrogen Pipeline Transport: an Approach Based on Machine Learning |
| title_full | Safety Evaluation of Hydrogen Pipeline Transport: an Approach Based on Machine Learning |
| title_fullStr | Safety Evaluation of Hydrogen Pipeline Transport: an Approach Based on Machine Learning |
| title_full_unstemmed | Safety Evaluation of Hydrogen Pipeline Transport: an Approach Based on Machine Learning |
| title_short | Safety Evaluation of Hydrogen Pipeline Transport: an Approach Based on Machine Learning |
| title_sort | safety evaluation of hydrogen pipeline transport an approach based on machine learning |
| url | http://www.cetjournal.it/index.php/cet/article/view/13759 |
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