Atmospheric Turbulent Dispersion Modeling Methods using Machine learning Tools
Assessment of likely consequences of a potential accident is a major concern for loss prevention and safety promotion in process industry. Loss of confinement on a storage tank, vessel or piping on industrial sites could imply atmospheric dispersion of toxic or flammable gases. Gas dispersion foreca...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
AIDIC Servizi S.r.l.
2014-04-01
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| Series: | Chemical Engineering Transactions |
| Online Access: | https://www.cetjournal.it/index.php/cet/article/view/5930 |
| _version_ | 1818877849953632256 |
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| author | P. Lauret F. Heymes L. Aprin A. Johannet G. Dusserre E. Lapebie A. Osmont |
| author_facet | P. Lauret F. Heymes L. Aprin A. Johannet G. Dusserre E. Lapebie A. Osmont |
| author_sort | P. Lauret |
| collection | DOAJ |
| description | Assessment of likely consequences of a potential accident is a major concern for loss prevention and safety promotion in process industry. Loss of confinement on a storage tank, vessel or piping on industrial sites could imply atmospheric dispersion of toxic or flammable gases. Gas dispersion forecasting is a difficult task since turbulence modeling at large scale involves expensive calculations. Therefore simpler models are used but remain inaccurate especially when turbulence is heterogeneous. The present work aims to study if Artificial Neural Networks coupled with Cellular Automata could be relevant to overcome these gaps. Two methods are reviewed and compared. An example database was designed from RANS k- e CFD model. Both methods were then applied. Their efficiencies are compared and discussed in terms of quality, real-time applicability and real-life plausibility. |
| first_indexed | 2024-12-19T14:04:50Z |
| format | Article |
| id | doaj.art-e940d69991ce4d7e89d5724e79bcbb82 |
| institution | Directory Open Access Journal |
| issn | 2283-9216 |
| language | English |
| last_indexed | 2024-12-19T14:04:50Z |
| publishDate | 2014-04-01 |
| publisher | AIDIC Servizi S.r.l. |
| record_format | Article |
| series | Chemical Engineering Transactions |
| spelling | doaj.art-e940d69991ce4d7e89d5724e79bcbb822022-12-21T20:18:21ZengAIDIC Servizi S.r.l.Chemical Engineering Transactions2283-92162014-04-013610.3303/CET1436087Atmospheric Turbulent Dispersion Modeling Methods using Machine learning ToolsP. LauretF. HeymesL. AprinA. JohannetG. DusserreE. LapebieA. OsmontAssessment of likely consequences of a potential accident is a major concern for loss prevention and safety promotion in process industry. Loss of confinement on a storage tank, vessel or piping on industrial sites could imply atmospheric dispersion of toxic or flammable gases. Gas dispersion forecasting is a difficult task since turbulence modeling at large scale involves expensive calculations. Therefore simpler models are used but remain inaccurate especially when turbulence is heterogeneous. The present work aims to study if Artificial Neural Networks coupled with Cellular Automata could be relevant to overcome these gaps. Two methods are reviewed and compared. An example database was designed from RANS k- e CFD model. Both methods were then applied. Their efficiencies are compared and discussed in terms of quality, real-time applicability and real-life plausibility.https://www.cetjournal.it/index.php/cet/article/view/5930 |
| spellingShingle | P. Lauret F. Heymes L. Aprin A. Johannet G. Dusserre E. Lapebie A. Osmont Atmospheric Turbulent Dispersion Modeling Methods using Machine learning Tools Chemical Engineering Transactions |
| title | Atmospheric Turbulent Dispersion Modeling Methods using Machine learning Tools |
| title_full | Atmospheric Turbulent Dispersion Modeling Methods using Machine learning Tools |
| title_fullStr | Atmospheric Turbulent Dispersion Modeling Methods using Machine learning Tools |
| title_full_unstemmed | Atmospheric Turbulent Dispersion Modeling Methods using Machine learning Tools |
| title_short | Atmospheric Turbulent Dispersion Modeling Methods using Machine learning Tools |
| title_sort | atmospheric turbulent dispersion modeling methods using machine learning tools |
| url | https://www.cetjournal.it/index.php/cet/article/view/5930 |
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