Environmental Risk Assessment Using Neural Network in Liquefied Petroleum Gas Terminal
The accidental release of toxic gases leads to fire, explosion, and acute toxicity, and may result in severe problems for people and the environment. The risk analysis of hazardous chemicals using consequence modelling is essential to improve the process reliability and safety of the liquefied petro...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-04-01
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| Series: | Toxics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2305-6304/11/4/348 |
| _version_ | 1797603380792655872 |
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| author | Lalit Rajaramji Gabhane NagamalleswaraRao Kanidarapu |
| author_facet | Lalit Rajaramji Gabhane NagamalleswaraRao Kanidarapu |
| author_sort | Lalit Rajaramji Gabhane |
| collection | DOAJ |
| description | The accidental release of toxic gases leads to fire, explosion, and acute toxicity, and may result in severe problems for people and the environment. The risk analysis of hazardous chemicals using consequence modelling is essential to improve the process reliability and safety of the liquefied petroleum gas (LPG) terminal. The previous researchers focused on single-mode failure for risk assessment. No study exists on LPG plant multimode risk analysis and threat zone prediction using machine learning. This study aims to evaluate the fire and explosion hazard potential of one of Asia’s biggest LPG terminals in India. Areal locations of hazardous atmospheres (ALOHA) software simulations are used to generate threat zones for the worst scenarios. The same dataset is used to develop the artificial neural network (ANN) prediction model. The threats of flammable vapour cloud, thermal radiations from fire, and overpressure blast waves are estimated in two different weather conditions. A total of 14 LPG leak scenarios involving a 19 kg capacity cylinder, 21 tons capacity tank truck, 600 tons capacity mounded bullet, and 1350 tons capacity Horton sphere in the terminal are considered. Amongst all scenarios, the catastrophic rupture of the Horton sphere of 1350 MT capacity presented the most significant risk to life safety. Thermal flux of 37.5 kW/ m<sup>2</sup> from flames will damage nearby structures and equipment and spread fire by the domino effect. A novel soft computing technique called a threat and risk analysis-based ANN model has been developed to predict threat zone distances for LPG leaks. Based on the significance of incidents in the LPG terminal, 160 attributes were collected for the ANN modelling. The developed ANN model predicted the threat zone distance with an accuracy of R<sup>2</sup> value being 0.9958, and MSE being 202.9061 in testing. These results are evident in the reliability of the proposed framework for safety distance prediction. The LPG plant authorities can adopt this model to assess the safety distance from the hazardous chemical explosion based on the prior forecasted atmosphere conditions from the weather department. |
| first_indexed | 2024-03-11T04:28:22Z |
| format | Article |
| id | doaj.art-b71a05da1a5c44ba969b2a34803d10fd |
| institution | Directory Open Access Journal |
| issn | 2305-6304 |
| language | English |
| last_indexed | 2024-03-11T04:28:22Z |
| publishDate | 2023-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Toxics |
| spelling | doaj.art-b71a05da1a5c44ba969b2a34803d10fd2023-11-17T21:37:37ZengMDPI AGToxics2305-63042023-04-0111434810.3390/toxics11040348Environmental Risk Assessment Using Neural Network in Liquefied Petroleum Gas TerminalLalit Rajaramji Gabhane0NagamalleswaraRao Kanidarapu1School of Chemical Engineering, Vellore Institute of Technology, Vellore 632014, IndiaSchool of Chemical Engineering, Vellore Institute of Technology, Vellore 632014, IndiaThe accidental release of toxic gases leads to fire, explosion, and acute toxicity, and may result in severe problems for people and the environment. The risk analysis of hazardous chemicals using consequence modelling is essential to improve the process reliability and safety of the liquefied petroleum gas (LPG) terminal. The previous researchers focused on single-mode failure for risk assessment. No study exists on LPG plant multimode risk analysis and threat zone prediction using machine learning. This study aims to evaluate the fire and explosion hazard potential of one of Asia’s biggest LPG terminals in India. Areal locations of hazardous atmospheres (ALOHA) software simulations are used to generate threat zones for the worst scenarios. The same dataset is used to develop the artificial neural network (ANN) prediction model. The threats of flammable vapour cloud, thermal radiations from fire, and overpressure blast waves are estimated in two different weather conditions. A total of 14 LPG leak scenarios involving a 19 kg capacity cylinder, 21 tons capacity tank truck, 600 tons capacity mounded bullet, and 1350 tons capacity Horton sphere in the terminal are considered. Amongst all scenarios, the catastrophic rupture of the Horton sphere of 1350 MT capacity presented the most significant risk to life safety. Thermal flux of 37.5 kW/ m<sup>2</sup> from flames will damage nearby structures and equipment and spread fire by the domino effect. A novel soft computing technique called a threat and risk analysis-based ANN model has been developed to predict threat zone distances for LPG leaks. Based on the significance of incidents in the LPG terminal, 160 attributes were collected for the ANN modelling. The developed ANN model predicted the threat zone distance with an accuracy of R<sup>2</sup> value being 0.9958, and MSE being 202.9061 in testing. These results are evident in the reliability of the proposed framework for safety distance prediction. The LPG plant authorities can adopt this model to assess the safety distance from the hazardous chemical explosion based on the prior forecasted atmosphere conditions from the weather department.https://www.mdpi.com/2305-6304/11/4/348artificial neural networkconsequence modellingenvironmental riskflammable vapour cloudjet firevapor cloud explosion |
| spellingShingle | Lalit Rajaramji Gabhane NagamalleswaraRao Kanidarapu Environmental Risk Assessment Using Neural Network in Liquefied Petroleum Gas Terminal Toxics artificial neural network consequence modelling environmental risk flammable vapour cloud jet fire vapor cloud explosion |
| title | Environmental Risk Assessment Using Neural Network in Liquefied Petroleum Gas Terminal |
| title_full | Environmental Risk Assessment Using Neural Network in Liquefied Petroleum Gas Terminal |
| title_fullStr | Environmental Risk Assessment Using Neural Network in Liquefied Petroleum Gas Terminal |
| title_full_unstemmed | Environmental Risk Assessment Using Neural Network in Liquefied Petroleum Gas Terminal |
| title_short | Environmental Risk Assessment Using Neural Network in Liquefied Petroleum Gas Terminal |
| title_sort | environmental risk assessment using neural network in liquefied petroleum gas terminal |
| topic | artificial neural network consequence modelling environmental risk flammable vapour cloud jet fire vapor cloud explosion |
| url | https://www.mdpi.com/2305-6304/11/4/348 |
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