Safety assessment: predicting fatality rates in methanol plant incidents

In this article, the prediction of fatality accident rate at methanol (MeOH) plant was studied using different assessment methods. The prediction method was performed and simulated using HYSYS, ALOHA, MARPLOT, and MATLAB software. Recent studies for pressure variation up to 442 bar in MeOH synthesis...

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發表在:Heliyon
Main Authors: Ahmad M.A., Rashid Z.A., Alzahrani A.A., El-Harbawi M.
格式: Article
語言:English
出版: Elsevier Ltd 2022
在線閱讀:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85144455300&doi=10.1016%2fj.heliyon.2022.e11610&partnerID=40&md5=2a86852aafde87cd54096dd8c4fca4e7
id 2-s2.0-85144455300
spelling 2-s2.0-85144455300
Ahmad M.A., Rashid Z.A., Alzahrani A.A., El-Harbawi M.
Safety assessment: predicting fatality rates in methanol plant incidents
2022
Heliyon
8
11
10.1016/j.heliyon.2022.e11610
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85144455300&doi=10.1016%2fj.heliyon.2022.e11610&partnerID=40&md5=2a86852aafde87cd54096dd8c4fca4e7
In this article, the prediction of fatality accident rate at methanol (MeOH) plant was studied using different assessment methods. The prediction method was performed and simulated using HYSYS, ALOHA, MARPLOT, and MATLAB software. Recent studies for pressure variation up to 442 bar in MeOH synthesis by carbon dioxide (CO2) hydrogenation showed that three times more MeOH was produced than in conventional plants, with 90% CO2 conversion and 95% MeOH selectivity. However, safety concerns were noted when MeOH production was operated at pressures above 76–500 bar. Therefore, a safety assessment of the pressures between 76 and 500 bar was performed to predict the fatality rate at the MeOH plant. Adaptive Neuro-Fuzzy Inference System (ANFIS) was compared with a conventional analysis by using the consequence method to predict the fatality rate. First, 26 input parameters were simulated in HYSYS, ALOHA, and MARPLOT software by using the consequence method. Then, the input parameters were reduced to six, namely, pressure, mass, volume, leakage size, wind speed, and wind direction, for prediction using ANFIS tool in MATLAB. This study aimed to highlight the accuracy of the fatality rate prediction by using the ANFIS method. In this manner, accurate prediction of fatality rate for MeOH plant incidents was achieved. The prediction values for the ANFIS method was validated using the standard error of the regression. The percent error measurement obtained the lowest regression of 0.0088 and the lowest percent error of 0.02% for Hydrogen (H2) Vapor Cloud Explosion (VCE) ident. Therefore, the ANFIS method was found to be a simpler and alternative prediction method for the fatality rate than the conventional consequence method. © 2022 Universiti Teknologi MARA; King Saud University
Elsevier Ltd
24058440
English
Article
All Open Access, Gold
author Ahmad M.A.
Rashid Z.A.
Alzahrani A.A.
El-Harbawi M.
spellingShingle Ahmad M.A.
Rashid Z.A.
Alzahrani A.A.
El-Harbawi M.
Safety assessment: predicting fatality rates in methanol plant incidents
author_facet Ahmad M.A.
Rashid Z.A.
Alzahrani A.A.
El-Harbawi M.
author_sort Ahmad M.A.
title Safety assessment: predicting fatality rates in methanol plant incidents
title_short Safety assessment: predicting fatality rates in methanol plant incidents
title_full Safety assessment: predicting fatality rates in methanol plant incidents
title_fullStr Safety assessment: predicting fatality rates in methanol plant incidents
title_full_unstemmed Safety assessment: predicting fatality rates in methanol plant incidents
title_sort Safety assessment: predicting fatality rates in methanol plant incidents
publishDate 2022
container_title Heliyon
container_volume 8
container_issue 11
doi_str_mv 10.1016/j.heliyon.2022.e11610
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85144455300&doi=10.1016%2fj.heliyon.2022.e11610&partnerID=40&md5=2a86852aafde87cd54096dd8c4fca4e7
description In this article, the prediction of fatality accident rate at methanol (MeOH) plant was studied using different assessment methods. The prediction method was performed and simulated using HYSYS, ALOHA, MARPLOT, and MATLAB software. Recent studies for pressure variation up to 442 bar in MeOH synthesis by carbon dioxide (CO2) hydrogenation showed that three times more MeOH was produced than in conventional plants, with 90% CO2 conversion and 95% MeOH selectivity. However, safety concerns were noted when MeOH production was operated at pressures above 76–500 bar. Therefore, a safety assessment of the pressures between 76 and 500 bar was performed to predict the fatality rate at the MeOH plant. Adaptive Neuro-Fuzzy Inference System (ANFIS) was compared with a conventional analysis by using the consequence method to predict the fatality rate. First, 26 input parameters were simulated in HYSYS, ALOHA, and MARPLOT software by using the consequence method. Then, the input parameters were reduced to six, namely, pressure, mass, volume, leakage size, wind speed, and wind direction, for prediction using ANFIS tool in MATLAB. This study aimed to highlight the accuracy of the fatality rate prediction by using the ANFIS method. In this manner, accurate prediction of fatality rate for MeOH plant incidents was achieved. The prediction values for the ANFIS method was validated using the standard error of the regression. The percent error measurement obtained the lowest regression of 0.0088 and the lowest percent error of 0.02% for Hydrogen (H2) Vapor Cloud Explosion (VCE) ident. Therefore, the ANFIS method was found to be a simpler and alternative prediction method for the fatality rate than the conventional consequence method. © 2022 Universiti Teknologi MARA; King Saud University
publisher Elsevier Ltd
issn 24058440
language English
format Article
accesstype All Open Access, Gold
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