Development of a multi-objective genetic algorithm to reduce individual risk and travelling time during evacuation in toxic cloud release scenarios

Detalhes bibliográficos
Ano de defesa: 2017
Autor(a) principal: SILVA, Erika Oliveira da
Orientador(a): Não Informado pela instituição
Banca de defesa: Não Informado pela instituição
Tipo de documento: Dissertação
Tipo de acesso: Acesso aberto
Idioma: eng
Instituição de defesa: Universidade Federal de Pernambuco
UFPE
Brasil
Programa de Pos Graduacao em Engenharia de Producao
Programa de Pós-Graduação: Não Informado pela instituição
Departamento: Não Informado pela instituição
País: Não Informado pela instituição
Palavras-chave em Português:
Engenharia de Produção
Evacuation route
Multi-objective optimization
Genetic algorithm
Evacuation time
Individual risk
Link de acesso: https://repositorio.ufpe.br/handle/123456789/29582
Resumo: The evacuation route planning is one of the protective actions that can be implemented in cases of hazardous substance leakage. Some toxic releases accidents that occurred recently in Brazil, such as in port of Santos, and release of a toxic gas in Cubatão, highlights the importance of an evacuation planning. Evacuation is the most complex mitigation measure so detailed analysis must be performed before planning. That is the reason the present work proposes a multiobjective optimization problem to give more information for the decision maker. The MOP aims to minimize both evacuation time and individual risk during evacuation due to a H₂S release in some of the treatment units in a hypothetical oil refinery. First, the possible accidental scenarios, causes and consequences are identified. After that, the scenarios with toxic cloud release and high severity are selected to be simulated in ALOHA® software in order to calculate the toxic concentration in each node of the evacuation route. The previous information is used in a multi-objective genetic algorithm written in C++ that results in a set of non-dominated solutions. Each solution was studied and the routes that both considered a good compromise between time and individual risk were selected.
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spelling Development of a multi-objective genetic algorithm to reduce individual risk and travelling time during evacuation in toxic cloud release scenariosEngenharia de ProduçãoEvacuation routeMulti-objective optimizationGenetic algorithmEvacuation timeIndividual riskThe evacuation route planning is one of the protective actions that can be implemented in cases of hazardous substance leakage. Some toxic releases accidents that occurred recently in Brazil, such as in port of Santos, and release of a toxic gas in Cubatão, highlights the importance of an evacuation planning. Evacuation is the most complex mitigation measure so detailed analysis must be performed before planning. That is the reason the present work proposes a multiobjective optimization problem to give more information for the decision maker. The MOP aims to minimize both evacuation time and individual risk during evacuation due to a H₂S release in some of the treatment units in a hypothetical oil refinery. First, the possible accidental scenarios, causes and consequences are identified. After that, the scenarios with toxic cloud release and high severity are selected to be simulated in ALOHA® software in order to calculate the toxic concentration in each node of the evacuation route. The previous information is used in a multi-objective genetic algorithm written in C++ that results in a set of non-dominated solutions. Each solution was studied and the routes that both considered a good compromise between time and individual risk were selected.CNPqO planejamento de rotas de evacuação é uma das ações de proteção que podem ser implementadas em casos de vazamento de substâncias perigosas. Alguns tóxicos liberados em acidentes ocorridos recentemente no Brasil, como no porto de Santos, e em Cubatão, destacam a importância de um planejamento de evacuação. A evacuação é a medida de mitigação mais complexa, e por essa razão uma análise detalhada deve ser realizada para ajudar no planejamento. Essa é a razão pela qual o presente trabalho propõe um problema de otimização multi-objetivo para dar mais informações ao tomador de decisão. O MOP visa minimizar o tempo de evacuação e o risco individual durante a evacuação devido a uma liberação de H₂S em algumas unidades de tratamento em uma refinaria de petróleo hipotética. Em primeiro lugar, são identificados os possíveis cenários acidentais, causas e consequências. Depois disso, os cenários com liberação de nuvem tóxica de alta severidade são selecionados para serem simulados no software ALOHA® e obter a concentração tóxica em cada nó da rota de evacuação. A informação anterior é utilizada em um algoritmo genético multi-objetivo escrito em C ++ que fornece como resultado um conjunto de soluções não-dominadas. Cada solução foi estudada e as rotas que consideraram um bom compromisso entre o tempo e o risco individual foram selecionadas.Universidade Federal de PernambucoUFPEBrasilPrograma de Pos Graduacao em Engenharia de ProducaoMOURA, Márcio José das Chagashttp://lattes.cnpq.br/1433428946420439http://lattes.cnpq.br/7778828466828647SILVA, Erika Oliveira da2019-03-07T22:35:17Z2019-03-07T22:35:17Z2017-12-19info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttps://repositorio.ufpe.br/handle/123456789/29582engAttribution-NonCommercial-NoDerivs 3.0 Brazilhttp://creativecommons.org/licenses/by-nc-nd/3.0/br/info:eu-repo/semantics/openAccessreponame:Repositório Institucional da UFPEinstname:Universidade Federal de Pernambuco (UFPE)instacron:UFPE2019-10-26T03:23:34Zoai:repositorio.ufpe.br:123456789/29582Repositório InstitucionalPUBhttps://repositorio.ufpe.br/oai/requestattena@ufpe.bropendoar:22212019-10-26T03:23:34Repositório Institucional da UFPE - Universidade Federal de Pernambuco (UFPE)false
dc.title.none.fl_str_mv Development of a multi-objective genetic algorithm to reduce individual risk and travelling time during evacuation in toxic cloud release scenarios
title Development of a multi-objective genetic algorithm to reduce individual risk and travelling time during evacuation in toxic cloud release scenarios
spellingShingle Development of a multi-objective genetic algorithm to reduce individual risk and travelling time during evacuation in toxic cloud release scenarios
SILVA, Erika Oliveira da
Engenharia de Produção
Evacuation route
Multi-objective optimization
Genetic algorithm
Evacuation time
Individual risk
title_short Development of a multi-objective genetic algorithm to reduce individual risk and travelling time during evacuation in toxic cloud release scenarios
title_full Development of a multi-objective genetic algorithm to reduce individual risk and travelling time during evacuation in toxic cloud release scenarios
title_fullStr Development of a multi-objective genetic algorithm to reduce individual risk and travelling time during evacuation in toxic cloud release scenarios
title_full_unstemmed Development of a multi-objective genetic algorithm to reduce individual risk and travelling time during evacuation in toxic cloud release scenarios
title_sort Development of a multi-objective genetic algorithm to reduce individual risk and travelling time during evacuation in toxic cloud release scenarios
author SILVA, Erika Oliveira da
author_facet SILVA, Erika Oliveira da
author_role author
dc.contributor.none.fl_str_mv MOURA, Márcio José das Chagas
http://lattes.cnpq.br/1433428946420439
http://lattes.cnpq.br/7778828466828647
dc.contributor.author.fl_str_mv SILVA, Erika Oliveira da
dc.subject.por.fl_str_mv Engenharia de Produção
Evacuation route
Multi-objective optimization
Genetic algorithm
Evacuation time
Individual risk
topic Engenharia de Produção
Evacuation route
Multi-objective optimization
Genetic algorithm
Evacuation time
Individual risk
description The evacuation route planning is one of the protective actions that can be implemented in cases of hazardous substance leakage. Some toxic releases accidents that occurred recently in Brazil, such as in port of Santos, and release of a toxic gas in Cubatão, highlights the importance of an evacuation planning. Evacuation is the most complex mitigation measure so detailed analysis must be performed before planning. That is the reason the present work proposes a multiobjective optimization problem to give more information for the decision maker. The MOP aims to minimize both evacuation time and individual risk during evacuation due to a H₂S release in some of the treatment units in a hypothetical oil refinery. First, the possible accidental scenarios, causes and consequences are identified. After that, the scenarios with toxic cloud release and high severity are selected to be simulated in ALOHA® software in order to calculate the toxic concentration in each node of the evacuation route. The previous information is used in a multi-objective genetic algorithm written in C++ that results in a set of non-dominated solutions. Each solution was studied and the routes that both considered a good compromise between time and individual risk were selected.
publishDate 2017
dc.date.none.fl_str_mv 2017-12-19
2019-03-07T22:35:17Z
2019-03-07T22:35:17Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
status_str publishedVersion
dc.identifier.uri.fl_str_mv https://repositorio.ufpe.br/handle/123456789/29582
url https://repositorio.ufpe.br/handle/123456789/29582
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv Attribution-NonCommercial-NoDerivs 3.0 Brazil
http://creativecommons.org/licenses/by-nc-nd/3.0/br/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Attribution-NonCommercial-NoDerivs 3.0 Brazil
http://creativecommons.org/licenses/by-nc-nd/3.0/br/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Universidade Federal de Pernambuco
UFPE
Brasil
Programa de Pos Graduacao em Engenharia de Producao
publisher.none.fl_str_mv Universidade Federal de Pernambuco
UFPE
Brasil
Programa de Pos Graduacao em Engenharia de Producao
dc.source.none.fl_str_mv reponame:Repositório Institucional da UFPE
instname:Universidade Federal de Pernambuco (UFPE)
instacron:UFPE
instname_str Universidade Federal de Pernambuco (UFPE)
instacron_str UFPE
institution UFPE
reponame_str Repositório Institucional da UFPE
collection Repositório Institucional da UFPE
repository.name.fl_str_mv Repositório Institucional da UFPE - Universidade Federal de Pernambuco (UFPE)
repository.mail.fl_str_mv attena@ufpe.br
_version_ 1856041932770246656

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