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

• Ano de defesa: 2017
• Autor(a) principal: SILVA, Erika Oliveira da
• 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
• 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

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.