Um modelo computacional para a otimização da eficiência energética de um sistema metroferroviário utilizando algoritmos genéticos

• Ano de defesa: 2018
• Autor(a) principal: Martins, Marcelle Batista
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal da Paraíba; Brasil; Engenharia Mecânica; Programa de Pós-Graduação em Engenharia Mecânica; UFPB
• Palavras-chave em Português: Sistema metroferroviário; Eficiência energética; Modelagem matemática; Modelagem computacional; Algoritmo genético; Metroferroviário system; Energy efficiency; Mathematical modeling; Computational modeling; Genetic algorithm; CNPQ::ENGENHARIAS::ENGENHARIA MECANICA
• Link de acesso: https://repositorio.ufpb.br/jspui/handle/123456789/19869

The electric motors of the trains and wagons of a Metroferroviário System are fed by the electric energy, necessitating a high energy demand. In addition, this rail transport system has grown every day and with this, currently seek solutions to optimize the consumption of electricity. This work aims to present an analysis of the manual conduction of the TUEs (Electric Unit Trains) of the CBTU (Brazilian Company of Urban Trains) - Recife, as well as to demonstrate an analysis on the use of electric energy provided by the engines in the processes of acceleration and braking . To this end, a general model will be proposed that includes the analysis of speed profiles, where we also call the Speed Profile Optimization Model. This model includes empirical simulations with real data collected through sensors at METROREC stations and demonstration of the development of a Genetic Algorithm of Artificial Intelligence appropriate for the context. Thus, after presentation of this model, it will be validated with the help of software where the proposed model was implemented using the Java programming language. By simulating the real data, it is intended to demonstrate that controlling the speed profiles of trains, considering some limits, restrictions and parameters, will reduce the energy consumption in the metro-rail network, which will favor a more sustainable transport and a reduction of expenditure. The model can be adapted to other metro rail networks, but can also serve as a prototype for academia and industry.