Navegando por Autor "FONSECA JUNIOR, Milton"

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Computational meta-heuristics based on Machine Learning to optimize fuel consumption of vessels using diesel engines
(Instituto de Tecnologia e Educação Galileo da Amazônia, 2021-05-01) SIQUEIRA JUNIOR, Paulo Oliveira; NASCIMENTO, Manoel Henrique Reis; SILVA, Ítalo Rodrigo Soares; PARENTE, Ricardo Silva; FONSECA JUNIOR, Milton; LEITE, Jandecy Cabral;
With the expansion of means of river transportation, especially in the caseof small and medium-sized vessels that make routes of greater distances, the cost of fuel, if not taken as an analysis criterion for a larger profit margin, is considered to be a primary factor , considering that the value of fuel specifically diesel to power internal combustion machines is high. Therefore, the use of tools that assist in decision making becomes necessary, as is the case of the present research, which aims to contribute with a computational model of prediction and optimization of the best speed to decrease the fuel cost considering the characteristics of the SCANIA 315 machine. propulsion model, of a vessel from the river port of Manaus that carries out river transportation to several municipalities in Amazonas. According to the results of the simulations, the best training algorithm of the Artificial Neural Network (ANN) was the BFGS Quasi-Newton considering the characteristics of the engine for optimization with Genetic Algorithm (AG).
Melhoria do Desempenho de Motores de Combustão Interna em Empresa de Geração de Energia - Via Implantação de Manutenção Preditiva
(Universidade Federal do Pará, 2011) FONSECA JUNIOR, Milton; BLANCO, Claudio José Cavalcante, Ph.D.
This work aims to describe the main issues related to implementation of TPM (Total Productive Maintenance) and PM (predictive maintenance) in power plants using internal combustion engines. The two types of planning were presented with emphasis on maintenance activities. Currently the generation planning of the company do not allow unplanned outage. The most common faults were as a result of unbalance, misalignment, improper setting, coupling defective and contaminated oil. Issues that were eliminated with the implementation of predictive maintenance and implementation of a TPM program. Emphasis was placed on the importance of four pillars in this methodology (specific improvements, Autonomous Maintenance, Planned Maintenance and Education & Training). By the use of this methodology, it was shown some results achieved after its implementation such as: reducing the annual cost of maintenance, reduction of corrective maintenance, increase of MTBF (Medium Time Between Failures) and reduction of MTTR (Mean Time To Repair) in all areas of our plant. These results are reflected in the power generation and more reliability for our customers.