Computational meta-heuristics based on Machine Learning to optimize fuel consumption of vessels using diesel engines
DOI:
https://doi.org/10.31686/ijier.vol9.iss5.3128Keywords:
Internal Combustion Engine (MCI), Optimization and Forecasting, Artificial Neural Networks (RNA), Genetic Algorithm, Meta-heuristics of computingAbstract
With the expansion of means of river transportation, especially in the case of 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).
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Copyright (c) 2021 Paulo Oliveira Siqueira Junior, Manoel Henrique Reis Nascimento, Ítalo Rodrigo Soares Silva, Ricardo Silva Parente, Milton Fonseca Júnior, Jandecy Cabral Leite
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Accepted 2021-04-29
Published 2021-05-01
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