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).
Downloads
References
ABD ELAZIZ, Mohamed; EWEES, Ahmed A.; OLIVA, Diego. Hyper-heuristic method for multilevel thresholding image segmentation. Expert Systems with Applications, v. 146, p. 113201, 2020. DOI: https://doi.org/10.1016/j.eswa.2020.113201
BASKOV, Vladimir; IGNATOV, Anton; POLOTNYANSCHIKOV, Vladislav. Assessing the influence of operating factors on the properties of engine oil and the environmental safety of internal combustion engine. Transportation Research Procedia, v. 50, p. 37-43, 2020. DOI: https://doi.org/10.1016/j.trpro.2020.10.005
BERTONI JUNIOR, Ivan Luiz. Análise de desempenho e emissões de um motor ciclo diesel operando com fumigação de água e etanol super-hidratado. 2020.
BOOB, Digvijay; DEY, Santanu S.; LAN, Guanghui. Complexity of training relu neural network. Discrete Optimization, p. 100620, 2020. DOI: https://doi.org/10.1016/j.disopt.2020.100620
BRUNETTI, Franco. Introdução ao estudo dos motores de combustão interna. In: MOTORES DE COMBUSTÃO INTERNA VOLUME 1, São Paulo, Edgard Blücher Ltda. 2018.
CHEN, Ming et al. Heuristic algorithms based on deep reinforcement learning for quadratic unconstrained binary optimization. Knowledge-Based Systems, v. 207, p. 106366, 2020. DOI: https://doi.org/10.1016/j.knosys.2020.106366
DADA, Emmanuel Gbenga et al. Predicting protein secondary structure based on ensemble Neural Network. ITEGAM-JETIA, v. 7, n. 27, p. 49-56, 2021. DOI: https://doi.org/10.5935/jetia.v7i27.732
DALPRÁ, Agustinho J. et al. Experimentação planejada para análise dos fatores que influenciam os pontos ótimos de funcionamento de um motor de combustão interna. 2020.
DELGADO-HIDALGO, Liliana; RAINWATER, Chase; NACHTMANN, Heather. A computational comparison of cargo prioritization and terminal allocation problem models. Computers & Industrial Engineering, v. 144, p. 106450, 2020. DOI: https://doi.org/10.1016/j.cie.2020.106450
FAGUNDEZ, J. L. S. et al. Joint use of artificial neural networks and particle swarm optimization to determine optimal performance of an ethanol SI engine operating with negative valve overlap strategy. Energy, v. 204, p. 117892, 2020. DOI: https://doi.org/10.1016/j.energy.2020.117892
GAO, K. Z. et al. A survey on meta-heuristics for solving disassembly line balancing, planning and scheduling problems in remanufacturing. Swarm and Evolutionary Computation, v. 57, p. 100719, 2020. DOI: https://doi.org/10.1016/j.swevo.2020.100719
HATAMI, Mohammad; HASANPOUR, Maryam; JING, Dengwei. Recent developments of nanoparticles additives to the consumables liquids in internal combustion engines: Part I: Nano-fuels. Journal of Molecular Liquids, p. 114250, 2020. DOI: https://doi.org/10.1016/j.molliq.2020.114250
HOSSEINIOUN, Pejman et al. A new energy-aware tasks scheduling approach in fog computing using hybrid meta-heuristic algorithm. Journal of Parallel and Distributed Computing, v. 143, p. 88-96, 2020. DOI: https://doi.org/10.1016/j.jpdc.2020.04.008
HUANG, Renfang et al. Energy performance prediction of the centrifugal pumps by using a hybrid neural network. Energy, v. 213, p. 119005, 2020. DOI: https://doi.org/10.1016/j.energy.2020.119005
HUI, Tianyu; ZENG, Wenjie; YU, Tao. Core power control of the ADS based on genetic algorithm tuning PID controller. Nuclear Engineering and Design, v. 370, p. 110835, 2020. DOI: https://doi.org/10.1016/j.nucengdes.2020.110835
JUNIOR, José Tomadon; CALLADO, Raphael Tavares. Avaliação comparativa do ciclo de vida de materiais usados em blocos de motor. Tópicos em Administração Volume 34, p. 43. 2020.
Koçak, Y., & Üstündağ Şiray, G. New activation functions for single layer feedforward neural network. Expert Systems with Applications, 164, 113977. doi:10.1016/j.eswa.2020.113977. 2021. DOI: https://doi.org/10.1016/j.eswa.2020.113977
MENZEL, Germano et al. Multi-objective optimization of the volumetric and thermal efficiencies applied to a multi-cylinder internal combustion engine. Energy Conversion and Management, v. 216, p. 112930, 2020. DOI: https://doi.org/10.1016/j.enconman.2020.112930
MOHAMMADI, Farzaneh et al. Modeling and sensitivity analysis of the alkylphenols removal via moving bed biofilm reactor using artificial neural networks: Comparison of levenberg marquardt and particle swarm optimization training algorithms. Biochemical Engineering Journal, v. 161, p. 107685, 2020. DOI: https://doi.org/10.1016/j.bej.2020.107685
NASCIMENTO, Manoel Henrique Reis et al. New solution for resolution of the economic load dispatch by different mathematical optimization methods, turning off the less efficient generators. Journal of Engineering and Tecnology for Industrial Aplications, v. 3, p. 10, 2017. DOI: https://doi.org/10.5935/2447-0228.20170061
OSABA, Eneko et al. Community detection in networks using bio-inspired optimization: Latest developments, new results and perspectives with a selection of recent meta-heuristics. Applied Soft Computing, v. 87, p. 106010, 2020. DOI: https://doi.org/10.1016/j.asoc.2019.106010
ROME2RIO. https://www.rome2rio.com/pt/. Consultado em: 08/04/2021.
RUFINO, Caio Henrique et al. Conceptual study of an internal combustion engine with adjustable cubic capacity and compression ratio: Estudo conceitual de um motor com cilindrada e taxa de compressão ajustáveis. 2020.
SCANIA. DC09 074A. 232 kW (315 hp) EU Stage II, China Phase II and Russia Stage I. 2020.
SCHELLENBERG, Christoph; LOHAN, John; DIMACHE, Laurentiu. Comparison of metaheuristic optimisation methods for grid-edge technology that leverages heat pumps and thermal energy storage. Renewable and Sustainable Energy Reviews, v. 131, p. 109966, 2020. DOI: https://doi.org/10.1016/j.rser.2020.109966
SHEYKHI, Mohammad et al. Investigation of the effects of operating parameters of an internal combustion engine on the performance and fuel consumption of a CCHP system. Energy, v. 211, p. 119041, 2020. DOI: https://doi.org/10.1016/j.energy.2020.119041
SILVA, Jean da Silva de Abreu et al. PROPOSTA DE IMPLANTAÇÃO DE SISTEMA DE PROTEÇÃO CONTRA POTENCIAL DE FALHA DO MOTOR À DIESEL (DISPARO DO MOTOR). ITEGAM-JETIA, v. 5, n. 19, p. 06-11, 2019.
SINGH, Akhilendra Pratap; KUMAR, Vikram; AGARWAL, Avinash Kumar. Evaluation of comparative engine combustion, performance and emission characteristics of low temperature combustion (PCCI and RCCI) modes. Applied Energy, v. 278, p. 115644, 2020. DOI: https://doi.org/10.1016/j.apenergy.2020.115644
TAN, Roy; DURU, Okan; THEPSITHAR, Prapisala. Assessment of relative fuel cost for dual fuel marine engines along major Asian container shipping routes. Transportation Research Part E: Logistics and Transportation Review, v. 140, p. 102004, 2020. DOI: https://doi.org/10.1016/j.tre.2020.102004
WANG, Xiaoming et al. Meta-heuristics for unrelated parallel machines scheduling with random rework to minimize expected total weighted tardiness. Computers & Industrial Engineering, v. 145, p. 106505, 2020. DOI: https://doi.org/10.1016/j.cie.2020.106505
ZF. Reversor Marítimo Marine Gearbox ZF W220. 2020.
Downloads
Published
Issue
Section
License
Copyright (c) 2021 Paulo Oliveira Siqueira Junior, Manoel Henrique Reis Nascimento, Ítalo Rodrigo Soares Silva, Ricardo Silva Parente, Milton Fonseca Júnior, Jandecy Cabral Leite
This work is licensed under a Creative Commons Attribution-NoDerivatives 4.0 International License.
Copyrights for articles published in IJIER journals are retained by the authors, with first publication rights granted to the journal. The journal/publisher is not responsible for subsequent uses of the work. It is the author's responsibility to bring an infringement action if so desired by the author for more visit Copyright & License.
How to Cite
Accepted 2021-04-29
Published 2021-05-01
