Computational meta-heuristics based on Machine Learning to optimize fuel consumption of vessels using diesel engines

Authors

  • Paulo Oliveira Siqueira Junior Institute of Technology and Education and Galileo of Amazon
  • Manoel Henrique Reis Nascimento ITEGAM
  • Ítalo Rodrigo Soares Silva ITEGAM
  • Ricardo Silva Parente ITEGAM
  • Milton Fonseca Júnior ITEGAM
  • Jandecy Cabral Leite ITEGAM

DOI:

https://doi.org/10.31686/ijier.vol9.iss5.3128

Keywords:

Internal Combustion Engine (MCI), Optimization and Forecasting, Artificial Neural Networks (RNA), Genetic Algorithm, Meta-heuristics of computing

Abstract

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

Download data is not yet available.

Author Biographies

Paulo Oliveira Siqueira Junior, Institute of Technology and Education and Galileo of Amazon

Student of the Graduate Program in Engineering, Process, Systems and Environmental Management

Manoel Henrique Reis Nascimento, ITEGAM

PhD in Electrical Engineering from the Graduate Program in Engineering, Process, Systems and Environmental Management

Ítalo Rodrigo Soares Silva, ITEGAM

Student of the Graduate Program in Engineering, Process, Systems and Environmental Management

Ricardo Silva Parente, ITEGAM

Student of the Graduate Program in Engineering, Process, Systems and Environmental Management

Milton Fonseca Júnior, ITEGAM

PhD in Electrical Engineering from the Graduate Program in Engineering, Process, Systems and Environmental Management

Jandecy Cabral Leite, ITEGAM

PhD in Electrical Engineering from the Graduate Program in Engineering, Process, Systems and Environmental Management

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.

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.

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.

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.

DADA, Emmanuel Gbenga et al. Predicting protein secondary structure based on ensemble Neural Network. ITEGAM-JETIA, v. 7, n. 27, p. 49-56, 2021.

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.

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.

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.

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.

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.

HUANG, Renfang et al. Energy performance prediction of the centrifugal pumps by using a hybrid neural network. Energy, v. 213, p. 119005, 2020.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

ZF. Reversor Marítimo Marine Gearbox ZF W220. 2020.

Downloads

Published

01-05-2021

How to Cite

Junior, P. O. S., Nascimento, M. H. R., Silva, Ítalo R. S., Parente, R. S., Júnior, M. F., & Leite, J. C. (2021). Computational meta-heuristics based on Machine Learning to optimize fuel consumption of vessels using diesel engines. International Journal for Innovation Education and Research, 9(5), 587–606. https://doi.org/10.31686/ijier.vol9.iss5.3128

Most read articles by the same author(s)