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Applying an improved particle swarm optimization algorithm to ship energy saving

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  • Du, Wei
  • Li, Yanjun
  • Shi, Jianxin
  • Sun, Baozhi
  • Wang, Chunhui
  • Zhu, Baitong

Abstract

Due to the increasingly competitive maritime market and stringent regulatory requirements, the optimization of ship energy efficiency is attracting more and more attention. The energy efficiency of ship navigation is affected by many factors such as ship structure, crew operation and navigation environment. In this paper, the proposed improved second-order oscillating PSO algorithm is used to study the ship energy efficiency from the viewpoint of route optimization by considering the sea conditions and constraints. Firstly, a nonlinear optimization model for ship FOC (fuel oil consumption) considering the time-varying sea state is established. On this basis, the energy efficiency and economic benefits are analyzed in terms of multiple indicators e.g., FOC and CO2 emissions per unit distance and per unit mass of freight. Finally, the energy saving potential of the method is demonstrated with an example of an oil tanker. The results show that both FOC and emissions are reduced after optimization, and energy efficiency and economy are improved by 1.17% and 2.55%, respectively. This indicated that the considerable effect of the proposed method applied to ship energy saving optimization.

Suggested Citation

  • Du, Wei & Li, Yanjun & Shi, Jianxin & Sun, Baozhi & Wang, Chunhui & Zhu, Baitong, 2023. "Applying an improved particle swarm optimization algorithm to ship energy saving," Energy, Elsevier, vol. 263(PE).
    • Handle: RePEc:eee:energy:v:263:y:2023:i:pe:s0360544222029668
    DOI: 10.1016/j.energy.2022.126080
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    References listed on IDEAS

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    1. Molina, S. & Guardiola, C. & Martín, J. & García-Sarmiento, D., 2014. "Development of a control-oriented model to optimise fuel consumption and NOX emissions in a DI Diesel engine," Applied Energy, Elsevier, vol. 119(C), pages 405-416.
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    Citations

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    Cited by:

    1. Elnaz Ghorbani & Tristan Fluechter & Laura Calvet & Majsa Ammouriova & Javier Panadero & Angel A. Juan, 2023. "Optimizing Energy Consumption in Smart Cities’ Mobility: Electric Vehicles, Algorithms, and Collaborative Economy," Energies, MDPI, vol. 16(3), pages 1-19, January.
    2. Xin Peng & Hui Chen & Cong Guan, 2023. "Energy Management Optimization of Fuel Cell Hybrid Ship Based on Particle Swarm Optimization Algorithm," Energies, MDPI, vol. 16(3), pages 1-15, January.
    3. Zhouxi Qin & Dazhi Pan, 2024. "Improved Dual-Center Particle Swarm Optimization Algorithm," Mathematics, MDPI, vol. 12(11), pages 1-15, May.
    4. Zhong Guan & Hui Wang & Zhi Li & Xiaohu Luo & Xi Yang & Jugang Fang & Qiang Zhao, 2024. "Multi-Objective Optimal Scheduling of Microgrids Based on Improved Particle Swarm Algorithm," Energies, MDPI, vol. 17(7), pages 1-20, April.
    5. Karatuğ, Çağlar & Tadros, Mina & Ventura, Manuel & Soares, C. Guedes, 2024. "Decision support system for ship energy efficiency management based on an optimization model," Energy, Elsevier, vol. 292(C).
    6. Awadh Ba Wazir & Ahmed Althobiti & Abdullah A. Alhussainy & Sultan Alghamdi & Mahendiran Vellingiri & Thangam Palaniswamy & Muhyaddin Rawa, 2024. "A Comparative Study of Load Frequency Regulation for Multi-Area Interconnected Grids Using Integral Controller," Sustainability, MDPI, vol. 16(9), pages 1-53, May.

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