Greenhouse gas emission and subsequent global warming have attracted more and more attention all over the world. As one of the biggest emission sources, shipping industry is suffering great emission reduction pressure from the public. Although shipping industry can improve the energy efficiency by exploring new strategies to reduce the fuel cost and the emission to air, some deficiencies may be discovered in practice due to lack of a comprehensive consideration of other significant factors, such as safety factors. Therefore, energy efficiency management strategies that consider both safety and economy factors have more practical significance for enhancing the ship energy efficiency. In this article, a power supply model considering economy, emission reduction and safety was established, and a multi-objective optimization problem to keep the system working at a high safety level, a low CO2 emission and a moderate fuel consumption was proposed. Furthermore, the particle swarm optimization algorithm was adopted to solve the multi-objective optimization problem, and the obtained non-inferior solutions could help officers on duty to select the optimum main engine speed by weighing the economy and the safety of the ship. The results show that the proposed method of energy efficiency management could reduce the greenhouse gas emission and the energy efficiency operation index (EEOI) of the ship effectively. The trade-off between ship economy, emission and robustness of the power supply system was studied, which is meaningful to the energy efficiency improvement and the CO2 emission reduction under the safety requirement of power supply.
Optimizing ship energy efficiency: Application of particle swarm optimization algorithm
2018-11-01
13 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Optimizing Robot Path Planning with the Particle Swarm Optimization Algorithm
BASE | 2023
|Optimizing Laying Hen Diet using Multi-Swarm Particle Swarm Optimization
BASE | 2018
|Train Timetable Optimizing and Rescheduling Based on Improved Particle Swarm Algorithm
Online Contents | 2010
|