Dynamic Prediction and Optimization of Energy Efficiency Operational Index (EEOI) for an Operating Ship in Varying Environments

Journal of Marine Science and Engineering

Chao Sun¹  Haiyan Wang²  Ye Zhao³  Chao Liu⁴ 

[1] Marine Engineering College, Dalian Maritime University, Dalian 116026, China;Merchant Marine College, Shanghai Maritime University, Shanghai 200136, China;Shanghai Rail Transportation Equipment Co., Ltd., Shanghai 200245, China;Systems Engineering Research Institute, Beijing 100036, China;

关键词: merchant shipping;    eeoi;    genetic algorithms;    neural networks;    ship dynamics;   

DOI  :  10.3390/jmse7110402

来源: DOAJ

【 摘 要 】

The demands for lower Energy Efficiency Operational Index (EEOI) reflect the requirements of international conventions for green shipping. Within this context it is believed that practical solutions for the dynamic optimization of a ship’s main engine and the reduction of EEOI in real conditions are useful in terms of improving sustainable shipping operations. In this paper, we introduce a model for dynamic optimization of the main engine that can improve fuel efficiency and decrease EEOI. The model considers as input environmental factors that influence overall ship dynamics (e.g., wind speed, wind direction, wave height, water flow speed) and engine revolutions. Fuel consumption rate and ship speed are taken as outputs. Consequently, a genetic algorithm is applied to optimize the initial connection weight and threshold of nodes of a neural network (NN) that is used to predict fuel consumption rate and ship speed. Navigation data from the training ship “YUMING” are applied to train the network. The genetic algorithm is used to optimize engine revolution and obtain the lowest EEOI. Results show that the optimization method proposed may assist with the prediction of lower EEOI in different environmental conditions and operational speed.

【 授权许可】

Unknown