基于航速与实时能效综合目标的船舶主机转速优化

孙超; 张均东; 刘超

doi:10.16411/j.cnki.issn1006-7736.2020.03.007

大连海事大学学报 >

2020 , Vol. 46 >Issue 3: 60 - 67

DOI: https://doi.org/10.16411/j.cnki.issn1006-7736.2020.03.007

船舶与海洋工程

基于航速与实时能效综合目标的船舶主机转速优化

孙超 ,
张均东 ,
刘超

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(1.大连海事大学轮机工程学院，辽宁大连 116026；2.中国船舶工业系统工程研究院，北京 100094)

孙超（1979 — ），男，博士生，E-mail：aba11@163.com.

收稿日期: 2020-06-18

修回日期: 2020-08-22

网络出版日期: 2020-08-22

基金资助

高技术船舶科研项目-船用低速机研制项目（MC-201501-D01-07）.

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optimization of marine main engine based on comprehensive objective of speed and real-time energy efficiency

SUN Chao ,
ZHANG Jun-dong ,
LIU Chao

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(1. Marine Engineering College, Dalian Maritime University, Dalian 116026, China;2. Systems Engineering Research Institute, Beijing 100094, China)

Received date: 2020-06-18

Revised date: 2020-08-22

Online published: 2020-08-22

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摘要

在实际航行环境中，通过对船舶主机转速进行实时优化，可以提高船舶的节能减排效果.为达到这一目的，导出实时能效运营指数作为监测指标，提出综合航速偏差和实时能效运营指数偏差的综合目标函数.以“育明轮”为例，采用反向传播神经网络预测主机油耗率和船舶航速，使用遗传算法对航行中船舶的主机转速进行了优化研究.计算结果表明：实时能效营运指数对航行环境变化更敏感，适合船舶实时能效监测；基于反向传播神经网络的航速和主机油耗率预测模型误差不超过2%；采用综合目标函数，更便于达到在一定航速限制要求下的节能减排目标；综合目标函数中航速权重越小，降低EEOI的效果越明显；使用遗传算法约在10代左右就可以得到优化转速，效果良好.该方法可在给定航速限制要求和航行环境条件下提高船舶主机的节能减排效果.

关键词： 船舶主机; 航速优化; 能效营运指数(EEOI); 神经网络; 遗传算法

本文引用格式

孙超 , 张均东 , 刘超 . 基于航速与实时能效综合目标的船舶主机转速优化[J]. 大连海事大学学报, 2020 , 46(3) : 60 -67 . DOI: 10.16411/j.cnki.issn1006-7736.2020.03.007

Abstract

In the actual navigation environment, through the real-time optimization of the ship’s main engine speed, the ship’s energy-saving and carbon emission reduction effect can be improved. Therefore, the real-time energy efficiency operation index was derived as the monitoring index, and the comprehensive objective function of comprehensive speed deviation and realtime energy efficiency operation index(EEOI) deviation was proposed. Taking “Yuming” as an example, BP neural network was used to predict the main engine fuel consumption rate and ship speed, and genetic algorithm was used to optimize the main engine speed. Simulation results show that the real-time EEOI is more sensitive to the variation of navigation environment and suitable for real-time energy efficiency monitoring; the error of prediction model of ship speed and main engine fuel consumption rate base on BPNN is less than 2%; it’s more convenient to achieve the objective of fuel saving and carbon emission reduction by adopting the comprehensive objective function; the smaller the weight of ship speed is, the more obvious the reduction of EEOI is; the optimized revolution can be obtained in around 10 epochs by genetic algorithm. The proposed method can improve the energy saving and emission reduction effect of the main engine under the given speed limit requirements and navigation environmental conditions.

Key words： marine main engine; ship speed optimization; energy efficiency operation index (EEOI); neural network; genetic algorithm

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