Real-time optimization of ship energy efficiency based on the prediction technology of working condition
Real-time optimization of ship energy efficiency based on the prediction technology of working condition
复制标题
基于工况预测技术的船舶能效实时优化
DOI：
10.1016/j.trd.2016.03.014
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发表时间：
2016-07
期刊：
影响因子：
--
通讯作者：
Feng Li
中科院分区：
文献类型：
--
作者：
Kai Wang;Xinping Yan;Yupeng Yuan;Feng Li
船舶能效管理与控制是提高海洋经济效益、减少CO2排放的有效策略。不同工况下最佳航速的确定是实时提高船舶能效的基础和前提。采用小波神经网络方法预测船舶前方近距离内与航行环境因素相关的工况，通过建立的船舶能效实时优化模型，确定不同工况下最优能效的发动机最佳转速。进一步地，通过将船舶发动机预设在该最优转速，可以保证船舶在提前到达航行环境时，船舶能效处于最优状态，从而实现不同航行环境因素下船舶能效的实时优化。试验研究表明，该优化模型具有节能减排的效果，可为在役船舶的优化航行提供理论指导。与传统的设定航速航行方法相比，本文提出的方法对提高船舶能效具有更大的实际意义。
Ship energy efficiency management and control is an effective strategy to improve the marine economy and reduce CO2emission. The determination of the best navigation speed under different working conditions is the basis and premise for real-time improvement of ship energy efficiency. In this paper, the working condition in short distance ahead of the ship related to navigation environment factors was predicted by the method of wavelet neural network, and then the best engine speed for the optimal energy efficiency under different working conditions could be determined through the established ship energy efficiency real-time optimization model. Further, by presetting the ship engine at this optimal speed, the ship energy efficiency could be guaranteed at the optimal state when the ship arrived at the navigation environment ahead of the ship, thus achieving real-time optimization of ship energy efficiency under different navigation environment factors. Experimental studies showed that the proposed optimization model was effective in energy saving and emission reduction, which could provide theoretical guidance for optimal sailing of the ship in service. Compared to traditional setting speed navigation methods, our proposed method has more practical significance to the improvement of ship energy efficiency.
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影响因子：
1.4
作者：
U. Okkan
通讯作者：
U. Okkan
DOI：
10.1016/j.trd.2013.03.002
发表时间：
2013-07
期刊：
Transportation Research Part D: Transport and Environment
影响因子：
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作者：
Sun Xing;Yan Xinping;Wu Bing;Song Xin
通讯作者：
Song Xin
影响因子：
5
作者：
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通讯作者：
E. Attah;R. Bucknall
影响因子：
9
作者：
I. Ančić;A. Šestan
通讯作者：
I. Ančić;A. Šestan
DOI：
--
发表时间：
2015
期刊：
Int. J. Rough Sets Data Anal.
影响因子：
--
作者：
A. Fan;Xin-ping Yan;Q. Yin;Xing Sun
通讯作者：
A. Fan;Xin-ping Yan;Q. Yin;Xing Sun