Energy-efficient shipping

Energy-efficient shipping: An application of big data analysis for optimizing engine speed of inland ships considering multiple environmental factors

Yan, Xinping (Wuhan University of Technology; MOST)
Wang, K. (TU Delft Transport Engineering and Logistics; Wuhan University of Technology)
Yuan, Yupeng (Wuhan University of Technology; MOST; University of Cambridge)
Jiang, X. (TU Delft Transport Engineering and Logistics)
Negenborn, R.R. (TU Delft Transport Engineering and Logistics)

2018

Energy efficiency of inland ships is significantly influenced by navigational environment, including wind speed and direction as well as water depth and speed. The complexity of the inland navigational environment makes it rather difficult to determine the optimal speeds under different environmental conditions to achieve the best energy efficiency. Route division according to the characteristics of these environmental factors could provide a good solution for the optimization of ship engine speed under different navigational environments. In this paper, the distributed parallel k-means clustering algorithm is adopted to achieve an elaborate route division by analyzing the corresponding environmental factors based on a self-developed big data analytics platform. Subsequently, a ship energy efficiency optimization model considering multiple environmental factors is established through analyzing the energy transfer among hull, propeller and main engine. Then, decisions are made concerning the optimal engine speeds in different segments along the path. Finally, a case study on the Yangtze River is performed to validate the present optimization method. The results show that the proposed method can effectively reduce energy consumption and CO₂ emissions of ships.

Big data analysis
Hadoop
Parallel k-means algorithm
Ship energy efficiency
Speed optimization

http://resolver.tudelft.nl/uuid:d0bbeda4-e234-4188-9d41-49846d8f481e

https://doi.org/10.1016/j.oceaneng.2018.08.050

2019-04-01

0029-8018

Ocean Engineering, 169, 457-468

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Institutional Repository

journal article

© 2018 Xinping Yan, K. Wang, Yupeng Yuan, X. Jiang, R.R. Negenborn