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

Xinping Yan, Kai Wang, Yupeng Yuan, Xiaoli Jiang, Rudy R. Negenborn

Research output: Contribution to journalArticleScientificpeer-review

18 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)457-468
JournalOcean Engineering
Volume169
DOIs
Publication statusPublished - 2018

Keywords

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

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