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Title:Energy efficient shipping: An application of big data analysis in engine speed optimization of Inland River ships considering multiple environmental factors
Authors:X. Yan, K. Wang, Y. Yuan, X. Jiang, R.R. Negenborn

Journal:Ocean Energy

Abstract:Nowadays, optimization of ship energy efficiency attracts increasing attention under the urgent requirement for energy conservation and emission reduction. Energy efficiency of inland river 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 sailing speeds under different environmental conditions so as to achieve the best energy efficiency along the whole route. Route division according to the distribution characteristics of the environment could provide a good solution for the optimization of ship engine speed under different navigational environment. In this paper, a big data analysis method is adopted to realize the engine speed optimization considering multiple environmental factors, with the advantages of advanced information extraction and high calculating efficiency. Based on the established big data analysis platform in our laboratory, a distributed parallel K-Means clustering algorithm is proposed to achieve elaborate route division by analyzing the corresponding environmental factors. Afterwards, the energy efficiency optimization model considering multiple factors is established through analyzing the energy transition among hull, propeller and main engine. Then, the decisions of the optimal engine speeds in different divisions along the whole route are achieved. Finally, a case study on the Yangtze River is deployed to demonstrate the validity of this optimization method. The results show that the proposed method can effectively reduce energy consumption and CO2 emissions of ships.

Reference:X. Yan, K. Wang, Y. Yuan, X. Jiang, R.R. Negenborn. Energy efficient shipping: An application of big data analysis in engine speed optimization of Inland River ships considering multiple environmental factors. Accepted for publication in Ocean Energy, 2018.
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