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Ship Emission Mitigation Strategies Choice Under Uncertainty

Author

Listed:
  • Jun Yuan

    (China Institute of Free Trade Zone Supply Chain, Shanghai Maritime University, Shanghai 201306, China)

  • Haowei Wang

    (Department of Industrial Systems Engineering and Management, National University of Singapore, Singapore 117576, Singapore)

  • Szu Hui Ng

    (Department of Industrial Systems Engineering and Management, National University of Singapore, Singapore 117576, Singapore)

  • Victor Nian

    (Energy Studies Institute, National University of Singapore, Singapore 119620, Singapore)

Abstract

Various mitigation strategies have been proposed to reduce the CO 2 emissions from ships, which have become a major contributor to global emissions. The fuel consumption under different mitigation strategies can be evaluated based on two data sources, real data from the real ship systems and simulated data from the simulation models. In practice, the uncertainties in the obtained data may have non-negligible impacts on the evaluation of mitigation strategies. In this paper, a Gaussian process metamodel-based approach is proposed to evaluate the ship fuel consumption under different mitigation strategies. The proposed method not only can incorporate different data sources but also consider the uncertainties in the data to obtain a more reliable evaluation. A cost-effectiveness analysis based on the fuel consumption prediction is then applied to rank the mitigation strategies under uncertainty. The accuracy and efficiency of the proposed method is illustrated in a chemical tanker case study, and the results indicate that it is critical to consider the uncertainty, as they can lead to suboptimal decisions when ignored. Here, trim optimisation is ranked more effective than draft optimisation when the uncertainty is ignored, but the reverse is the case when the uncertainty in the estimations are fully accounted for.

Suggested Citation

  • Jun Yuan & Haowei Wang & Szu Hui Ng & Victor Nian, 2020. "Ship Emission Mitigation Strategies Choice Under Uncertainty," Energies, MDPI, vol. 13(9), pages 1-20, May.
  • Handle: RePEc:gam:jeners:v:13:y:2020:i:9:p:2213-:d:353532
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    References listed on IDEAS

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    Cited by:

    1. Shang, Gang & Xu, Liyun & Tian, Jinzhu & Cai, Dongwei & Xu, Zhun & Zhou, Zhuo, 2023. "A real-time green construction optimization strategy for engineering vessels considering fuel consumption and productivity: A case study on a cutter suction dredger," Energy, Elsevier, vol. 274(C).
    2. Jun Yuan & Jiang Zhu & Victor Nian, 2020. "Neural Network Modeling Based on the Bayesian Method for Evaluating Shipping Mitigation Measures," Sustainability, MDPI, vol. 12(24), pages 1-14, December.

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