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The Development of a Machine Learning-Based Carbon Emission Prediction Method for a Multi-Fuel-Propelled Smart Ship by Using Onboard Measurement Data

Author

Listed:
  • Juhyang Lee

    (Department of Intelligent Mechatronics Engineering, Sejong University, Seoul 05006, Republic of Korea)

  • Jeongon Eom

    (Department of Intelligent Mechatronics Engineering, Sejong University, Seoul 05006, Republic of Korea)

  • Jumi Park

    (Department of Intelligent Mechatronics Engineering, Sejong University, Seoul 05006, Republic of Korea)

  • Jisung Jo

    (Logistics and Maritime Industry Research Department, Korea Maritime Institute, Busan 49111, Republic of Korea)

  • Sewon Kim

    (Department of Intelligent Mechatronics Engineering, Sejong University, Seoul 05006, Republic of Korea)

Abstract

Zero-carbon shipping is the prime goal of the seaborne trade industry at this moment. The utilization of ammonia and liquid hydrogen propulsion in a carbon-free propulsion system is a promising option to achieve net-zero emission in the maritime supply chain. Meanwhile, optimal ship voyage planning is a candidate to reduce carbon emissions immediately without new buildings and retrofits of the alternative fuel-based propulsion system. Due to the voyage options, the precise prediction of fuel consumption and carbon emission via voyage operation profile optimization is a prerequisite for carbon emission reduction. This paper proposes a novel fuel consumption and carbon emission quantity prediction method which is based on the onboard measurement data of a smart ship. The prediction performance of the proposed method was investigated and compared to machine learning and LSTM-model-based fuel consumption and gas emission prediction methods. The results had an accuracy of 81.5% in diesel mode and 91.2% in gas mode. The SHAP (Shapley additive explanations) model, an XAI (Explainable Artificial Intelligence), and a CO 2 consumption model were employed to identify the major factors used in the predictions. The accuracy of the fuel consumption calculated using flow meter data, as opposed to power load data, improved by approximately 21.0%. The operational and flow meter data collected by smart ships significantly contribute to predicting the fuel consumption and carbon emissions of vessels.

Suggested Citation

  • Juhyang Lee & Jeongon Eom & Jumi Park & Jisung Jo & Sewon Kim, 2024. "The Development of a Machine Learning-Based Carbon Emission Prediction Method for a Multi-Fuel-Propelled Smart Ship by Using Onboard Measurement Data," Sustainability, MDPI, vol. 16(6), pages 1-22, March.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:6:p:2381-:d:1356292
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    References listed on IDEAS

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    1. Luan Thanh Le & Gunwoo Lee & Keun-Sik Park & Hwayoung Kim, 2020. "Neural network-based fuel consumption estimation for container ships in Korea," Maritime Policy & Management, Taylor & Francis Journals, vol. 47(5), pages 615-632, July.
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