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

Juhyang Lee, Jeongon Eom, Jumi Park, Jisung Jo and Sewon Kim ()
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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

Sustainability, 2024, vol. 16, issue 6, 1-22

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.

Keywords: dual-fuel engines; fuel consumption prediction; smart ship; carbon emission calculation; machine learning; LSTM; SHAP (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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