A Data-Driven Approach to Analyzing Fuel-Switching Behavior and Predictive Modeling of Liquefied Natural Gas and Low Sulfur Fuel Oil Consumption in Dual-Fuel Vessels - Hyunju Kim; Sangbong Lee; Jihwan Lee; Donghyun Kim (Journal of Marine Science and Engineering)
International shipping is responsible for approximately 2.7% of the global greenhouse gas emissions, a share expected to rise by as much as 250% by 2050. In response, the International Maritime Organization (IMO) has set ambitious targets to reduce these emissions to near-zero by 2050, focusing on alternative fuels like LNG. This study examines the energy consumption patterns of dual-fuel engines powered by LNG and develops machine learning models using LightGBM to predict fuel usage for both fuel oil (FO) and gas (GAS) modes. The methodology involved analyzing operational data to identify patterns in fuel usage across different voyage conditions. The FO mode was found to be predominantly used for rapid propulsion during speed changes or directional shifts, while the GAS mode was optimized for stable conditions to maximize fuel efficiency. Additionally, a mixed mode of FO and GAS was occasionally applied on complex routes to balance safety and efficiency. Using these insights, LightGBM models were trained to predict fuel consumption in each mode, achieving high accuracy with R2 scores of 0.94 for the GAS mode and 0.98 for the FO mode. This model enables ship operators to optimize fuel decisions in response to varying voyage conditions, resulting in reduced overall fuel consumption and lower CO2 emissions. By applying the predictive model, operators can adjust fuel usage strategies to match operational demands, potentially achieving notable cost savings and meeting stricter environmental regulations. Furthermore, the accurate estimation of fuel usage supports CO2 emissions management, aligning with the Carbon Intensity Indicator (CII) and providing ship operators with actionable data for fleet management optimization. This research provides essential data to support carbon emission compliance, improves fuel efficiency, and offers practical insights into fuel management strategies. The predictive model serves as a valuable resource for ship operators to optimize fuel use and aligns with the IMO’s environmental targets, aiding the maritime industry’s transition toward carbon neutrality.