Abstract
As interest in eco-friendly ships increases, methods for status monitoring and forecasting using in-service data from ships are being developed. Models for predicting the energy efficiency of a ship in real time need to effectively process the operational data and be optimized for such an application. This paper presents models that can predict fuel consumption using in-service data collected from a 13,000 TEU class container ship, along with statistical and domain-knowledge methods to select the proper input variables for the models. These methods prevent overfitting and multicollinearity while providing practical applicability. To implement the prediction model, either an artificial neural network (ANN) or multiple linear regression (MLR) were applied, where the ANN-based models showed the best prediction accuracy for both variable selection methods. The goodness of fit of the models based on ANN ranged from 0.9709 to 0.9936. Furthermore, sensitivity analysis of the draught under normal operating conditions indicated an optimal draught of 14.79 m, which was very close to the design draught of the target ship, and provides the optimal fuel consumption efficiency. These models could provide valuable information for ship operators to support decision making to maintain efficient operating conditions.
Highlights
The environmental pollution resulting from increased consumption of fossil fuels has become a target of the international organizations attempting to regulate greenhouse gas emissions
We developed two models for predicting the fuel consumption from in-service data collected from a 13,000 TEU class container ship
The development of the fuel consumption prediction models with in-service data collected from a 13,000 TEU class container ship provided the following insights:
Summary
The environmental pollution resulting from increased consumption of fossil fuels has become a target of the international organizations attempting to regulate greenhouse gas emissions. In 2018, members of the International Maritime Organization agreed to an initial strategy to reduce ship emissions to half of the 2008 level by 2050 [1]; regulations such as the Energy Efficiency Design Index, Energy Efficiency Operational Indicator, and Ship. Energy Efficiency Management Plan are being applied to reduce emissions from ships [2,3]. Shipping companies are developing associated procedures [5], and management plans to maintain international competitiveness and reduce emissions by reducing fuel consumption, which accounts for nearly 50–60% of the total operating expenses [6,7,8]. During operation of the ship, marine organisms attach to the hull, which increase the weight and frictional resistance of the hull, resulting in a reduction in the propulsion efficiency [10,11]
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