Multilevel parallel integration framework for enhancing energy efficiency of wing-assisted ships based on deep learning and intelligent algorithms: Towards a smarter and greener shipping

Enhancing the energy efficiency of ships can directly reduce energy consumption and CO2 emissions in the shipping industry. This paper proposes a multilevel parallel integration (MPI) framework aimed at improving energy efficiency for the increasing number of wing-assisted vessels with significant decarbonization potential. This framework enables comprehensive consideration of both technological and operational measures, thereby maximizing the implementation of energy-saving and emission-reduction effects. Firstly, we develop the artificial ecosystem-based optimization (AEO)-Autoint deep learning model for fuel consumption prediction, which is capable of effectively capturing the interactions among features. Secondly, an adaptive segmentation method based on the augmented AEO (AAEO) algorithm is utilized to derive optimal division results that balance information mining with executability. Finally, the enhanced AEO (EAEO) algorithm is employed to optimize the sailing speed, optimal trim, and wings rotation angle during the voyage, thereby assisting navigation decision-making directly. Case results indicate that applying the proposed MPI framework can save 125.68 t of fuel and reduce carbon emissions by 391.36 t, leading to an 8.74 % improvement in the energy efficiency level of the vessels. This study provides a solid theoretical foundation and valuable reference for the practical management and energy efficiency enhancement of wing-assisted vessels, holding significant implications for further promoting intelligent and low-carbon shipping.