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Article

Research on Ship Engine Fuel Consumption Prediction Algorithm Based on Adaptive Optimization Generative Network

1
Maritime College, Tianjin University of Technology, Tianjin 300384, China
2
Low/Zero-Carbon Ship Propulsion Technology Center, Tianjin University of Technology, Tianjin 300384, China
3
Tianjin Aids to Navigation Department of NGCN, Tianjin 300456, China
4
Department of Navigation Technology, Tianjin Maritime College, Tianjin 300350, China
5
Tianjin Deren Dual-Fuel Environmental Protection Technology Co., Ltd., Tianjin 471599, China
*
Author to whom correspondence should be addressed.
J. Mar. Sci. Eng. 2025, 13(6), 1140; https://doi.org/10.3390/jmse13061140 (registering DOI)
Submission received: 13 May 2025 / Revised: 3 June 2025 / Accepted: 5 June 2025 / Published: 8 June 2025
(This article belongs to the Section Ocean Engineering)

Abstract

With the long-term operation of ships, the performance of marine diesel engines gradually declines due to the wear of internal moving components, increasing the risk of potential failures. Fuel consumption is a critical indicator for assessing engine operating conditions, and accurately predicting baseline fuel consumption under normal operating conditions is essential for evaluating ship energy efficiency and conducting fault diagnosis. To address common issues in marine engine operational data, such as noise pollution, missing values, inconsistent scales, and feature redundancy, a Diesel Engine Data Enhancement and Optimization Framework (DEOF) was developed to systematically improve data quality. Furthermore, to overcome the limitations of existing models, such as insufficient prediction accuracy and poor stability under complex operating conditions, a Meta-learning Diffusion Residual Attention Network (MD-RAN) is proposed. This approach leverages the strengths of diffusion models in nonlinear generative modeling, integrates meta-learning mechanisms to enhance task adaptation speed, employs multi-head attention modules to strengthen feature extraction, and incorporates dynamic residual connections to improve training stability and flexibility. The data used in this study were collected from real-world operations of ocean-going vessels, ensuring high representativeness. This paper systematically benchmarks the proposed model with the traditional learning model. The results are verified to be effective. The MD-RAN algorithm is significantly better than the original model in terms of prediction accuracy, stability, and nonlinear expression ability. The R2 value can reach 0.9853, and the RMSE and MAE are as low as 1.5801 and 1.1879, respectively. Its feasibility will be further evaluated in practical applications in the future. This study not only provides a systematic data-driven modeling framework, offering technical insights for constructing high-quality datasets, but also establishes a novel generative modeling approach for marine diesel engine fuel consumption prediction, providing robust support for intelligent engine maintenance and energy efficiency optimization.
Keywords: fuel consumption baseline model; diesel engine condition assessment; probabilistic generative model; advanced optimization strategies fuel consumption baseline model; diesel engine condition assessment; probabilistic generative model; advanced optimization strategies

Share and Cite

MDPI and ACS Style

Zhang, D.; Song, Y.; Gao, J.; Shen, Z.; Li, L.; Yao, A. Research on Ship Engine Fuel Consumption Prediction Algorithm Based on Adaptive Optimization Generative Network. J. Mar. Sci. Eng. 2025, 13, 1140. https://doi.org/10.3390/jmse13061140

AMA Style

Zhang D, Song Y, Gao J, Shen Z, Li L, Yao A. Research on Ship Engine Fuel Consumption Prediction Algorithm Based on Adaptive Optimization Generative Network. Journal of Marine Science and Engineering. 2025; 13(6):1140. https://doi.org/10.3390/jmse13061140

Chicago/Turabian Style

Zhang, Defu, Yuxuan Song, Jianfeng Gao, Zhenyu Shen, Liangkuan Li, and Anren Yao. 2025. "Research on Ship Engine Fuel Consumption Prediction Algorithm Based on Adaptive Optimization Generative Network" Journal of Marine Science and Engineering 13, no. 6: 1140. https://doi.org/10.3390/jmse13061140

APA Style

Zhang, D., Song, Y., Gao, J., Shen, Z., Li, L., & Yao, A. (2025). Research on Ship Engine Fuel Consumption Prediction Algorithm Based on Adaptive Optimization Generative Network. Journal of Marine Science and Engineering, 13(6), 1140. https://doi.org/10.3390/jmse13061140

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