With the implementation of the ISO 8217-2024 marine fuel standard, the use of high-concentration biofuels in ships has become viable. However, relatively few studies have been conducted on the effects of biofuels on cylinder lubrication performance in low-speed, two-stroke marine diesel engines. In this study, catering waste oil was blended with 180 cSt low-sulfur fuel oil (LSFO) to prepare biofuels with volume fractions of 24% (B24) and 50% (B50). These biofuels were evaluated in a MAN marine diesel engine under load conditions of 25%, 50%, 75%, and 90%. The experimental results showed that, at the same engine load, the use of B50 biofuel led to lower kinematic viscosity and oxidation degree of the cylinder residual oil, but higher total base number (TBN), nitration level, PQ index, and concentrations of wear elements (Fe, Cu, Cr, Mo). These results indicate that the wear of the cylinder liner–piston ring interface was more severe when using B50 biofuel than when using B24 biofuel. For the same type of fuel, as the engine load increased, the kinematic viscosity and TBN of the residual oil decreased, while the PQ index and the concentrations of Fe, Cu, Cr, and Mo increased, reflecting the aggravated wear severity. Ferrographic analysis further revealed that ferromagnetic wear particles in the oil mainly consisted of normal wear debris. When using B50 biodiesel, a small amount of fatigue wear particles were detected. These findings offer crucial insights for optimizing biofuel utilization and improving cylinder lubrication systems in marine engines.

With the implementation of the ISO 8217-2024 marine fuel standard, the use of high-concentration biofuels in ships has become viable. However, relatively few studies have been conducted on the effects of biofuels on the cylinder lubrication performance of low-speed, two-stroke marine diesel engines. In this study, catering waste oil was blended with 180 cSt low-sulfur fuel oil (LSFO) to prepare biofuels with volume fractions of 24% (B24) and 50% (B50). These biofuels were evaluated in a MAN marine diesel engine under load conditions of 25%, 50%, 75%, and 90%. The experimental results showed that, at the same engine load, the use of B50 biofuel led to lower kinematic viscosity and oxidation degree of the cylinder residual oil, but higher total base number (TBN), nitration level, PQ index, and concentrations of wear elements (Fe, Cu, Cr, Mo). These results indicate that the wear of the cylinder liner-piston ring interface was more severe when using B50 biofuel compared with B24 biofuel. For the same type of fuel, as the engine load increased, the kinematic viscosity and TBN of the residual oil decreased, while the PQ index and concentrations of Fe, Cu, Cr, and Mo increased, reflecting the aggravated wear severity. Ferrographic analysis further revealed that ferromagnetic wear particles in the oil mainly consisted of normal wear debris. When using B50 biodiesel, a small amount of fatigue wear particles were detected. These findings offer crucial insights for optimizing biofuel utilization and improving cylinder lubrication systems in marine engines.

Under the context of “Energy Conservation and Emission Reduction,” low-sulfur fuel has become widely adopted in maritime operations, posing significant challenges to cylinder lubrication systems. Traditional oil injection strategies, heavily reliant on manual experience, suffer from instability and high costs. To address this, a lightweight image retrieval model for cylinder lubrication is proposed, leveraging deep learning and computer vision to support oiling decisions based on visual features. The model comprises three components: a backbone network, a feature enhancement module, and a similarity retrieval module. Specifically, EfficientNetB0 serves as the backbone for efficient feature extraction under low computational overhead. MobileViT Blocks are integrated to combine local feature perception of Convolutional Neural Networks (CNNs) with the global modeling capacity of Transformers. To further improve receptive field and multi-scale representation, Receptive Field Blocks (RFB) are introduced between the components. Additionally, the Convolutional Block Attention Module (CBAM) attention mechanism enhances focus on salient regions, improving feature discrimination. A high-quality image dataset was constructed using WINNING’s large bulk carriers under various sea conditions. The experimental results demonstrate that the EfficientNetB0 + RFB + MobileViT + CBAM model achieves excellent performance with minimal computational cost: 99.71% Precision, 99.69% Recall, and 99.70% F1-score—improvements of 11.81%, 15.36%, and 13.62%, respectively, over the baseline EfficientNetB0. With only a 0.3 GFLOP and 8.3 MB increase in model size, the approach balances accuracy and inference efficiency. The model also demonstrates good robustness and application stability in real-world ship testing, with potential for further adoption in the field of intelligent ship maintenance.

To solve the problems of low accuracy and the significant impact of human factors when manually adjusting the oil feed rate (FR) and base number (BN) of the cylinder oil in a marine slow-speed diesel engine, a new method for optimising cylinder lubrication adjustments based on image deep learning is proposed. This method involves inspecting the wear and carbon deposition on the inner surface of the cylinder liner and the piston ring surface through the scavenging port. Images captured during an inspection are normalised, clustered and segmented, and features are extracted to create a comprehensive cylinder lubrication image database. Then, based on the HU invariant moment and HSV colour space, the images are fused and similarity matched, and image search software is developed. This approach addresses the randomness and significant errors that are often associated with the manual adjustment of the cylinder oil FR. The software is implemented to manage the cylinder lubrication on a very large crude carrier. The results demonstrate that the proposed optimisation method can achieve optimal control targets: the residual oil iron content is found to be below 25 ppm, the residual oil BN is above 30 mg KOH/g, and the cylinder liner wear rate is below 0.1 mm/1,000 h, thereby reducing the risk of excessive wear on the cylinder liner.

Green and energy-efficient maritime transport has become a strategic imperative under tightening decarbonization mandates by the International Maritime Organization (IMO). However, current ship energy efficiency optimization (EEO) frameworks often decouple fuel consumption prediction from operational decision-making, limiting real-time adaptability and integrated control. To address this gap, this study proposes a high-resolution collaborative framework that couples a Transformer-LSTM-A prediction model with a voyage-segmented NSGA-III multi-objective optimizer. An NSGA-III solves the four-objective, segment-level speed-and-trim optimization subject to practical stability and operating limits. The proposed architecture incorporates temporal attention mechanisms and VMD-enhanced multivariate inputs to accurately forecast fuel consumption rates, which then guide the segment-wise optimization of ship speed and trim. The optimization simultaneously minimizes fuel consumption, CO ₂ emissions, and the Energy Efficiency Operational Indicator (EEOI), while embedding soft constraints on voyage distance to preserve navigational feasibility. A real-world case study demonstrates the effectiveness of the proposed approach, achieving reductions of 4.76% in FCR, 3.04% in CO ₂ emissions, and 1.50% in EEOI. These results validate the framework’s potential for intelligent maritime energy management, offering a robust and scalable pathway toward low-carbon ship operations aligned with global regulatory targets.