The demands for lower Energy Efficiency Operational Index (EEOI) reflect the requirements of international conventions for green shipping. Within this context it is believed that practical solutions for the dynamic optimization of a ship’s main engine and the reduction of EEOI in real conditions are useful in terms of improving sustainable shipping operations. In this paper, we introduce a model for dynamic optimization of the main engine that can improve fuel efficiency and decrease EEOI. The model considers as input environmental factors that influence overall ship dynamics (e.g., wind speed, wind direction, wave height, water flow speed) and engine revolutions. Fuel consumption rate and ship speed are taken as outputs. Consequently, a genetic algorithm is applied to optimize the initial connection weight and threshold of nodes of a neural network (NN) that is used to predict fuel consumption rate and ship speed. Navigation data from the training ship “YUMING” are applied to train the network. The genetic algorithm is used to optimize engine revolution and obtain the lowest EEOI. Results show that the optimization method proposed may assist with the prediction of lower EEOI in different environmental conditions and operational speed.

The paper presents a performance prediction model of marine low-speed two-stroke diesel engines based on an advanced MCC (mixture controlled combustion) model coupled with a fuel injection model. Considering the time of real calculation, the so-called “concentrated exhausting gas” scavenging model and the working process model are used in the present work, and improved by introducing the ratio of pure combustion product over the total gas mass in the cylinder as an expression of the working medium components. The reaction rate model in the zero-dimensional MCC model is improved by introducing the fraction of combustion product in the fuel spray, and the relationship between the combustion model and scavenging quality is established. Meanwhile, the combustion model was simplified in the diffusion combustion phases and integrated with the fuel injection model in order to respond to the change of injection profile and injection timing. A large-scale low-speed marine diesel engine was used for a simulation. The results of the whole model are consistent with experimental data and the speed of calculation is fast enough for real time simulation of low speed and medium speed diesel engines. The prediction model can be used in the design and calibration of the electronic control system and performance optimization of the marine two-stroke diesel engine.

Accurately detecting state events is critical for hybrid dynamic systems. However, it is difficult to locate the exact point of a state event in systems with singularities or systems in which event functions are of multiple roots. Overcoming these difficult situations depends on accurately predicting event functions and a reliable root-finding method. A carefully constructed extrapolation polynomial, which is derived from a variable-step multi-step integration method, is applied to predict values of event functions. Due to not using state values of the next time point, the polynomial can be employed to determine the integration step size by checking potential events in all situations. The extended interval Newton’s method is employed to find all roots in a certain time interval, which is determined by the existing integration method. The procedure of the root non-existence test and root finding are combined into one. Simulation results show that the algorithm is effective under several different critical situations. By the algorithm, model singularities are found on time and simulation failures are avoided. The event detecting algorithm can be combined with the existing integration method.

A marine variable speed generator can dynamically adjust the set speed of a diesel engine according to the actual power load level by the grading method, so the goal of energy conservation is achieved. However, the grading method relies on engineering experience. The actual range of power load fluctuation is uncertain, which leads to a decrease in fuel efficiency. Therefore, a method based on load sliding interval is put forward. In this method, a fixed width load interval is set at first, and then the interval slides on the diesel engine load at a step of 1%. The minimum fuel consumption rate speed for the interval is optimized during the sliding process. Finally, a minimum fuel consumption rate speed curve corresponding to the sliding interval position is obtained. The optimization objective is to have the minimum weighted average fuel consumption rate at the integer load points within each interval. The actual power load is monitored and used to determine the current load interval during the calculation of the set speed. The minimum fuel consumption rate speed curve optimized was applied to query the setting value of the diesel speed. The mean value engine model was applied for simulation under three load conditions. The results show that the sliding interval method could achieve the best fuel saving effect under any power load; the set value of the diesel engine speed changed smoothly under the fluctuating power load conditions, and the fuel saving ability was better according to the actual power load conditions on the ship.

To enable proactive prediction of marine diesel engine failure time and root causes, thereby reserving sufficient time for maintenance, this study proposes a data-driven multi-algorithm integration framework for performance assessment and fault early warning in marine diesel engines. By integrating the SSD (steady-state detection) algorithm, a data-driven CLIQUE clustering algorithm was chosen for automatic multi-parameter high-dimensional running condition partitioning. This innovative approach overcomes the limitations of traditional single-parameter approaches or dimensionality reduction techniques, significantly enhancing state classification accuracy. The improved classification results subsequently increase the reliability of Mahalanobis distance as a performance indicator for marine diesel engine condition assessment. Finally, the cumulative anomaly method combined with the Yamamoto test was employed for anomaly detection analysis, enabling precise identification of fault occurrence time and establishing an effective early-warning mechanism. The study demonstrates that this technique effectively characterizes the overall performance of marine diesel engines and captures their performance degradation features. Implemented on a 6RT-flex82T marine diesel engine dataset, the method achieved precise prediction of fault occurrence time with early warnings, providing approximately 20 days advance notice for maintenance planning. Furthermore, comparative analyses with existing studies revealed its superior capability in pinpointing the anomaly to the jacket cooling water outlet temperature of cylinder #2. These results confirm the method’s effectiveness in both performance assessment and fault early warning for marine diesel engines, offering a novel approach for intelligent maintenance of shipboard equipment.

CO₂ emissions during ship building, maintenance, and scrapping are not be involved when defining Energy Efficiency Operational Indicator (EEOI). However, these CO₂ emissions can only show its value during operation phase of ship life. In order to evaluate the effect of CO₂ emissions during ship building, maintenance, and scrapping on energy efficiency, a real time EEOI definition was put forward, a life cycle assessment (LCA) calculation framework was established, and a ship propulsion model with main diesel engine was built. A bulker carrier YUMING was taken as the case ship. CO₂ emissions during ship building, maintenance, and scrapping were calculated. Main engine revolution and real time EEOI value with other parameters were obtained by simulating in different ship draft. The results show that life cycle assessment result will explicitly increase the real time EEOI value especially in lower engine speed and lower ship draft. The engine revolution at the lowest EEOI value will increase explicitly by life cycle assessment result. It means that the lower limit of ship speed should increase during slow steaming. Therefore, slow steaming may bring less environmental benefit if the life cycle assessment result is involved.