The Ship’s Digital Twin (SDT) is a digital record of a ship’s behaviour or a software clone, which can be used to simulate scenarios that are expensive or hardly feasible to perform on a real object and especially in real time. The purpose of the SDT is to achieve cost reduction, obtain timely warnings of irregularities, and optimise individual ship system performances or the operation of the whole ship and to assist ship management. The aim of this paper is to describe the concept of the SDT and clarify some perplexities that may occur from initial introduction to concept. To that end, the paper identifies the steps in the SDT formulation process and methods used in each step of the process. Furthermore, a four-step iterative procedure for the SDT development is proposed. The applications of the concept are numerous, and some of them are presented in a review analysis in this paper. The presented analysis leads to a conclusion that should give some direction to future research in this area.

Artificial neural networks (ANN) are a data driven tool that has been used for modeling, prediction, optimization, classification, diagnostics, decision-making, etc., in various systems where measurements are available to produce significant amount of data. Ship processes are constantly monitored in order to control the operation of the ship and to ensure efficient and safe environment, generating large amount of data. Those data are increasingly being exploited by ANNs and the number of applications is growing. The aim of this paper is to analyze the applications of ANNs in maritime industry, and especially on ships. Based on the review analysis of the sixty-nine papers found published on this topic over the last 10 years in relevant databases, applications have been classified into eight categories in this paper. ANN types, training algorithms, activation functions, as well as measures used to evaluate the performance of the ANN models, have been analyzed for each application category. ANNs rely on data, therefore data acquisition, data processing, organization of the data for training ANN models, their validation and testing have also been addressed in this paper. The conclusions from the review analysis presented should be useful for future work in the area of ANN applications on ships and in maritime industry.

To keep pace with global energy efficiency trends and, in particular, emission reduction targets in the maritime sector, both onshore and maritime power distribution systems need to be adapted to the relevant new technologies and concepts. As an important link in the distribution chain, medium-voltage switchgear (MV) is expected to be stable and reliable while operating as efficiently as possible. Failures of MV equipment, while rare because the equipment must be safe to handle and use, have far-reaching consequences. The consequences of such failures, whether to the shore or marine power system, present risks to the entire power plant, so an accurate assessment of equipment condition is required to identify potential failures early. The solution is an emerging concept of digital switchgear, where the implementation of sensor technology and communication protocols enables effective condition monitoring, and the creation of a database that, when combined with machine learning algorithms, enables predictive maintenance and/or fault detection. This paper presents, step by step, the previous challenges, the current research (divided into predictive maintenance, condition monitoring, and fault detection categories), and the future directions in this field. The use of artificial intelligence is discussed to eliminate the disadvantage of manually interpreting operational data, and recommendations for future work are formulated, such as the need to standardize test procedures and data sets to train and compare different algorithms before they are used in practice.

The increasing emphasis on environmental sustainability and stricter gas emissions regulations has made the optimization of fuel and emissions a crucial factor for marine propulsion systems. This paper investigates the potential to improve fuel efficiency and reduce emissions of LNG ship propulsion systems by using different load sharing strategies in Dual-Fuel Diesel-Electric (DFDE) propulsion systems. Using data collected from on-board cyclic measurements and an optimization model, the effects of different load sharing strategies for various types of fuel, such as HFO, MDO, and LNG, under different engine load conditions were investigated. The results of these strategies are compared with those of on-board power management systems (PMS), which evenly allocate power among the engines, irrespective of fuel usage and emission levels. The results show that load adjustments according to the optimization model can considerably increase fuel economy and contribute to the reduction of CO₂ and NO_x compared to standard practice at the equal load in different ship operating modes. Our approach introduces an innovative optimization concept that has been proven to improve fuel efficiency and reduce emissions beyond standard practices. This paper demonstrates the robustness of the model in balancing environmental and operational objectives and presents an effective approach for more sustainable and efficient ship operations. The results are in line with global sustainability efforts and provide valuable insights for future innovations in energy optimization and ship emission control.

Due to growing environmental concerns and stringent emissions regulations, optimizing the fuel consumption of marine propulsion systems is crucial. This work deals with the potential in an LNG ship propulsion system to reduce fuel consumption through controlled load distribution between engines in Dual-Fuel Diesel Electric (DFDE) plant. Based on cyclical data acquisition measured onboard and using an optimization model, this study evaluates different load distribution strategies between setups according to the optimization model results and automatic (equal) operation to determine their effectiveness in improving fuel efficiency. The analysis includes scenarios with different fuel types, including LNG, MDO and HFO, at different engine loads. The results indicate that load distribution adjustment based on the optimization model results significantly improves fuel efficiency compared to conventional methods of uniform load distribution controlled by power management systems in almost all load intervals. This research contributes to the maritime industry by demonstrating that strategic load management can achieve significant fuel savings and reduce environmental impact, which is in line with global sustainability goals. This work not only provides a framework for the implementation of more efficient energy management systems on LNG vessels, but also sets a benchmark for future innovations in maritime energy optimization as well as in the view of exhaust emission reduction.

Accurate controller tuning is important for ensuring optimal performance in flow control processes, particularly onboard ships, where precise control of fluid systems such as oil, gas, and water flows determines the operation of the whole ship. Models can be used to avoid experimentation on a real system, which is essential if systems are unavailable for controller tuning experiments. This paper presents the results of controller tuning using the ANN model. To evaluate the effectiveness of the proposed method, it is compared to the transfer function model-based tuning and to the controller tuning performed on a real system. The controller uses an ideal PID structure, and controller parameters are calculated by four well-known tuning methods for each case. The experiments are conducted on a flow control unit in a laboratory, while modelling and simulations are performed in Matlab. Graphical evaluation and numerical RMSE metrics are used to determine the quality of the tuning method. By evaluating controller tuning performance based on the ANN model, this research aims to validate the effectiveness of ANN models for practical control applications.