Ship Operation Research Articles

Towards sustainable energy selection for ships in response to maritime regulation

Ship owners and operators are continuously seeking financially viable ways to comply with the Sulphur emission limits set out by the IMO, which calls for a reduction of sulphur content in ship fuel to less than 0.5% in 2020. The prudent decision in managing required energy sources by shipowners/operators will lead to an efficient ship operating decision, subsequently resulting in long-term financial return. The study aims to identify the factors influencing ship owners and operators to select the energy types in operating their vessels. Furthermore, the research explores the most desired energy sources to address the IMO Sulphur Cap Regulations 2020. One operation research method, namely the Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE), and one econometrics tool, namely the conditional logit model, have been used to explore the research objectives. The PROMETHEE analysis suggests that clean fuel (VLSFO) is the best alternative to respond IMO 2020, followed by scrubber installation. The conditional logit analysis suggests almost the same, indicating clean fuel as the only alternative for responding to regulation. The decision is driven by the vessel's age negatively and the size of the vessel positively. The study will enormously impact the ship’s energy procurement practices in Bangladesh.

Strategic approaches to crisis management: The global challenges of the pandemic on the Turkish container shipping industry

The Covid-19 pandemic has posed significant challenges to the sector, and Türkiye’s strategic location plays a crucial role in crisis management. This study aims to explore the repercussions of the pandemic on the sector and propose appropriate solutions. To identify the most effective solutions for prioritizing the challenges posed by Covid-19, 53 sector experts participated in the study. The Stepwise Weight Assessment Ratio Analysis method was used to weigh the criteria representing these challenges. The solutions were ranked using the Weighted Aggregated Sum Product Assessment and Combined Compromise Solution methods. The results indicate that the primary challenge is the increased shipping cost, followed by the prolongation of the transport time, global-scale impacts, and port congestion. The most appropriate solution identified was alternative vessel loading, followed by mixed-mode transportation usage, reservation guarantees, long-term contracts with shipping operators, integrated logistics services, container purchases, and technological integration

Implementation of Maritime Transportation Service Policy in Riau Province (Study on the Ro-Ro Ship Dumai – Tanjung Kapal Pulau Rupat)

Sea transportation is a mobile bridge that acts as a link between areas separated by water. Rupat Island is an area surrounded by waters and designated as a National Tourism Strategic Area that can only be accessed using sea transportation. Sea transportation used to connect Rupat Island with Dumai City is the Ro-Ro (Roll On-Roll Off) ship. This research is motivated by various problems in the implementation of the Ro-Ro ship crossing service policy, including inappropriate vehicle spacing on the ship, exceeding load capacity, and deviation from the predetermined operational schedule. The purpose of this study is to determine and analyze the implementation of sea transportation service policies and to identify obstacles in the implementation of sea transportation service policies in Dumai - Tanjung Kapal, Rupat Island. This research employs Warwick's theory (1979), which comprises three components: Political Leadership Commitment, Organizational Capability, Implementation Commitment, and Interest Group Support. The method used in this research is descriptive qualitative research, utilizing data collection techniques such as observation, interviews, and documentation. The implementation of the Dumai - Tanjung Kapal, Rupat Island Ro-Ro boat sea transportation service policy is overseen by the Riau Provincial Transportation Agency and managed by UPT Transportation Management Region I, UPT Pelabuhan Rupat District, and BPTD. However, third-party ship operators have not efficiently executed these policies, encountering various obstacles including stakeholders' lack of understanding of their roles and functions, insufficient budget for infrastructure development, and limited community response to service improvements. To optimize the implementation of Ro-Ro ship service policies in accordance with Minister of Transportation Regulation Number 115 of 2016 and Minister of Transportation Regulation Number 62 of 2019, these obstacles must be addressed to ensure service standards are met.

Research on the Application of Stewart Platform for Wave Compensation

In the field of ocean engineering, the efficiency and safety of ship operation are significantly affected by waves. Faced with the instability and safety risks of ship crane operation under complex sea conditions, a design and research method of efficient wave compensation device based on Stewart platform is proposed in this paper, aiming at developing a compensation system that can respond in real time and effectively counteract the effects of waves, so as to improve the safety and efficiency of offshore operations. Aiming at the limitation of traditional wave compensation technology, this paper adopts the BP neural network PID control algorithm combined with Stewart platform. Utilizing the Stewart platform's six-degree-of-freedom motion capability, fast compensation of wave motion is achieved. On this basis, the BP neural network PID control strategy is introduced to optimize the response speed and compensation accuracy of the system, and realize the efficient and accurate compensation of wave motion under complex sea conditions.

Distributionally Robust Optimal Scheduling of Hybrid Ship Microgrids Considering Uncertain Wind and Wave Conditions

A hybrid ship uses integrated generators, an energy storage system (ESS), and photovoltaics (PV) to match its propulsion and service loads, and together with optimal power and voyage scheduling, this can lead to a substantial improvement in ship operation cost, ensuring compliance with the environmental constraints and enhancing ship sustainability. During the operation, significant uncertainties such as waves, wind, and PV result in considerable speed loss, which may lead to voyage delays and operation cost increases. To address this issue, a distributionally robust optimization (DRO) model is proposed to schedule power generation and voyage. The problem is decoupled into a bi-level optimization model, the slave level can be solved directly by commercial solvers, the master level is further formulated as a two-stage DRO model, and linear decision rules and column and constraint generation algorithms are adopted to solve the model. The algorithm aims at minimizing the operation cost, limiting greenhouse gas (GHG) emissions, and satisfying the technical and operational constraints considering the uncertainty. Extensive simulations demonstrate that the expected total cost under the worst-case distribution is minimized, and compared with the conventional robust optimization methods, some distribution information can be incorporated into the ambiguity sets to generate fewer conservative results. This method can fully ensure the on-time arrival of hybrid ships in various uncertain scenarios while achieving expected operation cost minimization and limiting greenhouse gas (GHG) emissions.

Towards risk-informed design and operation of ammonia-powered ships: Critical aspects and prospective solutions

Ammonia is a promising fuel for marine propulsion and generation, yet its acute toxicity and associated safety challenges necessitate careful consideration. Current regulations recognize the hazards of ammonia, introducing numerous technical safety measures in response. However, the effectiveness of these measures in ensuring acceptable risk levels for future vessels remains to be fully assessed. This study estimates the risk level onboard ammonia-powered ships, identifying the aspects with the largest and controllable influence on it. Three hypothetical concepts of ammonia fuel supply were developed in this study on an example tanker vessel and analyzed using the quantitative risk assessment (QRA) methodology. The obtained risk profiles were evaluated against the risks by an equivalent liquefied natural gas-fueled system, serving as a benchmark. The results demonstrate that ammonia-fueled ships exhibit individual risk levels for engineering crew with periodic duties in fuel preparation rooms (FPR), or similar compartments, which are 1–1.5 orders of magnitude higher than those observed for a conventional gas-fueled alternative. An analogous increase has been noted for the public potentially present onboard or in proximity. The study underscores the importance of managing human-machine interactions, enhancing the reliability of supply systems, and managing the systems’ complexity to mitigate risks in FPRs. Regarding public safety, the analysis highlights new risk mechanisms introduced by ammonia and examines how storage conditions affect exposure levels. By offering a detailed QRA framework, this research contributes to the development of effective risk management strategies for ammonia-powered ships.

A New Method of Intelligent Fault Diagnosis of Ship Dual-Fuel Engine Based on Instantaneous Rotational Speed

Ship engine misfire faults not only pose a serious threat to the safe operation of ships but may also cause major safety accidents or even lead to ship paralysis, which brings huge economic losses. Most traditional fault diagnosis methods rely on manual experience, with limited feature extraction capability, low diagnostic accuracy, and poor adaptability, which make it difficult to meet the demand for high-precision diagnosis. To this end, a fusion intelligent diagnostic model—ResNet–BiLSTM—is proposed based on a residual neural network (ResNet) and a bidirectional long short-term memory network (BiLSTM). Firstly, a multi-scale decomposition of the instantaneous rotational speed signal of a ship’s engine is carried out by using the continuous wavelet transform (CWT), and features containing misfire fault information are extracted. Subsequently, the extracted features are fed into the ResNet–BiLSTM model for learning. Finally, the intelligent diagnosis of ship dual-fuel engine misfire faults is realized by the classifier. The model combines the advantages of ResNet18 in image feature extraction and the capability of BiLSTM in temporal information processing, which can efficiently capture the time-frequency features and dynamic changes in the fault signal. Through comparison experiments with fusion models AlexNet–BiLSTM, VGG–BiLSTM, and the existing AlexNet–LSTM and VGG–LSTM models, the results show that the ResNet–BiLSTM model outperforms the other models in terms of diagnostic accuracy, robustness, and generalization ability. This model provides an effective new method for intelligent diagnosis of ship dual-fuel engine misfire faults to solve the traditional diagnostic methods’ limitations.

Investigation and evaluation of risk of pathogen transfer by ballast water in Shahid Rajaee Port, Hormozgan Province, Iran.

Ballast water is essential for ship operations, but can also transport harmful organisms between ports, threatening local environments. The Ballast Water Management (BWM) Convention, established by the International Maritime Organization (IMO), requires ships to implement ballast water management measures to address this issue. In this study, ballast water samples were collected from ships entering Shahid Rajaee Port in Iran before and after the Ballast Water Management Convention came into force in 2017. The sampling was conducted in coordination with Iranian authorities, following IMO guidelines. Vibrio cholerae, Escherichia coli, and Enterococci were identified in 97 ships, and physical parameters such as temperature, salinity, and pH were measured in 15 ships. To prevent sample contamination, rigorous protocols were followed, including using sterile equipment, appropriate storage, and immediate transfer to the lab. The results showed that before the BWM Convention, V. cholerae was found in 6 out of 14 ships (42%), with ballast water retention times of 2-58days. After the convention, V. cholerae was found in only 2 out of 83 ships (2.4%), with ballast water retention of 2-3days. This indicates a significant reduction in the risk of pathogen transfer through ballast water. Further analysis after the Convention showed that V. cholerae was initially detected in 4 out of 15 ships, but only 1 ship (6.6%) had levels above the standard. E. coli and Enterococci were also detected in multiple ships, but their levels were below the standard. The study investigated the relationship between the bacterial levels and the physical parameters. While some correlations were found between E. coli/Enterococci and parameters like pH, temperature, salinity, and water retention time, no significant impact of physical parameters on V. cholerae levels was observed. T-tests revealed significant relationships between the bacterial levels and the physical parameters, as well as the ballast water retention time. Ships were found to have followed the ballast water exchange guidelines of exchanging at least 200 or 50 nautical miles from shore and at depths over 200m, as mapped out for the Persian Gulf region. The results indicate that the implementation of the BWM Convention has been effective in reducing the risk of pathogen transfer through ballast water. However, the study emphasizes the importance of ongoing monitoring and enforcement to ensure continued compliance with the convention's requirements.

Trajectory tracking for autonomous surface ships using Gaussian process regression and model predictive control with BVS strategy

Autonomous navigation is critical to the development of next-generation shipping systems. The proposal of intelligent ships enables innovation in the shipping and shipbuilding industry and increases the safety and efficiency of ship operations. Autonomous control of surface ships to follow a prescribed trajectory is critical in a variety of maritime applications. This paper proposes a trajectory tracking control strategy for autonomous surface ships that combines nonparametric modelling using Gaussian Process Regression (GPR) with the Model Predictive Control (MPC) framework. A Bézier curve-based Virtual Ship(BVS) guidance strategy is proposed to convert dynamic trajectory points into reference heading angles and speeds, such that the trajectory tracking problem can be decomposed into heading control and speed control problems. Gaussian process regression is utilised to identify the correlation between propeller revolution speed and ship speed, as well as the correlation between rudder angle and heading angle based on experimental data. Two GPR models are therefore constructed as the prediction models for designing MPC controllers for heading control and speed control, respectively. Nonlinear optimisation algorithms are utilised to search for optimal control commands in each sampling interval to solve the optimisation problem in MPC with GPR models and input constraints. Simulations are carried out to evaluate the effectiveness of the proposed method.

Investigating Fuel Reduction Methods Through Propeller Optimization on Coastal Ro-Ro Liner Vessels

In light of efforts to reduce GHG emissions, liner ship operators in coastal navigation are undertaking numerous activities to contribute to the reduction of fossil fuel consumption during the transitional period toward environmentally friendly propulsion systems without compromising their regular operations. Regular ship overhauls, which include removing fouling from the underwater part of the hull and optimizing propellers, also contribute to this goal and are the focus of this research. The ship propeller, as a key component of the propulsion system, plays a significant role in ensuring reliable, safe, and economical navigation. Proper selection and regular maintenance of the propeller are essential for achieving optimal performance. This paper presents the optimization of a fixed-pitch propeller according to the ISO 484/2 standard to Class I using Metrascan 3D technology. This technology enables a detailed analysis of the propeller geometry, identification of irregularities, correction, and adjustment to achieve better hydrodynamic characteristics. The pitch corrections of the propeller resulted in improved overall vessel performance and fuel consumption, as demonstrated by comparing the consumption before and after the optimization.

A New Deep Learning Methodology for Alarm Supervision in Marine Power Stations.

Marine engineering officers operate and maintain the ship's machinery during normal navigation. Most accidents on board are related to human factors which, at the same time, are associated with the workload of the crew members and the working environment. The number of alarms is so high that, most of the time, instead of helping to prevent accidents, it causes more stress for crew members, which can result in accidents. Convolutional Neural Networks (CNNs) are being employed in the recognition of images, which depends on the quality of the images, the image recognition algorithm, and the very complex configuration of the neural network. This research study aims to develop a user-friendly image recognition tool that may act as a visual sensor of alarms adjusted to the particular needs of the ship operator. To achieve this, a marine engineering simulator was employed to develop an image recognition tool that advises marine engineering officers when they are conducting their maintenance activities, with the aim to reduce their stress as a work risk prevention tool. Results showed adequate accuracy for three-layer Convolutional Neural Networks and balanced data, and the use of external cameras stands out for user-friendly applications.

A human factor analysis of remote-control failure for maritime autonomous surface ships

Human factor management is crucial to the safety of maritime shipping. Whilst there has been extensive research on human factors in conventional shipping, the development of autonomous shipping necessitates the investigation of emerging human factors under the evolving landscape. Given that the research on human factor analysis for remote operation failure is limited, this study aims to enrich the literature by establishing a comprehensive human factor framework and empirically assessing the factors. Based on the literature review and expert interview, a total of ten categories and thirty-one human factors are organized under the human factor analysis and classification system. Adopting the purposive sampling, questionnaire surveys are then distributed to experts specializing in ship operations and automation to collect their degree of belief on potential human factors. A hybrid method composed of evidential reasoning and the rule-based Bayesian network is applied to analyze the data. The empirical results indicate that adverse mental states and adverse cognitive states are perceived to be the most threatening risk categories in remote operations. This study expands the academic research on human factor analysis and highlights the significance of managing psychological and cognitive factors. The findings provide managerial implications for the future safety management of autonomous ships.

Optimizing Maritime Industry Management: An Innovative Leak Repair Technique for Ballast and Cargo Tanks

The operation of ships at sea involves various risks, including damage to the ship's structure, particularly to the ballast tanks and cargo tanks. Leaks in these tanks can cause significant financial losses, seriously impact the environment, and even threaten lives at sea. Therefore, this study aims to analyze the causes of leaks in ballast tanks and implement repair techniques used to address leaks in the ballast and cargo tanks on the vessel MT. Sepinggan. It also evaluates maintenance practices that include technical patching procedures and regular docking as part of ship management strategies. This research uses field research methods and literature studies with a qualitative descriptive approach, where the research sample is taken through purposive sampling with MT Sepinggan as the research object. The study shows leaks in the ballast tanks of MT. Sepinggan is caused by delayed docking and a lack of routine maintenance, which makes the tank plates thin and prone to leakage. Repair techniques, including patching procedures and regular docking schedules, have proven effective in addressing leaks and improving ship management. Limited funds and uncertainty of ship operational routes also resulted in irregular maintenance schedules.

The effect of failure on energy efficiency in maritime vessels autopilot systems

The increasing demands for energy efficiency and environmental sustainability in the maritime industry have underscored the critical importance of optimizing autopilot systems, which, despite their significance, are often overlooked in ship energy efficiency management. The objective of this study is to enhance the energy efficiency of ships by focusing on the efficiency of autopilot systems, which play a significant role in the management of energy efficiency. The research emphasizes the need for effective decision support systems for ship operators, not only for optimizing ship speeds but also for making informed operational decisions. By utilizing Fuzzy Fault Tree Analysis (FFTA), the study identifies and prioritizes the causes of efficiency losses in autopilot systems and examines their frequency. Based on expert opinions, the research delves into the complexity of autopilot systems and the interactions among various components. Notably, the study highlights the impact of multiple factors on the efficiency of complex autopilot systems, elucidating their relationships through Minimum Cut Sets (MCS) analysis. Furthermore, attention was drawn to the “Improper Alarm Input” event caused by insufficient knowledge and awareness among ship operators, which hinders the effective use of autopilot systems. The findings demonstrate that decision support systems can increase energy efficiency and contribute to the reduction of operational errors by reducing the human factor, which is 99% effective on the “Inefficient Heading Control System”. Additionally, proper utilization of autopilot systems can lead to a decrease in a ship's carbon footprint and operating costs. Overall, the results can affect strategic decisions in ship energy efficiency management and encourage significant steps toward achieving International Maritime Organization's (IMO) sustainability objectives.

Analysis of the Suboptimal Performance of the Steering Gear on MT. Narpatisuta

The steering gear is one of the auxiliary machineries on board a ship that is used to move the ship's rudder, allowing the ship to turn to the right or left. This machinery utilizes an electro-hydraulic system, which combines electric and hydraulic technologies to control the ship's steering direction. The research focuses on analyzing the suboptimal performance of the steering gear on the MT. Narpatisuta. The objectives of the research are to identify the factors causing the suboptimal performance of the steering gear, the impacts resulting from this suboptimal performance, and the efforts made to address the suboptimal performance of the steering gear on the MT. Narpatisuta. The research method used is a qualitative descriptive method, with data collection techniques in the form of observation, interviews, documentation and literature study. The data analysis technique uses the SHEL method. Test the validity of the data using triangulation. The research results show that suboptimal performance of the steering gear is caused by hydraulic pipe leaks, deteriorating quality of hydraulic oil, and damage to seal and o-ring components, which lead to reduced steering gear performance, slower rudder movement, and can disrupt ship operations, endangering crew safety and the ship's cargo. Efforts that can be made to remedy this include routine inspection and maintenance of the steering gear and its components. Thus, this research emphasizes the need for routine inspection and maintenance to prevent these problems which must ensure maximum steering gear function, maintain safety and ship operational efficiency.

Analysis of the Impact of Dynamic Operation of PEMFCs for Ship Applications

<i>Dynamic responses of polymer electrolyte membrane fuel cells (PEMFC) were analyzed under varying loads to understand the performance of PEMFCs for ship applications. Experiments employing single cells were conducted under a static load with a constant current of 6.64 A and dynamic loads at a ramp rate of 2.5%/s and 5.0%/s, representing a simplified ship operation condition. Under the static load, the voltage drop was 0.054 V at 1.00 A/cm<sup>2</sup> and was dominant below 0.40 A/cm<sup>2</sup>. In contrast, under the dynamic load (5.0%/s ramp rate), the voltage drop was 0.061 V at 1.00 A/cm<sup>2</sup> and was dominant above 0.40 A/cm<sup>2</sup>, with similar behavior observed at the ramp rate of 2.5%/s. To compare performance across load conditions, degradation rates were calculated through linear regression of the voltage drop for each current density. The degradation rate at 1.00 A/cm<sup>2</sup> was 0.143 mV/h under static load conditions and 0.174 mV/h under dynamic load conditions (ramp rate: 5.0%/s), indicating approximately 21% greater degradation rates than those observed under static load conditions. These experimental results provide foundational data for PEMFC performance analysis, particularly regarding performance degradation caused by dynamic loads in marine environments.</i>

A ship motion forecasting approach based on Fourier transform, regularized Bi-LSTM and chaotic quantum adaptive WOA

Ship motion forecasting is of great significance to assist ship navigation and ensure ship operation. However, due to the coupling influence of wind, waves, currents and other factors, the ship motion has a strong time variation, which brings great challenges to the improvement of the ship motion forecasting accuracy. This paper proposes a novel approach for ship motion forecasting. Firstly, Fourier Transform (FT) is used for data pre-processing to deal with the data noise. Secondly, the Bi-LSTM is applied to simulate the nonlinear dynamical system of ship motion, and the Dropout mechanism and l2 regularization method are used to improve the generalization performance of the network, and a novel ship motion prediction model (FRB model) is constructed. Thirdly, in view of the inherent defects of Whale Optimization Algorithm (WOA), based on chaotic mapping and quantum computing, a new Chaotic Quantum Adaptive Whale Optimization Algorithm (CQAWOA) is proposed to construct a new ship motion forecasting approach, namely FRB&CQAWOA. Finally, based on the measured pitch and roll data of ship, the feasibility and superiority of the established FRB model and the proposed CQAWOA are tested. The test results indicate that the proposed model have better prediction accuracy than other algorithms selected in this paper.

Predicting practical ship powering and speed loss in waves using added resistance test results

ABSTRACT This paper introduces a practical technique for accurately estimating ship propulsion and speed loss in the presence of waves. Utilising the previously established self-propulsion estimation framework, our approach enhances the method, which previously solely considered bare hull resistance, propeller performance, thrust deduction factor, and wake fraction, by incorporating added resistance test results. The full-scale DTC container ship is the focus of our investigation, providing a thorough examination of its performance in different sea states. The model's predictive abilities are enhanced by incorporating test data on added ship resistance, and it addresses a crucial aspect that is generally disregarded while estimating self-propulsion. This research is considered to be a contribution to the advancement of ship hydrodynamics, as it offers a practical and reliable tool for ship designers, operators, and researchers to accurately assess powering requirements and speed loss in harsh ocean conditions.

Application of Safety Procedure As Efforts To Prevent Work Accidents In MT. Serang Jaya Engine Room

Safety Procedure is a formal written safety procedure and contains procedures, identification and obligations to carry it out while working. Not optimal due to lack of understanding of Safety Procedures that pose a risk of work accidents. This study aims to determine the causes and impacts of the non-optimal implementation of Safety Procedures and efforts so that the Safety Procedure can run optimally so as to prevent work accidents in the engine room. The research method used in this study is a qualitative method with a descriptive pattern. Research data sources are obtained from primary and secondary data. Data collection techniques through observation, literature study, interviews and documentation during the time researchers carried out marine practices on the MT ship. Serang Jaya. The data analysis techniques used are by collecting data, reducing data, presenting data and drawing conclusions or verifying data which is also supported by fishbone analysis. Testing the validity of data using the tringulation method. The results stated that the factors of not optimal Safety Procedure due to lack of crew understanding of Safety Procedure, and crew concern for safety as well as lack of supervision and coordination between crew and management are the reasons why Safety Procedure cannot run optimally. The impact of not optimal Safety Procedure is the high risk of work accidents, hampering ship operations, harming the ship or the parties concerned. Efforts that can be made to optimize this are by carrying out Safety meeting activities, Toolbox Meeting, Drill, installing Safety Signs. And always provide counseling related to Safety Procedure in the engine room.

Contribution of Onshore Power Supply (OPS) and Batteries in Reducing Emissions from Ro-Ro Ships in Ports

Increasingly restrictive environmental regulations for the maritime sector have led shipping companies to look for technological alternatives to reduce emissions. This article introduces a methodology to analyse emission reductions of ships in port by incorporating batteries into the ships or using an onshore power supply system. These have not yet been considered together for comparison or with a focus on ship operation. The aim is to avoid the use of auxiliary engines in ports. First, the cost calculation method to be used is specified; then, the engine’s behaviour and the established basic navigation criteria are analysed; and finally, different alternatives are considered. A methodology is afterwards defined for selecting alternatives, comparing their costs with those of using auxiliary engines in port. As an example, it is applied to a Ro-Ro route between the ports of Montoir (France) and Vigo (Spain). The results indicate that incorporating batteries into the ship produces greater savings in annual costs than onshore power supply. The cost savings from onshore power supply depend on the range of prices in each port. However, the greatest emission savings are obtained by using the onshore power supply. This methodology can be extrapolated to other routes and vessels by incorporating real operating data.