Ship Operation Research Articles

OPTIMIZING SUSTAINABLE MATERIAL SELECTION FOR AIR CONDITIONERS IN A SUPPLY CHAIN

In order to cut their carbon footprint and promote environmental sustainability, the majority of businesses have now turned towards sustainable practises in their manufacturing processes and supply networks. The use of sustainable materials has drawn a lot of attention recently as a crucial step in accomplishing these goals. Choosing the material that is most suited for a product can be difficult, despite the fact that there are many sustainable materials available. This study uses machine learning – a random forest algorithm and multi-criteria decision analysis (MCDA) to optimise the use of sustainable materials in supply-chain operations. The study uses machine learning algorithms to analyse data on different sustainable materials, their characteristics and their effects on the environment. The study also investigates how an optimised material selection affects the whole supply chain, including the production, packing and shipping operations. The research offers a complete strategy for reducing the environmental effect of industrial processes by combining approaches from material engineering, supply chain management and machine learning. The novelty of this work resides in its integration of material engineering and machine learning strategies to enhance the supply chain choice of sustainable materials. As a notable example, the study highlights the potential of mycelium as a sustainable material for air conditioner components. Mycelium’s unique properties, such as its biodegradability, lightweight nature and adaptability position it as a promising candidate, enhancing the environmental profile of air conditioners. By incorporating mycelium-based components, manufacturers can significantly reduce carbon emissions, resource consumption and waste generation throughout a product’s lifecycle. This investigation underscores both the viability of mycelium and the broader significance of innovative material choices in reshaping industries towards a more sustainable future. Through such advances, this research not only contributes to the air conditioning sector but also establishes a paradigm for sustainable material adoption with far-reaching positive implications.

SYSTEMIC APPROACHES TO RATIONAL MANAGEMENT OF FUNCTIONAL MARINE TECHNICAL SYSTEMS AND COMPLEXES

In the present era, the development of effective and safe management methods for marine technical systems and complexes (MTSC) is gaining particular importance. The article, considering the continuous progress of science and technology, proposes the development of a comprehensiveapproach to the rational management of functional MTSC, integrating scientific strategies, automation, and intelligent systems.The main goal of the research is to create a framework structure of comprehensive measures for managing MTSC in conditions of partial internal and external uncertainty, with a special emphasis on the human factor and ergonomics in management. A deep risk analysis is proposed, including a review of the evolution of risk assessment methods, from intuitive to scientifically substantiated strategies, and the implementation of automated and intelligent systems in the processes of managing MTSC risks.The work focuses on the importance of creating a structural model of information support for MTSC, including the identification of key components focused on risks, studying internal and external factors affecting MTSC, and developing mechanisms for their monitoring and analysis. Significant attention is paid to the development of a scheme for ensuring rational management of MTSC in conditions of partial uncertainty, with an emphasis on creating adaptive management strategies.The importance of developing an intelligent decision-making model for MTSC is emphasized, which includes a detailed analysis of methods for intelligent data analysis specific to ship technical systems, the implementation of machine learning for dynamic ship management, and the development of criteria for management rationality.Finally, the article highlights the need to refine the method for deriving the coefficient of rational management of MTSC, including the development of models of interaction between operators and technical means to optimize interfaces and train operators.Thus, a comprehensive approach to managing MTSC is proposed, integrating scientific strategies,automation, intelligent systems, ergonomics, and considering the importance of the human factor. This approach aims to increase the efficiency, speed, accuracy, and safety of managing MTSC, especially considering the importance of monitoring the actions of ship operators. Keywords:rational management; ship technical systems; automation and intelligent systems; risk management; ergatic aspects; human factor.

Operational Risk Management On The Training Ship Bung Tomo

Risk management plays a crucial role in ensuring the sustainability of an organization's operations. At the Surabaya Navigation Polytechnic, Bung Tomo's training vessel became the backbone for cadets to undergo navigation training. However, it is important to acknowledge that the ship's operational activities are not exempt from potential risks. Therefore, the study aims to identify potential obstacles or issues that may arise in the implementation of risk management at the Tomo Training Ship. This research adopts a qualitative descriptive approach, prioritizing an in-depth understanding of the observed phenomena. Various data collection techniques, such as observations, interviews, and documentation, are used to gain comprehensive insights. The results of this study reveal that the Engine of Tomo's Training Ship faces the highest level of risk. It's related to the fact that the ship hasn't undergone a major dock process since it was officially inaugurated six years ago, causing frequent damage. Therefore, risk management focused on the engine aspects becomes an urgent need to minimize potential interference in ship operations. Precautionary measures, periodic maintenance, and possible risk reduction must be implemented carefully to ensure the safety and reliability of the Tomo Brother's Training Ship during the cadets' sailing training.

Probabilistic analysis of operational ice damage for Polar class vessels using full-scale data

To ensure the safety of maritime operations in polar waters, the IMO enforced the International Code for Ships Operating in Polar Waters (Polar Code) in 2017. To address ice navigation related risks, the Polar Code refers to a set of guidelines known as the Polar Operational Limit Assessment Risk Indexing System (POLARIS). Following POLARIS, operational limits for ice navigation are defined based on the Risk Index Outcome (RIO) value, which takes into account the prevailing ice conditions and the ice class of a ship.Recent studies indicate that the POLARIS guidelines are well-founded. However, no direct relationship between RIO values and the probability of an ice-induced hull structural damage has been established. To enable a more accurate analysis of ice navigation risks, this article addresses this issue by (i) relating measured ice-induced loads to RIO values corresponding to the ice conditions in which the loads were measured, (ii) calculating the load limits for plastic deformation and rupture of the ice belt of hull structures representing different ice classes, and (iii) defining the probability of structural damage for different load limits. The study utilizes long-term full-scale ice load measurements carried out onboard Polar Supply and Research Vessel (PSRV) S.A. Agulhas II in the Antarctic Ocean. The load limits were calculated for ice class standards, PC3, PC4, PC5, PC6, and PC7 in accordance with the Unified Requirements of the International Association of Classification Societies (IACS).On a general level, the results are consistent with earlier findings indicating that the POLARIS guidelines are well-founded. If a ship operates mainly at ‘normal operation’ level, the probability for fracture at hull are at probability levels of 10-3 and 10-4 for ice classes PC3 to PC5. The probability levels for PC6 and PC7 are higher that is possibly a result from conservative load probability distributions. When the portion of operations at ‘special consideration’ level becomes significant, the probability of fracture at the hull increases significantly. However, large ice thicknesses and the largest load magnitudes may be associated with positive RIO values. Some inconsistencies are recognized, and the uncertainty and limitations of the analysis are discussed.

Toward optimized operation of freshwater generator using computer vision, and its economic and environmental benefits

Sufficient quantities of fresh water (FW) must be maintained for the operation of ships and fresh water generators (FWGs), used to produce FW on ships tend to be suboptimal because of work schedules and low-skilled marine crew. Therefore, a system that optimally and automatically operates the discharge valve of an FWG distillate pump by recognizing the FW condition through a sight glass using computer vision technology was developed. Three deep-learning models were trained using videos of FW flowing and filling the sight glass. An optimal model structure was selected in the pre-test phase, which consisted of (i) a test using a camera and a monitor showing the validation videos, and (ii) a test based on data. In the real-test phase, an electrical valve and stepper valve were used as optimization valves, and the flow rates of the fuel oil for the generator engine (G/E) and FW for the FWG were recorded for 10 experimental conditions. The stepper valve operated in a more stable manner than the electrical valve, and its prediction performance with CNN-LSTM was superior. Finally, the economic and environmental impacts were evaluated based on the flow rates of fuel oil for the G/E and FW for the FWG.

Comprehensive evaluation of machine learning models for predicting ship energy consumption based on onboard sensor data

Machine learning models for predicting ship energy consumption are built and their influencing factors are investigated. First, data collected from a real ship is preprocessed. Six machine learning methods are used to establish the prediction models of ship fuel consumption, and the performance of models is evaluated by Mean Absolute Error, Coefficient of Determination and training time. Then, by analysing the correlation and importance of the features, it's studied whether the model established complies with the laws of physics. Finally, the factors affecting the prediction performance of machine learning models are analysed. The results show that Random Forest and Extreme Gradient Boosting are the most suitable algorithms for ship fuel consumption prediction. Data preprocessing, data normalisation, training sample size, model type, ship operating conditions, as well as the thermotechnical parameters of main engine have impact on the prediction performance. In particular, when taking the thermotechnical parameters into consideration, R2 is increased by 0.32%, MAE is reduced by 5.0%.

The enhanced role of canals and route choice due to disruptions in maritime operations

PurposeThis paper focusses on the aftermath of disruptions and the importance of the two largest canals (Suez and Panama), commenting on how during the pandemic the canal fees were lowered. Considering the ongoing efforts to decarbonize shipping, some of the ongoing disruptions will help reach these objectives faster.Design/methodology/approach Following a literature review of route choice in shipping, and a presentation of significant disruptions in recent years, the author deploys a simplified fuel consumption model and conduct case study analyses to compare different routes environmentally and economically.Findings The results explain why at times of low fuel prices as in 2020, canals provided discounts to entice ship operators to keep transiting these, instead of opting for longer routes. Considering the ongoing repercussions of the pandemic in supply chains, as well as the potential introduction of market-based measures in shipping, the value of transiting canals will be much higher in the coming years.Research limitations/implications The main limitation in this work is that the author used the publicly available information on canal tolls, for the different ship types examined.Practical implications The envisioned model is simple, and it can be readily used for any ship and route (port to port) combination available, if ship data are available to researchers.Social implications It is possible that canal tolls will increase, to account for the additional environmental benefits brought to ship operators.Originality/value The methodology is simple and transferable, and the author proposes several interesting research questions for follow-up work.

Comparative analysis of the manoeuvrability performance for a 9000 tdw chemical/oil tanker

The concept of manoeuvrability is very important for efficient ship operation but also in ship safety. The aim of this paper is to test and perform a comparative analysis of manoeuvrability performance of a 9000 tdw chemical/oil tanker. The comparative analysis has been performed between the results obtained based from two software’s, namely PHP and MPP, but also between the results obtained in the sea trials with the output data from MPP. In this way it will be shown the differences (if any) in terms of manoeuvrability., and if the two used platforms are effective for providing preliminary determination of performances of manoeuvrability.

Preliminary hydrodynamic analyses for a river research ship. comparative study of mono-hull and catamaran ship type

The design in early stages of the ships involves several preliminary hydrodynamic analyses, also for the ship operating on river waterways, IN(1.2) navigation class. This technical paper includes two main parts as preliminary assessment for the design constructive versions of a river research ship. The first part is focused in the comparative study of the mono-hull and the catamaran ship design by seakeeping and global strength criteria. The second part is focused on the ship resistance for two mono-hull versions of the river research ship. The study hydrodynamic preliminary results are the initial bases for proper design in next steps of the river research ship.

The Origin of Mineral Waste from Constanta Port and the Necessity to Introduce it into the Circular Economy

Abstract Constanta Port is the largest port on the Black Sea. Only here are operated the largest ships that have access through the Bosporus and Dardanelles straits. Of these, the largest tonnage is operated in berths 80 – 84 belonging to COMVEX SA. In the port there are also terminals for operating bulk cargo belonging to SOCEP and Minmetal. During the operation of ships, transport and storage of bulk cargo, basic activities of the terminal, losses of bulk materials occur, which periodically collected are waste because they no longer meet the original characteristics of the goods. According to GD 111/2002, these quantities of materials are called technological losses and are a mixture of iron ore, bauxite, coke and coal. The mineral components of this waste, defined as mineral waste, are from the handled materials, and which are of the highest quality. This quantity cannot be shipped to customers and therefore the respective quantities of goods represent operating and storage losses. Periodically, this mixture must be removed so that the land surfaces on which the stacks of materials are formed remain clean. The production of mineral waste is closely linked to the activity of the Ore Terminal. The mineral mixture from cleaning operations is stored in temporary storage dumps. In addition to the fact that these quantities of goods represent losses, they also represent an impact on the environment. The storage of this waste involves occupying important, available, adjacent areas and sometimes production areas. This also has implications for the operator's handling capacity as it requires additional operations involving energy consumption, labour consumption, vehicle use, etc. In conclusion, this scenario negatively affects the economic and social development of the company operating with bulk materials. Due to the specificity of the bulk cargo handling terminal, between 1991 and until now, significant quantities of mineral waste valued at approx. 650.000 t. Similar wastes are a characteristic of large ports, which transit such bulk goods, such as: Rotterdam – EMO operator – Netherlands, Dunkirk and Sollac sur Mer – France, Tianjin – China.

Effect of Daily Regulation on Navigation Conditions in Deep Reservoirs

The daily regulation and anti-regulation of upstream and downstream power stations, respectively, frequently alter the river flow regime, velocity, and surface gradient, thus resulting in unsteady flow characteristics of the river and hindering shipping, waterway maintenance, and wharf operations. This study investigated the influence of daily regulation on the navigation conditions in the deep reservoir by taking the rivers between the Three Gorges Dam and the Gezhouba Dam as the research object. Prototype observations and a depth-averaged 2-D model were used to determine the main factors affecting the propagation law of unsteady flow. The propagation pattern of unsteady flow and channel navigational conditions and measures of the power station were analyzed systematically. The results showed that the water level amplitude was affected primarily by the peak amplitude and duration of the peak shaving. Additionally, the base flow significantly influenced time spatial distributions of the water level amplitude. Impacted by the reservoir storage capacity, a threshold for the duration of peak shaving was noted; this may result in maximum water level variation. As the peak shaving duration increased, the amplitude of the water level decreased. The research results can provide theoretical support for the optimization of hub shipping.

Decarbonization of Ship Operations

Decarbonization of Ship Operations

Hydrodynamic Simulation of Green Hydrogen Catamaran Operating in Lisbon, Portugal

Similar to other industries, the maritime industry is also facing increasing restrictions on ships regarding pollution control. The research presented in this paper is aimed at studying the pros and cons of alternative fuels followed by a detailed analysis on hydrogen fuel cells (PEMFC) for a particular ship operating in Lisbon, Portugal. Dynamic forces acting on the ship have been studied for a year. Assessing various scenarios based on these results aids ship operators in making informed decisions regarding the future course of action for their existing vessels. These different cases are first: business as usual (diesel engine), second: replacing the diesel engine with a hydrogen hybrid system and, third: replacement of the ship with a new hydrogen hybrid ship. The study is based on the simulation of numerical equations and CFD simulation results. As the result, the second scenario is best suited in both aspects; namely, environmental and economic.

CFD Prediction of Ship Propeller-induced Underwater Radiated Noise

As the negative impact of man-made underwater radiated noise (URN) on marine life is recognized by the scientific community, guidelines and initiatives are proposed by port authorities and international organizations for monitoring and reducing URN from ship operations. In medium to large-size ships, propellers are often the dominating URN source as compared to on board machinery and flow turbulence. Within this paper, URN from cavitating flow around a ship propeller in behind-hull condition is predicted by a high-fidelity CFD solver, coupled with an acoustic analogy method. The noise sources are hydrodynamic flow fields around propeller and rudder models, simulated by CFD with a multi-phase flow model. Far-field acoustic noise is calculated by the permeable Ffowcs-Williams Hawkings (FWH) acoustic analogy method. Unsteady cavitation patterns and URN levels from the numerical predictions are validated against experimental results from a cavitation tunnel test. The propeller blade shape is optimized for minimum cavity volume by parametric design variations and performance evaluations based on steady CFD simulations with a simplified hull wake model. The URN predictions from unsteady CFD-FWH simulations demonstrate that the cavity volume reduction for the optimized blade design can lead to considerable decrease of low-frequency tonal noise.

Retracted: IOT Monitoring System for Ship Operation Management Based on YOLOv3 Algorithm

Retracted: IOT Monitoring System for Ship Operation Management Based on YOLOv3 Algorithm

Pemahaman Awak Kapal dan Manajemen dalam Penerapan ISM Code pada Perusahaan Agensi Awak Kapal

The purpose of the research is to find out and analyze the suitability of the crew and management perception related to work safety, protection of the marine environment and implementation of the ships’ operation using the International Safety Management Code in PT. Lautan Jaya Hasana. The main problem of the research is the lack of understanding and awareness of the crew and management of the importance to implement the ISM Code for work safety and to protect the marine environment. The ISM Code has not been implemented properly. This study used a Descriptive Qualitative method. There were 20 statements on understanding variables and actual implementation variables that were tested for validity and reliability. The samples were taken from 35 crew members from two types of bulk carriers by conducting in-depth interview with the crew members and the management employees. The result shows that most of the crew members, including the captain and the management of the company perform the same perception regarding the work safety, the protection of marine environment and the implementation of the International Safety Management Code for ships operation with a significant average difference and a perceived value of 9.20 between perceived understanding and actual implementation.

Research on Carbon Intensity Prediction Method for Ships Based on Sensors and Meteorological Data

The Carbon Intensity Index (CII) exerts a substantial impact on the operations and valuation of international shipping vessels. Accurately predicting the CII of ships could help ship operators dynamically evaluate the possible CII grate of a ship at the end of the year and choose appropriate methods to improve its CII grade to meet the IMO requirement with minimum cost. This study developed and compared five CII predicting models with multiple data sources. It integrates diverse data sources, including Automatic Identification System (AIS) data, sensor data, meteorological data, and sea state data from 2022, and extracts 21 relevant features for the vessel CII prediction. Five machine learning methods, including Artificial Neural Network (ANN), Support Vector Regression (SVR), Least Absolute Shrinkage and Selection Operator (LASSO), Extreme Gradient Boosting (XGBoost), and Random Forest (RF), are employed to construct the CII prediction model, which is then applied to a 2400 TEU container ship. Features such as the mean period of total swell, mean period of wind waves, and seawater temperature were considered for inclusion as inputs in the model. The results reveal significant correlations between cumulative carbon emissions intensity and features like cumulative distance, seawater temperature, wave period, and swell period. Among these, the strongest correlations are observed with cumulative distance and seawater temperature, having correlation coefficients of 0.45 and 0.34, respectively. Notably, the ANN model demonstrates the highest accuracy in CII prediction, with an average absolute error of 0.0336, whereas the LASSO model exhibits the highest error of 0.2817. Similarly, the ANN model provides more accurate annual CII ratings for the vessel. Consequently, the ANN model proves to be the most suitable choice for cumulative CII prediction.

Modeling Shipboard Power Systems for Endurance and Annual Fuel Calculations

_ Endurance fuel calculations are used to determine the required volume of fuel tanks; annual fuel calculations are used to estimate the fuel consumed during a year of ship operations, primarily to estimate the projected cost of fuel as part of the life cycle cost estimate. These calculations depend on the fuel rates (kg/h) for different electrical and propulsion system configurations. The fuel rates in turn depend on factors, such as equipment efficiency, prime mover-specific fuel consumption curves, electrical loads, ambient temperature, propulsion loads, and the manner in which the power and propulsion systems, are operated. This paper details how to perform endurance fuel and annual fuel calculations, provides guidance for modeling system components based on data typically provided in data sheets, and provides guidance on the manner in which the power and propulsion systems are operated. Four examples are provided to illustrate the methods using the Smart Ship System Design modeling and simulation tool along with supporting spreadsheets. Keywords Marine design for ships and offshore structures; endurance; fuel efficiency; electrical efficiency; electrical system; performance prediction

The effect of a ship's acceleration and deceleration on parametric roll motion in regular head waves at resonance conditions

The effect of a ship's acceleration and deceleration in regular head waves on parametric roll motion is examined experimentally in the study and compared with a mathematical model. Operators queries such as "Should I accelerate or decelerate the ship in resonance condition?" and "Is the magnitude of acceleration or deceleration effective in reducing large roll motion?" are tried to answer. It is well known that waves with lengths similar to that of the ship may suffer parametric roll motion. Additionally, it is possible to say that the encounter frequency itself provides the ship's tuning factor. Since one particular wave is selected with a length equal to the ship's length, the tuning factor in this study at ship operation is the velocity. Results indicate that the amplitudes of roll motion at acceleration and deceleration periods are still large. Results also indicate that the magnitude of acceleration or deceleration affects the roll motion. The mathematical model is also solved numerically and compared with experimental results to better understand the effect of acceleration magnitude.

Detection and quantification of wave trends in the Mediterranean basin

A study of a 42-year (1979–2020) long wave time series was performed for the Mediterranean Sea to detect and quantify trends in two relevant wave parameters adopted for coastal and offshore engineering purposes: significant wave height, Hs and peak period, Tp. The high resolution MeteOcean Re-Analysis database by the Department of Civil, Chemical and Environmental Engineering (DICCA) was used. At yearly and seasonal scales, the trends of the mean and maximum values were detected by the Mann–Kendall test, choosing a significance level equal to 90%. The slope or increase/decrease trends in Hs and Tp were assessed by the Theil–Sen estimator. For the mean values of Hs and Tp, increasing trends were detected for the Libyan, Levantine and Aegean seas, while for the maximum values this increasing trend was observed in a large part of the Mediterranean Sea. Finally, for the different marginal seas of the Mediterranean basin, a running trend analysis was applied in order to quantify the effect of the time window in the trend detection. The obtained results can be significant for flooding and erosion control strategies, ship and port operations, design and verification of structures, installations of Wave Energy Converters, in a hot spot for climate change such as the Mediterranean basin.