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The recovery of exhaust gas recovery from marine engines is gaining attention in regard to saving fuel and improving system efficiency. Waste heat recovery is particularly beneficial for providing thermal and electric power, and offers efficient solutions to both economic and environmental challenges. The use of waste heat recovery technology offers the opportunity to lower fuel consumption and improve systems, and this approach also falls in line with the stringent emissions guidelines of the International Maritime Organization. This paper describes a unique exhaust gas heat recovery system in which a thermal oil system is used to heat fuel and feed cargo, in order to lower exploitation costs while also addressing environmental issues. CFD simulations of the heat recovery unit with plain and finned helix coils provide important insights into their thermal performance and pressure characteristics. The results indicate that the incorporation of fins could markedly enhance the heat transfer performance. Finned configurations are also found to have higher oil outlet temperatures, reaching up to 145.4°C in the case of a rectangular configuration. In general, this study contributes to the advancement of waste heat recovery technologies in marine applications.

With the increasing demand for efficient maritime logistic management, industries are striving to develop automation software. However, collecting data for analytics from diverse sources like shipping routes, weather conditions, historical incidents, and cargo specifications has become a challenging task in the distribution environment. This challenge gives rise to the possibility of faulty products and traditional testing techniques fall short of achieving optimal performance. To address this issue, we propose a novel decentralised software system based on Transfer Learning and blockchain technology named as BETL (Blockchain -Enabled Transfer Learning). Our proposed system aims to automatically detect and prevent vulnerabilities in maritime operational data by harnessing the power of transfer learning and smart contract-driven blockchain. The vulnerability detection process is automated and does not rely on manually written rules. We introduce a non-vulnerability score range map for the effective classification of operational factors. Additionally, to ensure efficient storage over the blockchain, we integrate an InterPlanetary File System (IPFS). To demonstrate the effectiveness of transfer learning and blockchain integration for secure logistic management, we conduct a testbed-based experiment. The results show that this approach can achieve high precision (98.00%), detection rate (98.98%), accuracy (97.90%), and F-score (98.98), which highlights its benefits in enhancing the safety and reliability of maritime logistics processes. Additionally, the computational time of BETL (the proposed approach) was improved by 18.9% compared to standard transfer learning.

Logistics centres are currently performing a key function in the development of countries through their ability to regulate goods, markets, and transport. This is shown by the infrastructure, cost, goods flow, and quality of logistical services provided by these centres. Nevertheless, in developing nations or regions with antiquated logistics infrastructure, conventional logistics centres seem to struggle to manage the volume of commodities passing through them, resulting in persistent congestion and an unsteady flow of goods inside these facilities. This issue poses a challenge to the progress of any nation. The emergence of new technology offers a potential avenue to solve the problems inherent in traditional logistics centres. Most prominently, four technologies (the Internet of Things (IoT), Blockchain, Big Data and Cloud computing) are widely applied in traditional logistics centres. This work has conducted a thorough analysis and evaluation of these new technologies in relation to their respective functions and roles inside a logistics centre. Furthermore, this work proposes difficulties in applying new technologies to logistics centres related to issues such as science, energy, cost, or staff qualifications. Finally, future development directions, related to expanding policies in technological applications, or combining each country’s policies for the logistics industry, are carefully discussed.

In marine vessel operations, fuel costs are major operating costs which affect the overall profitability of the maritime transport industry. The effective enhancement of using ship fuel will increase ship operation efficiency. Since ship fuel consumption depends on different factors, such as weather, cruising condition, cargo load, and engine condition, it is difficult to assess the fuel consumption pattern for various types of ships. Most traditional statistical methods do not consider these factors when predicting marine vessel fuel consumption. With technological development, different statistical models have been developed for estimating fuel consumption patterns based on ship data. Artificial Neural Networks (ANN) are some of the most effective artificial methods for modelling and validating marine vessel fuel consumption. The application of ANN in maritime transport improves the accuracy of the regression models developed for analysing interactive relationships between various factors. The present review sheds light on consolidating the works carried out in predicting ship fuel consumption using ANN, with an emphasis on topics such as ANN structure, application and prediction algorithms. Future research directions are also proposed and the present review can be a benchmark for mathematical modelling of ship fuel consumption using ANN.

Recently, because of serious global challenges including the consumption of energy and climate change, there has been an increase in interest in the environmental effect of port operations and expansion. More interestingly, a strategic tendency in seaport advancement has been to manage the seaport system using a model which balances environmental volatility and economic development demands. An energy efficient management system is regarded as being vital for meeting the strict rules aimed at reducing the environmental pollution caused by port facility activities. Moreover, the enhanced supervision of port system operating methods and technical resolutions for energy utilisation also raise significant issues. In addition, low-carbon ports, as well as green port models, are becoming increasingly popular in seafaring nations. This study comprises a comprehensive assessment of operational methods, cutting-edge technologies for sustainable generation, storage, and transformation of energy, as well as systems of smart grid management, to develop a green seaport system, obtaining optimum operational efficiency and environmental protection. It is thought that using a holistic method and adaptive management, based on a framework of sustainable and green energy, could stimulate creative thinking, consensus building, and cooperation, as well as streamline the regulatory demands associated with port energy management. Although several aspects of sustainability and green energy could increase initial expenditure, they might result in significant life cycle savings due to decreased consumption of energy and output of emissions, as well as reduced operational and maintenance expenses.

Due to recent emission-associated regulations imposed on marine fuel, ship owners have been forced to seek alternate fuels, in order to meet the new limits. The aim of achieving low-carbon shipping by the year 2050, has meant that alternative marine fuels, as well as various technological and operational initiatives, need to be taken into account. This article evaluates and examines recent clean fuels and novel clean technologies for vessels. The alternative fuels are classified as low-carbon fuels, carbon-free fuels, and carbon neutral fuels, based on their properties. Fuel properties, the status of technological development, and existing challenges are also summarised in this paper. Furthermore, researchers have also investigated energy-saving devices and discovered that zero-carbon and virtually zero-carbon clean fuels, together with clean production, might play an important part in shipping, despite the commercial impracticability of existing costs and infrastructure. More interestingly, the transition to marine fuel is known to be a lengthy process; thus, early consensus-building, as well as action-adoption, in the maritime community is critical for meeting the expectations and aims of sustainable marine transportation.

Owners of vessels are interested in the lowest possible operating costs. These costs are mainly related to fuel consumption during navigation. To manage it rationally, the main decision-making problem is selecting the proper parameters of the ship’s propulsion system during navigation. In practice, operators of ships equipped with controllable pitch propellers controlled in manual mode make a selection of the commanded outputs based on their own knowledge, intuition, and all accessible information regarding sea conditions. In many cases, their decisions are unreasonable or incorrect. Therefore, it would be desirable to support their decision-making in selecting the commanded outputs.

In this paper, we examine the problem of optimising the process of topping up lubricating oil in medium-speed marine engines. This process is one of the methods that can be applied to improve the properties of lubricating oil. The amount of fresh oil added to lubricating oil system always balances its consumption, but the method used to top up depends on the marine engineer. Small amounts of fresh oil can be added at short intervals, or large ones at long intervals, and the element of randomness often plays a significant role here. It would therefore be valuable to find a method that can help the mechanical engineer to choose the right strategy. We apply a multi-criteria optimisation method for this purpose, and assume that the criterion functions depend on the concentration of solid impurities and the alkalinity, which are among the most important aspects of the quality and properties of lubricating oil. These criterion functions form the basis for multi-objective optimisation carried out with the use of the MATLAB computer program.

The recovery of exhaust gas recovery from marine engines is gaining attention in regard to saving fuel and improving system efficiency. Waste heat recovery is particularly beneficial for providing thermal and electric power, and offers efficient solutions to both economic and environmental challenges. The use of waste heat recovery technology offers the opportunity to lower fuel consumption and improve systems, and this approach also falls in line with the stringent emissions guidelines of the International Maritime Organization. This paper describes a unique exhaust gas heat recovery system in which a thermal oil system is used to heat fuel and feed cargo, in order to lower exploitation costs while also addressing environmental issues. CFD simulations of the heat recovery unit with plain and finned helix coils provide important insights into their thermal performance and pressure characteristics. The results indicate that the incorporation of fins could markedly enhance the heat transfer performance. Finned configurations are also found to have higher oil outlet temperatures, reaching up to 145.4°C in the case of a rectangular configuration. In general, this study contributes to the advancement of waste heat recovery technologies in marine applications. [ABSTRACT FROM AUTHOR]

Owners of vessels are interested in the lowest possible operating costs. These costs are mainly related to fuel consumption during navigation. To manage it rationally, the main decision-making problem is selecting the proper parameters of the ship's propulsion system during navigation. In practice, operators of ships equipped with controllable pitch propellers controlled in manual mode make a selection of the commanded outputs based on their own knowledge, intuition, and all accessible information regarding sea conditions. In many cases, their decisions are unreasonable or incorrect. Therefore, it would be desirable to support their decision-making in selecting the commanded outputs. For this reason, we have decided to develop a decision support system in the form of an expert system. This computer-aided system supports the selection of the commanded outputs of the ship's propulsion system. The most important component of this system is the two-criteria optimization model, allowing the rational management of the ship fuel consumption and navigation time. [ABSTRACT FROM AUTHOR]