Principal component analysis of containership traffic in the Black Sea

A novel quantitative analysis employing the Principal Component Analysis (PCA) of containership traffic in the Black Sea from 2018 to 2021 is performed. The study uses a matrix covering five ship size classes from A to E for four years of operation, from 2018 to 2021, accounting for ship traffic, CO2, fuel consumption (FC), shipping intensity, and eco and traffic efficiency. Only the first two principal factors are analysed because of their total variation weight. Shipping intensity, FC intensity, and CO2 intensity plays a significant role in the first factor, while Eco efficiency, FC efficiency, and Traffic efficiency are considered for the second factor. Notably, the set of parameters pertains to time and is strongly associated with DWT. Two principal components were identified, F1 and F2, where F1 integrates efficiency and intensity. At the same time, F2 separates the intensity from the efficiency conditional on the ship size and the year of operations. In the principal component F1 the activities of ships A and C differ from B, D and E, separating more efficiently from less efficiently used ships, and in F2, the activities of class sizes of ships C and D and E contrast A and B ships, distinguishing the big-size class ships from small ones. It was concluded that the most intensively used ships are the ship size classes C and D, and the most efficient are ship size classes A and B. The most intensive use of the ships was in 2020, followed by 2021, and the most efficient were in 2018, 2019. Based on the ship activities and using the Within-class variance, ships are grouped into two clusters of similar activities, where the first one, with lower variance and more homogeneous, includes only the ship size class A. The second one with a relatively large variance consists of the rest size of the ships. Linear relationships considering the intensity and efficiency are derived as a function of the main variables, where the factor loading represents the variable’s coefficient, given as a relative weight to any factor

A Review on Applications of Machine Learning in Shipping Sustainability

The shipping industry faces a significant challenge as it needs to significantly lower the amounts of Green House Gas emissions at the same time as it is expected to meet the rising demand. Traditionally, optimising the fuel consumption for ships is done during the ship design stage and through operating it in a better way, for example, with more energy-efficient machinery, optimising the speed or route. During the last decade, the area of machine learning has evolved significantly, and these methods are applicable in many more fields than before. The field of ship efficiency improvement by using Machine Learning methods is significantly progressing due to the available volumes of data from online measuring, experiments and computations. This amount of data has made machine learning a powerful tool that has been successfully used to extract information and intricate patterns that can be translated into attractive ship energy savings. This article presents an overview of machine learning, current developments, and emerging opportunities for ship efficiency. This article covers the fundamentals of Machine Learning and discusses the methodologies available for ship efficiency optimisation. Besides, this article reveals the potentials of this promising technology and future challenges

Machine learning in sustainable ship design and operation: a review

The shipping industry faces a large challenge as it needs to significantly lower the amounts of Green House Gas emissions. Traditionally, reducing the fuel consumption for ships has been achieved during the design stage and, after building a ship, through optimisation of ship operations. In recent years, ship efficiency improvements using Machine Learning (ML) methods are quickly progressing, facilitated by available data from remote sensing, experiments and high-fidelity simulations. The data have been successfully applied to extract intricate empirical rules that can reduce emissions thereby helping achieve green shipping. This article presents an overview of applying ML techniques to enhance ships’ sustainability. The work covers the ML fundamentals and applications in relevant areas: ship design, operational performance, and voyage planning. Suitable ML approaches are analysed and compared on a scenario basis, with their space for improvements also discussed. Meanwhile, a reminder is given that ML has many inherent uncertainties and hence should be used with caution