An artificial neural network based decision support system for energy efficient ship operations

Reducing fuel consumption of ships against volatile fuel prices and greenhouse gas emissions resulted from international shipping are the challenges that the industry faces today. The potential for fuel savings is possible for new builds, as well as for existing ships through increased energy efficiency measures; technical and operational respectively. The limitations of implementing technical measures increase the potential of operational measures for energy efficient ship operations. Ship owners and operators need to rationalise their energy use and produce energy efficient solutions. Reducing the speed of the ship is the most efficient method in terms of fuel economy and environmental impact. The aim of this paper is twofold: (i) predict ship fuel consumption for various operational conditions through an inexact method, Artificial Neural Network ANN; (ii) develop a decision support system (DSS) employing ANN based fuel prediction model to be used on-board ships on a real time basis for energy efficient ship operations. The fuel prediction model uses operating data -‘Noon Data’ - which provides information on a ship’s daily fuel consumption. The parameters considered for fuel prediction are ship speed, revolutions per minute (RPM), mean draft, trim, cargo quantity on board, wind and sea effects, in which output data of ANN is fuel consumption. The performance of the ANN is compared with multiple regression analysis (MR), a widely used surface fitting method, and its superiority is confirmed. The developed DSS is exemplified with two scenarios, and it can be concluded that it has a promising potential to provide strategic approach when ship operators have to make their decisions at an operational level considering both the economic and environmental aspects

Expanding the Use of Liquefied Natural Gas in the Baltic Sea Region via Tailor-made Training Activities

https://commons.wmu.se/lib_chapters/1010/thumbnail.jp

Liquefied Natural Gas as a Marine Fuel: The Case of the Baltic Sea Region

https://commons.wmu.se/lib_chapters/1011/thumbnail.jp

The development of a policy framework to mitigate underwater noise pollution from commercial vessels: the role of ports

Shipping is the most fuel-efficient type of transportation and plays a significant role in global trade. However, it has negative externalities. With the projected growth of shipping, the potential for low-frequency noise will increase along with its negative effects, such as impacts on marine species. For example, its intensity has been doubling in the North Pacific Ocean every decade for the past 60 years.Underwater noise pollution is not visible. Therefore, a scientific approach and data collection are required to raise awareness and demonstrate its negative impacts. While societal awareness in respect of other pollutants, such as oil, dangerous goods, noxious liquids substances, sewage, plastic, and air, has been raised and they have been regulated, society is not yet familiar with underwater noise pollution and, accordingly, there is no international legally binding instrument to regulate it. Ports are key interfaces between maritime transportation and land and they play a crucial role as one of the main stakeholders in the shipping industry. At the same time, this key role can be extended to cover prevention, control, mitigation, and monitoring of UWN pollution by considering appropriate policies and mitigation measures. This paper strives to identify the gaps between the potential measures and the current situation in ports and to consider multi-interdisciplinary actions by developing the concept of an Under- Water Noise Management Plan (UWNMP) within Ports. This plan is also expected to contribute to the enhancement of sustainable development along with port and ocean governance