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

End-user and Stakeholder Views on Selected Risk Assessment Tools for Marine Oil Spill Preparedness and Response, Including Future Research and Development Needs

Risks in the maritime domain have various sources, of which the transportation of oil and other noxious products is one of key concern to industry and public stakeholders. Operational or accidental releases of oil or other pollutants from ships or offshore facilities into the marine environment can have disastrous effects on the marine ecosystems, while also leading to very significant economical losses. Therefore, national states have implemented various mechanisms for preventing and responding to pollution in the maritime domain, with activities which are often embedded in regional cooperation frameworks clustered around certain sea areas. To support collaborative, harmonized, and risk-informed oil spill Pollution Preparedness and Response (PPR) planning for response authorities, the Baltic Marine Environment Protection Commission (HELCOM), together with its research partners, and with extensive end-user and stakeholder inputs, have developed the OpenRisk Toolbox. This toolbox includes several risk assessment tools and techniques, which can assist in providing answers to a range of PPR risk management questions in a range of organizational contexts. To better understand and ensure the applicability and usefulness of the OpenRisk Toolbox, a workshop was organized where some of these tools were tested. Selected end user and stakeholder views on the perceived usefulness of the tools were collected and analyzed. Another workshop focused on further development needs to implement the tools in organizational practices. This paper first presents the OpenRisk Toolbox, then describes the settings of the workshops. Finally, a summary of the end-user and stakeholder views on the tested tools, and on future development needs, is given.Peer reviewe