A Bayesian Belief Network Model for Integrated Energy Efficiency of Shipping
Climate change is one of the major problems in today’s world and shipping has a direct influence on climate change by the amount of energy consumed and volume of emissions generated during shipping and port operations. The energy efficiency and port operation relationship has been widely mentioned in the existing literature under the term of energy efficiency management. However, there is still a need for detailed research on the ship-port interface development regarding holistic energy efficiency. The complex logistic processes should include a port performance study to avoid the inevitable delays and to obtain a more energy efficient transport system. Therefore, ports and fleets can be managed together within a conceptual communication framework. The primary purpose of this research is to enhance the scientific understanding of port and ship operation inter-operability based on energy efficiency interactions. A theoretical framework is developed to investigate how ports and ships could work together to reduce energy consumption and CO2 emissions. The integrated shipping system is analysed to create a unique Bayesian Belief Network (BBN) model aiming to support the operational optimisation of the ship and port interface. In this research, the BBN theory is applied to an oil tanker case study in order to examine the energy efficiency of voyages between two ports. This paper aims to provide a guide to the holistic energy efficiency of oil/product tanker shipping operations.
KeywordsIntegrated shipping system Energy efficiency Bayesian networks Tanker shipping Ship-port interface
This research is funded by Turkish Government. Authors specially thank to National Education Ministry of Turkey and Bursa Technical University, University of Strathclyde, and Izmir Katip Celebi University for their support.
- Arslan, O., Besikci, E., & Olcer, A. (2014). Improving energy efficiency of ships through optimisation of ship operations. No. FY2014-3 IAMU.Google Scholar
- Banks, C. (2015). Operational practices to improve ship energy efficiency. Doctoral dissertation, University of Strathclyde.Google Scholar
- Banks, C., Turan, O., Incecik, A., et al. (2013). Understanding ship operating profiles with an aim to improve energy efficient ship operations. In Proceedings of the Low Carbon Shipping Conference, London, 9 Sep 2013.Google Scholar
- Cao, T., Coutts, A., & Lui, F. A. (2013). Combined Bayesian belief network analysis and systems architectural approach to analyse an amphibious C4ISR system. Paper presented at the 22nd National Conference of the Australian Society for Operations Research, Adelaide, Australia, 1–6 Dec 2013.Google Scholar
- Coraddu, A., Figari, M., & Savio, S. (2014). Numerical investigation on ship energy efficiency by Monte Carlo simulation. Proceedings of the Institution of Mechanical Engineers, Part M: Journal of Engineering for the Maritime Environment, 228(3), 220–234.Google Scholar
- Cui, T., Turan, O., & Boulougouris, E. (2016). Development of a ship weather routing system for energy efficient shipping. Paper presented at the Annual Conference of the International Association of Maritime Economists, 26 Aug 2016.Google Scholar
- Cullinane, K. (2014). Targeting the environmental sustainability of european shipping: The need for innovation in policy and technology.Google Scholar
- GeNIe Modeler. (2017). GeNIe modeler user manual. http://support.bayesfusion.com/docs/genie.pdf. Accessed 17 May 2017.
- Hansen, S. V. (2012). Performance monitoring of ships. Doctoral dissertation, Technical University of Denmark.Google Scholar
- Helfre, J. F., & Boot, P. A. C. (2013). Emission reduction in the shipping industry: Regulations, exposure and solutions.Google Scholar
- IMO. (2011). Final SBSTA EEDI SEEMP COP17. http://www.imo.org/en/OurWork/Environment/PollutionPrevention/AirPollution/Documents/COP%2017/Submissions/Final%20SBSTA%20EEDI%20SEEMP%20COP17.pdf. Accessed 05 January 2017.
- Intertanko and OCIMF. (2010). Virtual arrival, optimising voyage management and reducing vessel. London, UK: Oil Companies International Marine Forum.Google Scholar
- Johnson, H. (2014). GHG emissions and the energy efficiency gap in shipping, European panel of sustainable development.Google Scholar
- Kwon, Y. J. (2008). Speed loss due to added resistance in wind and waves. The Naval Architect, March, RINA, UK (pp. 14–16).Google Scholar
- Lu, R., Turan, O., Boulougouris, E., Banks, C., et al. (2014). A semi-empirical ship operation performance prediction model for voyage optimization towards. Paper presented at 2nd International Conference on Maritime Technology (ICMT), Glasgow, 23 January 2014.Google Scholar
- McKinnon, A. C. (2014). Options for reducing logistics-related emissions from global value chains. Robert Schuman Centre for Advanced Studies Research Paper No. RSCAS, 31.Google Scholar
- Nandakumar, C. G. (2012). Environmental impact of non metallic hull ships. Paper presented at Green Technologies (ICGT), 2012 International Conference IEEE, 18 Dec 2012.Google Scholar
- Parker, S., Raucci, C., Smith, T. W. P., et al. (2015). Understanding the energy efficiency operational indicator: An empirical analysis of ships from the Royal Belgian Shipowners’ Association. Energy Institute. s.l. Royal Belgian Shipowners’ Association.Google Scholar
- Plessas, T., Papanikolaou, A., Pytharoulis, M., Boulougouris, E., & Adamopoulos, N. (2013). Simulation of loading/discharging procedure of tankers, 7 Oct 2013. In C. Guedes Soares & F. Lopez Pena (Eds.), Developments in maritime transportation and exploitation of sea resources: IMAM 2013 (pp. 501–510). London: CRC Press.CrossRefGoogle Scholar
- SCG. (2009). 2006 Expanded Greenhouse Gas Inventory. Available at: https://www.portoflosangeles.org/DOC/REPORT_GHG_Inventory_2006.pdf. Accessed: 17 January 2016.
- Shao, W. (2013). Development of an intelligent tool for energy efficient and low environment impact shipping. Doctoral dissertation, University of Strathclyde.Google Scholar
- Sharma, S. (2014). Global Voyage Centre: Energy efficiency and route optimisation. http://maersklinesocial.com/global-voyage-centre-energy-efficiency-and-route-optimisation/#sthash.Qz0BkOzJ.dpuf. Accessed 18 November 2014.
- Smith, R., Madsen A. L., & Barton, D. N. (2015). Bayesian Belief Networks, Preliminary guidelines describing the set of methods for mapping and modelling ecosystem service supply and their application in the WP5 case studies. http://www.esmeralda-project.eu/getatt.php?filename=OpenNESS_D3.2_FINAL_13634.pdf. Accessed 05 January 2017.
- Sutrisnowati, R. A., Bae, H., & Park, J. (2014). Bayesian network learning for port-logistics-process knowledge discovery. International Journal of Industrial Engineering, 21(3), 141–152.Google Scholar
- UNFCCC. (2016). Background on the UNFCCC: The international response to climate change. http://unfccc.int/essential_background/items/6031.php. Accessed 15 April 2015.
- Wang, H., & Lutsey, N. (2013). Long-term potential for increased shipping efficiency through the adoption of industry-leading practices. The International Council on Clean Transportation, Retrieved July. 30 Sep 2013.Google Scholar
- Wessels, G. (2016). Actual market and regulation change/input [Seminar of department of NAOME]. University of Strathclyde, 31 March.Google Scholar
- Windischhofer, R., & Lepistö, M. (n.d.) Integrated operations: ABB’s digital business transformation for the maritime industry. http://new.abb.com/marine/generations/technical-insight/integrated-operations. Accessed 17 February 2017.
- Wunder Ground. (n.d.) Historical weather. https://www.wunderground.com/history/. Accessed 17 January 2017.
- Yuqing, L., & Tong, Z. (2008). Bayesian network to construct interoperability model of open source software. In Computer Science and Software Engineering, Presented at the 2008 International Conference on 12 Dec 2008 (Vol. 3, pp. 758–761). IEEE.Google Scholar