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

E. Bal Besikci, O. Arslan, O. Turan, A.I. Ölçer

Research output: Contribution to journalArticle

30 Citations (Scopus)

Abstract

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.
LanguageEnglish
Pages393-401
Number of pages9
JournalComputers & Operations Research
Volume66
Early online date24 Apr 2015
DOIs
Publication statusPublished - 1 Feb 2016

Fingerprint

Decision Support Systems
Decision support systems
Energy Efficient
Ship
Artificial Neural Network
Ships
Neural networks
Fuel consumption
Prediction Model
Artificial neural network
Energy
Inexact Methods
Surface Fitting
Greenhouse Gases
Gas fuels
Volatiles
Multiple Regression
Fuel economy
Operator
Freight transportation

Keywords

  • ship energy efficiency
  • operational measures
  • decision support system
  • artificial neural networks

Cite this

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title = "An artificial neural network based decision support system for energy efficient ship operations",
abstract = "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.",
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An artificial neural network based decision support system for energy efficient ship operations. / Bal Besikci, E.; Arslan, O.; Turan, O.; Ölçer, A.I.

In: Computers & Operations Research, Vol. 66, 01.02.2016, p. 393-401.

Research output: Contribution to journalArticle

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