Selection of the Best Optimal Operational Parameters to Reduce the Fuel Consumption Based on the Clustering Method of Artificial Neural Networks

Tien Anh Tran

A chapter in Energy-Efficient Approaches in Industrial Applications from IntechOpen

Abstract: The international shipping transportation industry becomes gradually important in the field of national economic development. It is explained by means of an increase in a number of ships nowadays, and it also expands the operating routes on international routes including the North of America, Baltic Sea, and emission control areas (ECAs). The energy efficiency of ships is very necessary to respond to the regulations of the International Maritime Organization (IMO). Moreover, the operational parameters have a significant meaning in supervising and monitoring the engines on a ship. They completely depend on the navigation environment condition. So, selecting the optimal operational parameters' component is a target of this study. In this chapter, a study on the energy efficiency of ship by decreasing the fuel consumption of the main engine for a certain ship namely M/V NSU JUSTICE 250,000 DWT of VINIC shipping transportation company in Vietnam is by the method of artificial neural networks (ANNs). In particular, these studies were conducted by the classification and clustering method of artificial neural networks (ANNs) based on the experimental database of M/V NSU JUSTICE 250,000 DWT. The results of this chapter will solve the energy efficiency issue on ships nowadays and contribute the aims in the next studies.

Keywords: energy efficiency; fuel consumption of engines; artificial neural networks; clustering; classification (search for similar items in EconPapers)
JEL-codes: Q56 (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:ito:pchaps:173720

DOI: 10.5772/intechopen.81734

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