The Use of Artificial Neural Networks to Determine the Engine Power and Fuel Consumption of Modern Bulk Carriers, Tankers and Container Ships

Abstract

The 2007–2008 financial crisis, together with rises in fuel prices and stringent pollution regulation, led to the need to update the methods concerning ship propulsion system design. In this article, a set of artificial neural networks was used to update the design equations to estimate the engine power and fuel consumption of modern tankers, bulk carriers, and container ships. Deadweight or TEU capacity and ship speed were used as the inputs for the ANNs. This study shows that even a linear ANN with two neurons in the input and output layers, with purelin activation functions, offers an accurate estimation of ship propulsion parameters. The proposed linear ANNs have simple mathematical structures and are straightforward to apply. The ANNs presented in the article were developed based on the data of the most recent ships built from 2015 to present, and could have a practical application at the preliminary design stage, in transportation or air pollution studies for modern commercial cargo ships. The presented equations mirror trends found in the literature and offer much greater accuracy for the features of new-built ships. The article shows how to estimate CO2 emissions for a bulk carrier, tanker, and container carrier utilizing the proposed ANNs.