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

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  • Tomasz Cepowski

    (Faculty of Navigation, Maritime University of Szczecin, ul. Wały Chrobrego 1-2, 70-500 Szczecin, Poland)

  • Paweł Chorab

    (Faculty of Navigation, Maritime University of Szczecin, ul. Wały Chrobrego 1-2, 70-500 Szczecin, Poland)

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 CO 2 emissions for a bulk carrier, tanker, and container carrier utilizing the proposed ANNs.

Suggested Citation

  • Tomasz Cepowski & Paweł Chorab, 2021. "The Use of Artificial Neural Networks to Determine the Engine Power and Fuel Consumption of Modern Bulk Carriers, Tankers and Container Ships," Energies, MDPI, vol. 14(16), pages 1-26, August.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:16:p:4827-:d:610353
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    References listed on IDEAS

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    1. Luan Thanh Le & Gunwoo Lee & Keun-Sik Park & Hwayoung Kim, 2020. "Neural network-based fuel consumption estimation for container ships in Korea," Maritime Policy & Management, Taylor & Francis Journals, vol. 47(5), pages 615-632, July.
    2. Morten Simonsen & Hans Jakob Walnum & Stefan Gössling, 2018. "Model for Estimation of Fuel Consumption of Cruise Ships," Energies, MDPI, vol. 11(5), pages 1-29, April.
    3. Ernest Czermański & Giuseppe T. Cirella & Aneta Oniszczuk-Jastrząbek & Barbara Pawłowska & Theo Notteboom, 2021. "An Energy Consumption Approach to Estimate Air Emission Reductions in Container Shipping," Energies, MDPI, vol. 14(2), pages 1-18, January.
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    Cited by:

    1. Tadeusz Szelangiewicz & Katarzyna Żelazny, 2023. "Reducing CO 2 Emissions during the Operation of Unmanned Transport Vessels with Diesel Engines," Energies, MDPI, vol. 16(12), pages 1-21, June.
    2. Armin Norouzi & Hamed Heidarifar & Mahdi Shahbakhti & Charles Robert Koch & Hoseinali Borhan, 2021. "Model Predictive Control of Internal Combustion Engines: A Review and Future Directions," Energies, MDPI, vol. 14(19), pages 1-40, October.

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