IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i16p4827-d610353.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/16/4827/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/14/16/4827/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yan, Ran & Wang, Shuaian & Psaraftis, Harilaos N., 2021. "Data analytics for fuel consumption management in maritime transportation: Status and perspectives," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 155(C).
    2. Nestor Goicoechea & Luis María Abadie, 2021. "Optimal Slow Steaming Speed for Container Ships under the EU Emission Trading System," Energies, MDPI, vol. 14(22), pages 1-25, November.
    3. Jarosław Ziółkowski & Mateusz Oszczypała & Jerzy Małachowski & Joanna Szkutnik-Rogoż, 2021. "Use of Artificial Neural Networks to Predict Fuel Consumption on the Basis of Technical Parameters of Vehicles," Energies, MDPI, vol. 14(9), pages 1-23, May.
    4. Jessica Kersey & Natalie D. Popovich & Amol A. Phadke, 2022. "Rapid battery cost declines accelerate the prospects of all-electric interregional container shipping," Nature Energy, Nature, vol. 7(7), pages 664-674, July.
    5. Nguyen, Son & Fu, Xiuju & Ogawa, Daichi & Zheng, Qin, 2023. "An application-oriented testing regime and multi-ship predictive modeling for vessel fuel consumption prediction," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 177(C).
    6. Luka Vukić & Kee-hung Lai, 2022. "Acute port congestion and emissions exceedances as an impact of COVID-19 outcome: the case of San Pedro Bay ports," Journal of Shipping and Trade, Springer, vol. 7(1), pages 1-26, December.
    7. Giuseppe T. Cirella & Barbara Pawłowska, 2021. "Advancements in the Energy Sector and the Socioeconomic Development Nexus," Energies, MDPI, vol. 14(23), pages 1-5, December.
    8. Joanna Kizielewicz, 2021. "Eco-Trends in Energy Solutions on Cruise Ships," Energies, MDPI, vol. 14(13), pages 1-13, June.
    9. Douwe F. A. van der Kroft & Jeroen F. J. Pruyn, 2021. "A Study into the Availability, Costs and GHG Reduction in Drop-In Biofuels for Shipping under Different Regimes between 2020 and 2050," Sustainability, MDPI, vol. 13(17), pages 1-20, September.
    10. Dariusz Bernacki, 2021. "Assessing the Link between Vessel Size and Maritime Supply Chain Sustainable Performance," Energies, MDPI, vol. 14(11), pages 1-21, May.
    11. Yan, Ran & Yang, Dong & Wang, Tianyu & Mo, Haoyu & Wang, Shuaian, 2024. "Improving ship energy efficiency: Models, methods, and applications," Applied Energy, Elsevier, vol. 368(C).
    12. Guomin Li & Wei Li & Yinke Dou & Yigang Wei, 2022. "Antarctic Shipborne Tourism: Carbon Emission and Mitigation Path," Energies, MDPI, vol. 15(21), pages 1-17, October.
    13. Michael E. Stamatakis & Maria G. Ioannides, 2021. "State Transitions Logical Design for Hybrid Energy Generation with Renewable Energy Sources in LNG Ship," Energies, MDPI, vol. 14(22), pages 1-26, November.
    14. Manuel Woschank & Erwin Rauch & Helmut Zsifkovits, 2020. "A Review of Further Directions for Artificial Intelligence, Machine Learning, and Deep Learning in Smart Logistics," Sustainability, MDPI, vol. 12(9), pages 1-23, May.
    15. Børge Heggen Johansen & Dina Margrethe Aspen & Magnus Sparrevik & Vilmar Æsøy, 2021. "Applying System-Oriented Sustainability Scoring for Cruise Traffic Port Operators: A Case Study of Geiranger, Norway," Sustainability, MDPI, vol. 13(11), pages 1-15, May.
    16. Juhyang Lee & Jeongon Eom & Jumi Park & Jisung Jo & Sewon Kim, 2024. "The Development of a Machine Learning-Based Carbon Emission Prediction Method for a Multi-Fuel-Propelled Smart Ship by Using Onboard Measurement Data," Sustainability, MDPI, vol. 16(6), pages 1-22, March.
    17. Simonsen, Morten & Gössling, Stefan & Walnum, Hans Jakob, 2019. "Cruise ship emissions in Norwegian waters: A geographical analysis," Journal of Transport Geography, Elsevier, vol. 78(C), pages 87-97.
    18. Dariusz Bernacki & Christian Lis, 2021. "Investigating the Sustainable Impact of Seaport Infrastructure Provision on Maritime Component of Supply Chain," Energies, MDPI, vol. 14(12), pages 1-22, June.
    19. Philip Cammin & Jingjing Yu & Stefan Voß, 2023. "Tiered prediction models for port vessel emissions inventories," Flexible Services and Manufacturing Journal, Springer, vol. 35(1), pages 142-169, March.
    20. Andrzej Łebkowski, 2018. "Reduction of Fuel Consumption and Pollution Emissions in Inland Water Transport by Application of Hybrid Powertrain," Energies, MDPI, vol. 11(8), pages 1-16, July.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:14:y:2021:i:16:p:4827-:d:610353. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.