IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i22p15802-d1277362.html
   My bibliography  Save this article

Impact of Port Shallowness (Clearance under the Ship’s Keel) on Shipping Safety, Energy Consumption and Sustainability of Green Ports

Author

Listed:
  • Vytautas Paulauskas

    (Marine Engineering Department, Klaipeda University, H. Manto 84, LT-92219 Klaipeda, Lithuania)

  • Viktoras Senčila

    (Marine Engineering Department, Klaipeda University, H. Manto 84, LT-92219 Klaipeda, Lithuania)

  • Donatas Paulauskas

    (Marine Engineering Department, Klaipeda University, H. Manto 84, LT-92219 Klaipeda, Lithuania)

  • Martynas Simutis

    (Marine Engineering Department, Klaipeda University, H. Manto 84, LT-92219 Klaipeda, Lithuania)

Abstract

In a majority of ports, a ship’s speed is limited for reasons of navigational safety. At the same time, captains and port pilots choose the speed of the ship, but it cannot be higher than the speed allowed in the port. Therefore, the speed of the ship also depends on the experience of the masters and harbor pilots and the sailing conditions in specific situations. Choosing the optimal speed of the ship in the port, considering the hydrodynamic effect of shallow water and the controllability of the ship, can help reduce fuel consumption and ship emissions, which is important for the development of a sustainable port. In all cases, the safety of the shipping is the highest priority. The main objectives of this article are determining the optimal speed of ships in ports with low clearance under a ship’s hull, ensuring navigational safety, reducing fuel consumption and emissions, and creating a sustainable port. This article presents the methodology for calculating the optimal ship speed as the minimum controllable speed, fuel consumption and emission reduction, as well as its implications for sustainable and green maritime transport and port development. The methodology presented has been tested on real ships and using a calibrated simulator, navigating through port channels and port water’s restricted conditions.

Suggested Citation

  • Vytautas Paulauskas & Viktoras Senčila & Donatas Paulauskas & Martynas Simutis, 2023. "Impact of Port Shallowness (Clearance under the Ship’s Keel) on Shipping Safety, Energy Consumption and Sustainability of Green Ports," Sustainability, MDPI, vol. 15(22), pages 1-20, November.
  • Handle: RePEc:gam:jsusta:v:15:y:2023:i:22:p:15802-:d:1277362
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/22/15802/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/15/22/15802/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Bing Wu & Xinping Yan & Yang Wang & C. Guedes Soares, 2017. "An Evidential Reasoning‐Based CREAM to Human Reliability Analysis in Maritime Accident Process," Risk Analysis, John Wiley & Sons, vol. 37(10), pages 1936-1957, October.
    2. Bye, Rolf J. & Aalberg, Asbjørn L., 2018. "Maritime navigation accidents and risk indicators: An exploratory statistical analysis using AIS data and accident reports," Reliability Engineering and System Safety, Elsevier, vol. 176(C), pages 174-186.
    Full references (including those not matched with items on IDEAS)

    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. Vytautas Paulauskas & Donatas Paulauskas, 2024. "Dependence of Ships Turning at Port Turning Basins on Clearance under the Ship’s Keel," Sustainability, MDPI, vol. 16(7), pages 1-22, March.
    2. Shaoqi Jiang & Weijiong Chen & Yutao Kang & Jiahao Liu & Wanglai Kuang, 2021. "Identifying Cognitive Mechanism Underlying Situation Awareness of Pilots’ Unsafe Behaviors Using Quantitative Modeling," IJERPH, MDPI, vol. 18(6), pages 1-17, March.
    3. Montewka, Jakub & Manderbacka, Teemu & Ruponen, Pekka & Tompuri, Markus & Gil, Mateusz & Hirdaris, Spyros, 2022. "Accident susceptibility index for a passenger ship-a framework and case study," Reliability Engineering and System Safety, Elsevier, vol. 218(PA).
    4. Yu, Qing & Teixeira, Ângelo Palos & Liu, Kezhong & Rong, Hao & Guedes Soares, Carlos, 2021. "An integrated dynamic ship risk model based on Bayesian Networks and Evidential Reasoning," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    5. Wu, Bing & Yip, Tsz Leung & Yan, Xinping & Guedes Soares, C., 2022. "Review of techniques and challenges of human and organizational factors analysis in maritime transportation," Reliability Engineering and System Safety, Elsevier, vol. 219(C).
    6. Sezer, Sukru Ilke & Akyuz, Emre & Arslan, Ozcan, 2022. "An extended HEART Dempster–Shafer evidence theory approach to assess human reliability for the gas freeing process on chemical tankers," Reliability Engineering and System Safety, Elsevier, vol. 220(C).
    7. Yuga Raju Gunda & Suprakash Gupta & Lalit Kumar Singh, 2023. "Assessing human performance and human reliability: a review," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 14(3), pages 817-828, June.
    8. Wang, Lei & Liu, Qing & Dong, Shiyu & Guedes Soares, C., 2022. "Selection of countermeasure portfolio for shipping safety with consideration of investment risk aversion," Reliability Engineering and System Safety, Elsevier, vol. 219(C).
    9. Zhang, Yang & Sun, Xukai & Chen, Jihong & Cheng, Cheng, 2021. "Spatial patterns and characteristics of global maritime accidents," Reliability Engineering and System Safety, Elsevier, vol. 206(C).
    10. Zhou, Yusheng & Li, Xue & Yuen, Kum Fai, 2022. "Holistic risk assessment of container shipping service based on Bayesian Network Modelling," Reliability Engineering and System Safety, Elsevier, vol. 220(C).
    11. Cai, Mingyou & Zhang, Jinfen & Zhang, Di & Yuan, Xiaoli & Soares, C. Guedes, 2021. "Collision risk analysis on ferry ships in Jiangsu Section of the Yangtze River based on AIS data," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    12. Wang, Likun & Yang, Zaili, 2018. "Bayesian network modelling and analysis of accident severity in waterborne transportation: A case study in China," Reliability Engineering and System Safety, Elsevier, vol. 180(C), pages 277-289.
    13. Bing Wu & Huibin Tian & Xinping Yan & C. Guedes Soares, 2020. "A probabilistic consequence estimation model for collision accidents in the downstream of Yangtze River using Bayesian Networks," Journal of Risk and Reliability, , vol. 234(2), pages 422-436, April.
    14. Adland, Roar & Jia, Haiying & Lode, Tønnes & Skontorp, Jørgen, 2021. "The value of meteorological data in marine risk assessment," Reliability Engineering and System Safety, Elsevier, vol. 209(C).
    15. Abrishami, Shokoufeh & Khakzad, Nima & Hosseini, Seyed Mahmoud, 2020. "A data-based comparison of BN-HRA models in assessing human error probability: An offshore evacuation case study," Reliability Engineering and System Safety, Elsevier, vol. 202(C).
    16. Munim, Ziaul Haque & Sørli, Michael André & Kim, Hyungju & Alon, Ilan, 2024. "Predicting maritime accident risk using Automated Machine Learning," Reliability Engineering and System Safety, Elsevier, vol. 248(C).
    17. Lan, He & Ma, Xiaoxue & Qiao, Weiliang & Ma, Laihao, 2022. "On the causation of seafarers’ unsafe acts using grounded theory and association rule," Reliability Engineering and System Safety, Elsevier, vol. 223(C).
    18. Rong, H. & Teixeira, A.P. & Guedes Soares, C., 2022. "Maritime traffic probabilistic prediction based on ship motion pattern extraction," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    19. Du, Lei & Goerlandt, Floris & Kujala, Pentti, 2020. "Review and analysis of methods for assessing maritime waterway risk based on non-accident critical events detected from AIS data," Reliability Engineering and System Safety, Elsevier, vol. 200(C).
    20. Ziyang Ye & Yanyi Chen & Tao Wang & Baiyuan Tang & Chengpeng Wan & Hao Zhang & Bozhong Zhou, 2024. "A Two-Stage Bayesian Network Approach to Inland Waterway Navigation Risk Assessment Considering the Characteristics of Different River Segments: A Case of the Yangtze River," Sustainability, MDPI, vol. 16(20), pages 1-22, October.

    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:jsusta:v:15:y:2023:i:22:p:15802-:d:1277362. 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.