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LNG Logistics Model to Meet Demand for Bunker Fuel

Energies (Basel), 2024-04, Vol.17 (7), p.1758 [Peer Reviewed Journal]

COPYRIGHT 2024 MDPI AG ;2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 1996-1073 ;EISSN: 1996-1073 ;DOI: 10.3390/en17071758

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  • Title:
    LNG Logistics Model to Meet Demand for Bunker Fuel
  • Author: Orysiak, Ewelina ; Zielski, Hubert ; Gawle, Mateusz
  • Subjects: Air quality management ; Carbon dioxide ; Consumption ; Crude oil ; Emissions ; Energy policy ; energy security ; Infrastructure ; Liquefied natural gas ; Nitrogen ; Ports ; Shipping industry ; Sulfur ; Supply and demand ; traffic modelling ; traffic optimisation ; transport infrastructure ; transportation safety
  • Is Part Of: Energies (Basel), 2024-04, Vol.17 (7), p.1758
  • Description: The main objective of this manuscript is to build a model for the distribution of LNG as a marine fuel in the southern Baltic Sea based on a genetic algorithm in terms of cost. In order to achieve this, it was necessary to develop, in detail, research sub-objectives like analysis of the intensity of ship traffic in the indicated area and analysis of LNG demand in maritime transport. In the first part of this study, the authors use data from the IALA IWRAP Mk2 and the Statistical Office in Szczecin to analyse the marine traffic density (by type of vessel) in the southern part of the Baltic Sea. LNG used as marine fuel reduces toxic emissions into the atmosphere. The authors specify the LNG fleet size and locations of LNG storage facilities in a way to ensure that the defined LNG bunker vessels can supply fuel to LNG-powered vessels within the shortest possible time period. The database contains a set of traits necessary to determine the optimal demand for LNG. The traits were developed based on an existing LNG fleet and appropriately selected infrastructure, and they represent existing LNG-powered vessels as well as LNG bunker vessels and their specifications. Based on the created LNG distribution model, were performed in Matlab R2019a software. An LNG distribution model was developed, which uses a genetic algorithm to solve the task. The demand for LNG for the sea area under analysis was determined based on data on the capacity of LNG-powered vessels (by type of vessel) and their distance from the specified port.
  • Publisher: Basel: MDPI AG
  • Language: English
  • Identifier: ISSN: 1996-1073
    EISSN: 1996-1073
    DOI: 10.3390/en17071758
  • Source: GFMER Free Medical Journals
    ROAD: Directory of Open Access Scholarly Resources
    ProQuest Central
    DOAJ Directory of Open Access Journals
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