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Small-scale LNG Market Optimization – Intelligent Distribution Network

Edyta Kuk orcid.org/0000-0003-3298-7359 ; AGH University of Science and Technology, Poland
Bartłomiej Małkus ; AGH University of Science and Technology, Poland
Michał Kuk ; AGH University of Science and Technology, Poland

Abstract

Intelligent Systems, thanks to their effectiveness and robustness, find many applications in various industries. One of such applications is optimization of distribution network of small-scale LNG market, which was highly dynamic throughout last years. LNG (Liquified Natural Gas) is a fuel produced from natural gas, but its volume is approx. 600 times smaller than in the gas (natural) state, which makes it more economically effective to transport and store. Distribution network consists of several pickup points (varying in LNG specification) and a number of destination points (varying in tanks capacities). From economic point of view, optimization of LNG truck tanks paths is an important factor in whole market development. The optimization process involves selecting a pickup point and a sequence of destination points with amount of LNG unloaded in each of them. Solution proposed in this paper is based on graph theory and advanced machine learning methods, such as reinforcement learning, recurrent neural networks and online learning. Optimization of distribution network translates directly into a number of economic benefits: reduction of LNG transport cost, shortening the delivery time, reduction of distribution costs and increase in the effectiveness of tank truck usage.

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Keywords

Hrčak ID:

250979

URI

https://hrcak.srce.hr/250979

Publication date:

22.9.2020.

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