Modified dijkstra’s algorithm for oil-gas pipeline optimization problem
The optimal design of the oil-gas pipeline network is challenging due to its complexity and variety. In this study, the real data of the existing pipeline structure is used, which is currently applied in Iraq. The weakness of the original Dijkstra's algorithm is unable to solve the network...
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| Format: | Thesis |
| Language: | English English English |
| Published: |
2020
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| Subjects: | |
| Online Access: | http://eprints.uthm.edu.my/918/3/24p%20NABEEL%20NAEEM%20HASAN.pdf http://eprints.uthm.edu.my/918/2/NABEEL%20NAEEM%20HASAN%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/918/1/NABEEL%20NAEEM%20HASAN%20WATERMARK.pdf http://eprints.uthm.edu.my/918/ |
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| Institution: | Universiti Tun Hussein Onn Malaysia |
| Language: | English English English |
| Summary: | The optimal design of the oil-gas pipeline network is challenging due to its complexity
and variety. In this study, the real data of the existing pipeline structure is used, which
is currently applied in Iraq. The weakness of the original Dijkstra's algorithm is unable
to solve the network problem with a huge data size of more than 50 wells. Therefore,
this thesis aims to propose the Modify Dijkstra's Algorithm, which can obtain an
optimal solution for huge data size and avoid several possible obstacles on the path with
minimum total costs. To evaluate the effectiveness of the proposed algorithm,
comparison with ant colony optimization, genetic algorithm, original Dijkstra's
algorithm, and minimum spanning tree (MST) has been made in terms of the total cost
and total run time. In addition, a specific mathematical model has been developed to
minimize the overall distance between oil-gas wells and the central station. The results
showed that the proposed algorithm, called the Modify Dijkstra's Algorithm (MDA),
only run for less than one minute even for large data size and provides a better solution
for the 700 to 840 wells. Meanwhile, the genetic algorithm performs very well for the
data size between 100 to 600 wells but took about 20 minutes to get the best solution.
For a small data size of 10 to 50 wells, a minimum spanning tree (MST) is capable of
producing optimal solutions in less than 5 minutes. However, MST is unable to solve
more than 50 wells. This result is similar to the result obtained from the original
Dijkstra's Algorithm. Meanwhile, the ACO algorithm showed the average performance
in terms of the total cost and total run time. In conclusion, the proposed algorithm could
be considered as a fast-running algorithm and has been proven to successfully avoid the
obstacles that may appear in the path of the pipeline network. In addition, verification
of the proposed algorithm has been done through simulation data of 100 to 2000 wells,
which results in less than 2 minutes run times. Besides that, the average cost reduction
could reach up to 86% compared to the existing pipeline structure, which may assist the
Iraq government in the future development of a new discovery oil field |
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