An efficient algorithm to improve oil-gas pipelines path

  • Authors

    • Nabeel Naeem Hasan Almaalei Department of Mathematics and Statistics, Faculty of Applied Sciences and Technology, Universiti Tun Hussein Onn Malaysia
    • Siti Noor Asyikin Mohd Razali
    • Nayef Abdulwahab Mohammed Alduais
    2019-04-03
    https://doi.org/10.14419/ijet.v7i4.22201
  • Shortest Path Algorithm, Ant Colony Optimization, and Oil-Gas Assembly Pipeline.

  • Abstract

    Oil-gas pipeline is a complex and high-cost system in terms of materials, construction, maintenance, control, and monitoring in which it involves environmental, economic and social risk. In the case study of Iraq, this system of pipelines is above the ground and is liable to accidents that may cause environmental disaster, loss of life and money. Therefore, the aim of this study is to propose a new algorithm to obtain the shortest path connecting oil-gas wells and addressing obstacles that may appear on the path connecting any two wells. In order to show the efficiency of the proposed algorithm, comparison between ant colony optimization (ACO) algorithm and a real current method of linking is used for this purpose. Result shows that the new proposed algorithm outperformed the other methods with higher reduction in operational cost by 16.4% for a number of 50 wells. In addition, the shortest path of connecting oil-gas wells are able to overcome all the addressed obstacles in the Rumaila north field, which is located in the city of Basra in southern Iraq.

     

     

     

  • References

    1. [1] Bast H, Funke S, Sanders P, and Schultes D (2007), Fast routing in road networks with transit nodes. Science , 316 (5824), 566. https://doi.org/10.1126/science.1137521.

      [2] Chan T (2010), More algorithms for all-pairs shortest paths in weighted graphs. SIAM J. Comput. 39(5), 590-598. https://doi.org/10.1137/08071990X.

      [3] Maniezzo V (1996), Ant System: Optimization by a Colony of Cooperating Agents. IEEE Trans. Syst. Man Cybern. B 26(1), 1–13.

      [4] Eberhart R and Kennedy J (1995), A new optimizer using particle swarm theory. MHS’95. Proc. Sixth Int. Symp. Micro Mach. Hum. Sci., 39–43. https://doi.org/10.1109/MHS.1995.494215.

      [5] Goldberg DE and Holland JH (1988), Genetic Algorithms and Machine Learning. Mach. Learn. 3(2), 95–99. https://doi.org/10.1023/A:1022602019183.

      [6] Pillac V, Gendreau M, Guéret C and Medaglia AL (2011), A Review of Dynamic Vehicle Routing Problems. Cirrelt-2011-62, 225(1), 0–28.

      [7] Dorigo M, Maniezzo V and Colorni A (1991), Positive feedback as a search strategy. Tech. Rep. no. 91-016.

      [8] Vitekar KN (2013), Review of Solving Software Project Scheduling Problem with Ant Colony Optimization, IJAREEIE 2(4), 1177–1182.

      [9] Yu-Hsin Chen G (2013), A new data structure of solution representation in hybrid ant colony optimization for large dynamic facility layout problems. Int. J. Prod. Econ. 142(2), 362–371. https://doi.org/10.1016/j.ijpe.2012.12.012.

      [10] Kuo RJ, Zulvia FE and Suryadi K (2012), Hybrid particle swarm optimization with genetic algorithm for solving capacitated vehicle routing problem with fuzzy demand – A case study on garbage collection system. Appl. Math. Comput. 219(5), 2574–2588. https://doi.org/10.1016/j.amc.2012.08.092.

      [11] Tsuji Y, Kuroda M, Kitagawa Y and Imoto Y (2012), Ant Colony Optimization approach for solving rolling stock planning for passenger trains, IEEE/SICE Int. Symp. Syst. Integr., 716–721.

      [12] Aimoerfu, Shi M, Li C, Wang D and Hairihan (2017), Implementation of the protein sequence model based on ant colony optimization algorithm. IEEE/ACIS 16th Int. Conf. Comput. Inf. Sci., 661–665. https://doi.org/10.1109/ICIS.2017.7960075.

      [13] Suresh LP, Dash SS and Panigrahi BK (2015), Artificial Intelligence and Evolutionary Algorithms in Engineering Systems. Adv. Intell. Syst. Comput. 325, 275–284.

      [14] Anitha Rao and Sandeep Kumar Hegde (2015), Literature Survey On Travelling Salesman Problem Using Genetic Algorithms. Int. J. Adv. Res. Eduation Technol. 2(1), 4.

      [15] Salama KM.and Freitas AA (2013), Learning Bayesian network classifiers using ant colony optimization. Swarm Intell. 7(2–3), 229–254. https://doi.org/10.1007/s11721-013-0087-6.

      [16] Orlin JB, Madduri K, Subramani K, and Williamson M (2010), A faster algorithm for the single source shortest path problem with few distinct positive lengths. J. Discret. Algorithms 8(2), 189–198. https://doi.org/10.1016/j.jda.2009.03.001.

      [17] Williams VV (2010), Nondecreasing paths in a weighted graph. ACM Trans. Algorithms 6(4), 1–24. https://doi.org/10.1145/1824777.1824790.

      [18] Carter RGG, Gablonsky JM, Patrick A, Kelley CT and Eslinger OJ (2001), Algorithms for Noisy Problems in Gas Transmission Pipeline Optimization. Optim. Eng. 2(2), 139–157. https://doi.org/10.1023/A:1013123110266.

      [19] A. Cano-Acosta, J. Fontecha, N. Velasco, and F. Muñoz-Giraldo, “Shortest path algorithm for optimal sectioning of hydrocarbon transport pipeline,†IFAC-PapersOnLine, vol. 49, no. 12, pp. 532–537, 2016.

      [20] Cano-Acosta A, Fontecha J, Velasco N and Muñoz-Giraldo F (2016), Shortest path algorithm for optimal sectioning of hydrocarbon transport pipeline. IFAC-Papers OnLine 49(12), 532–537. https://doi.org/10.1016/j.ifacol.2016.07.686.

      [21] Xu MH (2007), An improved Dijkstra ’ s shortest path algorithm for sparse network q. Appl. Math. Comput. 185(1), 247–254. https://doi.org/10.1016/j.amc.2006.06.094.

      [22] Chu F and Chen S (2012), Optimal Design of Pipeline Based on the Shortest Path, Phys. Procedia 33, 216–220. https://doi.org/10.1016/j.phpro.2012.05.054.

      [23] Kang JY and Lee BS (2017), Optimisation of pipeline route in the presence of obstacles based on a least cost path algorithm and laplacian smoothing. Int. J. Nav. Archit. Ocean Eng. 9(5), 492–498. .https://doi.org/10.1016/j.ijnaoe.2017.02.001.

      [24] Su H, Zhang J, Zio E, Yang N, Li X and Zhang Z (2018), An integrated systemic method for supply reliability assessment of natural gas pipeline networks. Appl. Energy 209 (May 2017), 489–501. https://doi.org/10.1016/j.apenergy.2017.10.108.

      [25] Toksari MD (2016), A hybrid algorithm of Ant Colony Optimization (ACO) and Iterated Local Search (ILS) for estimating electricity domestic consumption: Case of Turkey. Int. J. Electr. Power Energy Syst. 78, 776–7. https://doi.org/10.1016/j.ijepes.2015.12.032.

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  • How to Cite

    Naeem Hasan Almaalei, N., Noor Asyikin Mohd Razali, S., & Abdulwahab Mohammed Alduais, N. (2019). An efficient algorithm to improve oil-gas pipelines path. International Journal of Engineering & Technology, 7(4), 5412-5418. https://doi.org/10.14419/ijet.v7i4.22201

    Received date: 2018-11-29

    Accepted date: 2019-01-09

    Published date: 2019-04-03