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A hybrid computational approach for detailed scheduling of products in a pipeline with multiple pump stations

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  • Zhang, Haoran
  • Liang, Yongtu
  • Liao, Qi
  • Wu, Mengyu
  • Yan, Xiaohan

Abstract

Multi-product pipeline is the most effective mode for refined products transportation and is of vital importance in the energy supply chain. The essential task in actual pipeline operation is scheduling delivery and injection of numerous kinds of products. Despite much research was done on products pipeline scheduling issue, little of them focused on multiple pump stations which are significant for long-distance pipelines. The paper establishes a mixed-integer nonlinear programming model (MINLP) for products pipeline with single source and multiple pump stations. The model has taken factors such as batch migration, local electricity price, demand time window, avoidance of idle segment, change of minimum flow rate and nonlinear hydraulic related objective function into consideration. The model contains two parts and is solved by a hybrid computational approach, the ant colony optimization algorithm (ACO) and the simplex method (SM). Finally, the formulation is successfully applied to a virtual and a real-world pipeline to verify the stability, convergence and practicability.

Suggested Citation

  • Zhang, Haoran & Liang, Yongtu & Liao, Qi & Wu, Mengyu & Yan, Xiaohan, 2017. "A hybrid computational approach for detailed scheduling of products in a pipeline with multiple pump stations," Energy, Elsevier, vol. 119(C), pages 612-628.
    • Handle: RePEc:eee:energy:v:119:y:2017:i:c:p:612-628
    DOI: 10.1016/j.energy.2016.11.027
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    References listed on IDEAS

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    2. Mostafaei, Hossein & Castro, Pedro M. & Oliveira, Fabricio & Harjunkoski, Iiro, 2021. "Efficient formulation for transportation scheduling of single refinery multiproduct pipelines," European Journal of Operational Research, Elsevier, vol. 293(2), pages 731-747.
    3. Li, Zhengbing & Feng, Huixia & Liang, Yongtu & Xu, Ning & Nie, Siming & Zhang, Haoran, 2019. "A leakage risk assessment method for hazardous liquid pipeline based on Markov chain Monte Carlo," International Journal of Critical Infrastructure Protection, Elsevier, vol. 27(C).
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    6. Mostafaei, Hossein & Castro, Pedro M. & Relvas, Susana & Harjunkoski, Iiro, 2021. "A holistic MILP model for scheduling and inventory management of a multiproduct oil distribution system," Omega, Elsevier, vol. 98(C).
    7. Chen, Haihong & Zuo, Lili & Wu, Changchun & Li, Qingping, 2019. "An MILP formulation for optimizing detailed schedules of a multiproduct pipeline network," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 123(C), pages 142-164.
    8. Neda Beheshti Asl & S. A. MirHassani & S. Relvas & F. Hooshmand, 2022. "A novel two-phase decomposition-based algorithm to solve MINLP pipeline scheduling problem," Operational Research, Springer, vol. 22(5), pages 4829-4863, November.
    9. Long, Yin & Yoshida, Yoshikuni & Fang, Kai & Zhang, Haoran & Dhondt, Maya, 2019. "City-level household carbon footprint from purchaser point of view by a modified input-output model," Applied Energy, Elsevier, vol. 236(C), pages 379-387.
    10. Wu, Yan & Xia, Tianqi & Wang, Yufei & Zhang, Haoran & Feng, Xiao & Song, Xuan & Shibasaki, Ryosuke, 2022. "A synchronization methodology for 3D offshore wind farm layout optimization with multi-type wind turbines and obstacle-avoiding cable network," Renewable Energy, Elsevier, vol. 185(C), pages 302-320.
    11. Fan, Mu-wei & Ao, Chu-chu & Wang, Xiao-rong, 2019. "Comprehensive method of natural gas pipeline efficiency evaluation based on energy and big data analysis," Energy, Elsevier, vol. 188(C).
    12. Mehrnoosh Taherkhani, 2020. "An MILP approach for scheduling of tree-like pipelines with dual purpose terminals," Operational Research, Springer, vol. 20(4), pages 2133-2161, December.
    13. Yuan, Qing & Gao, Yuyao & Luo, Yiyang & Chen, Yujie & Wang, Bohong & Wei, Jinjia & Yu, Bo, 2023. "Study on the optimal operation scheme of a heated oil pipeline system under complex industrial conditions," Energy, Elsevier, vol. 272(C).
    14. Zheng, Jianqin & Wang, Chang & Liang, Yongtu & Liao, Qi & Li, Zhuochao & Wang, Bohong, 2022. "Deeppipe: A deep-learning method for anomaly detection of multi-product pipelines," Energy, Elsevier, vol. 259(C).

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