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An integrated methodology for the supply reliability analysis of multi-product pipeline systems under pumps failure

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  • Zhou, Xingyuan
  • van Gelder, P.H.A.J.M.
  • Liang, Yongtu
  • Zhang, Haoran

Abstract

As the main way for the long-distance transportation of refined products, multi-products pipelines are of vital importance to the regional energy security. The supply reliability evaluation of multi-product pipeline systems can improve the effective response to unexpected disruptions and guarantee the reliable oil supply. Based on reliability theory and pipeline scheduling method, an integrated supply reliability evaluation methodology for multi-product pipeline systems is proposed in this paper and the pumps failure, of which influence is the most complex, is focused on. In the methodology, the discrete-time Markov process is adopted to describe the stochastic failure and the Monte Carlo method is used to simulate the system states transition. With the pipeline flowrate upper limits under various pumps failure scenarios optimized in advance, the maximum supply capacity to the downstream markets in each trial is calculated by the pipeline scheduling model. Three indicators are also developed to analyze the pipeline supply reliability from the holistic and individual perspectives. At last, the methodology application is performed on a real-world multi-product pipeline system in China and the supply reliability is analyzed in detail according to the simulation results. It is proved to provide a practical method for the emergency response decision-making and loss prevention.

Suggested Citation

  • Zhou, Xingyuan & van Gelder, P.H.A.J.M. & Liang, Yongtu & Zhang, Haoran, 2020. "An integrated methodology for the supply reliability analysis of multi-product pipeline systems under pumps failure," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    • Handle: RePEc:eee:reensy:v:204:y:2020:i:c:s0951832020306864
    DOI: 10.1016/j.ress.2020.107185
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    2. Wang, Chang & Zheng, Jianqin & Liang, Yongtu & Wang, Bohong & Klemeš, Jiří Jaromír & Zhu, Zhu & Liao, Qi, 2022. "Deeppipe: An intelligent monitoring framework for operating condition of multi-product pipelines," Energy, Elsevier, vol. 261(PB).
    3. Fan, Lin & Su, Huai & Wang, Wei & Zio, Enrico & Zhang, Li & Yang, Zhaoming & Peng, Shiliang & Yu, Weichao & Zuo, Lili & Zhang, Jinjun, 2022. "A systematic method for the optimization of gas supply reliability in natural gas pipeline network based on Bayesian networks and deep reinforcement learning," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    4. Hassan, Shamsu & Wang, Jin & Kontovas, Christos & Bashir, Musa, 2022. "An assessment of causes and failure likelihood of cross-country pipelines under uncertainty using bayesian networks," Reliability Engineering and System Safety, Elsevier, vol. 218(PA).

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