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Comparative analyses of intelligent scheduling optimization algorithms for the control schemes of water injection pumps on offshore crude oil production platform

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  • Yang, Yuhang
  • Zhao, Ruijie
  • Zhang, Desheng
  • Wang, Xikun

Abstract

To address the inefficiency of long-term operation of water injection pumps on the offshore crude oil production platform and to achieve energy savings and emission reductions, this study conducts a comparative analysis of two types of optimization algorithms for optimizing the pump scheduling schemes of the water injection piping networks. One is genetic algorithm (GA), which is a heuristic algorithm, and the other is deep Q-network (DQN) algorithm, which is an artificial intelligent algorithm. Through computational analysis, both algorithms exhibit strong robustness. When disregarding the differences in motor energy efficiency, compared to manual control method, GA demonstrates an average energy-saving rate of 7.08 %, while DQN algorithm achieves 5.96 %. Both algorithms significantly enhance the stability of water injection piping networks. Although a few negative optimizations are found in the DQN algorithm, the optimized solutions are still at the boundary of the feasible domain and has no obvious effect on the stability of the system. DQN algorithm can quickly generate viable solutions regardless of problem size, the time taken is significantly less than that of the GA.

Suggested Citation

  • Yang, Yuhang & Zhao, Ruijie & Zhang, Desheng & Wang, Xikun, 2025. "Comparative analyses of intelligent scheduling optimization algorithms for the control schemes of water injection pumps on offshore crude oil production platform," Energy, Elsevier, vol. 328(C).
    • Handle: RePEc:eee:energy:v:328:y:2025:i:c:s0360544225022625
    DOI: 10.1016/j.energy.2025.136620
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    References listed on IDEAS

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    1. Chen, Pengzhan & Liu, Mengchao & Chen, Chuanxi & Shang, Xin, 2019. "A battery management strategy in microgrid for personalized customer requirements," Energy, Elsevier, vol. 189(C).
    2. Yihong Guan & Yangyang Chu & Mou Lv & Shuyan Li & Hang Li & Shen Dong & Yanbo Su, 2023. "Application of Strength Pareto Evolutionary Algorithm II in Multi-Objective Water Supply Optimization Model Design for Mountainous Complex Terrain," Sustainability, MDPI, vol. 15(15), pages 1-20, August.
    3. O’Malley, Cormac & de Mars, Patrick & Badesa, Luis & Strbac, Goran, 2023. "Reinforcement learning and mixed-integer programming for power plant scheduling in low carbon systems: Comparison and hybridisation," Applied Energy, Elsevier, vol. 349(C).
    4. J. Yazdi, 2016. "Decomposition based Multi Objective Evolutionary Algorithms for Design of Large-Scale Water Distribution Networks," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(8), pages 2749-2766, June.
    5. Zhang, Zijun & Zeng, Yaohui & Kusiak, Andrew, 2012. "Minimizing pump energy in a wastewater processing plant," Energy, Elsevier, vol. 47(1), pages 505-514.
    6. Shao, Yu & Zhou, Xinhong & Yu, Tingchao & Zhang, Tuqiao & Chu, Shipeng, 2024. "Pump scheduling optimization in water distribution system based on mixed integer linear programming," European Journal of Operational Research, Elsevier, vol. 313(3), pages 1140-1151.
    7. Volodymyr Mnih & Koray Kavukcuoglu & David Silver & Andrei A. Rusu & Joel Veness & Marc G. Bellemare & Alex Graves & Martin Riedmiller & Andreas K. Fidjeland & Georg Ostrovski & Stig Petersen & Charle, 2015. "Human-level control through deep reinforcement learning," Nature, Nature, vol. 518(7540), pages 529-533, February.
    8. Olszewski, Pawel, 2016. "Genetic optimization and experimental verification of complex parallel pumping station with centrifugal pumps," Applied Energy, Elsevier, vol. 178(C), pages 527-539.
    9. Hong, Sung-Pil & Kim, Taegyoon & Lee, Subin, 2019. "A precision pump schedule optimization for the water supply networks with small buffers," Omega, Elsevier, vol. 82(C), pages 24-37.
    10. Yasaman Makaremi & Ali Haghighi & Hamid Reza Ghafouri, 2017. "Optimization of Pump Scheduling Program in Water Supply Systems Using a Self-Adaptive NSGA-II; a Review of Theory to Real Application," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(4), pages 1283-1304, March.
    11. Hieninger, Thomas & Schmidt-Vollus, Ronald & Schlücker, Eberhard, 2021. "Improving energy efficiency of individual centrifugal pump systems using model-free and on-line optimization methods," Applied Energy, Elsevier, vol. 304(C).
    12. Correa-Jullian, Camila & López Droguett, Enrique & Cardemil, José Miguel, 2020. "Operation scheduling in a solar thermal system: A reinforcement learning-based framework," Applied Energy, Elsevier, vol. 268(C).
    Full references (including those not matched with items on IDEAS)

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