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Energy-Efficient Optimization of Jaw-Type Blowout Preventer Activation Using Combined Experimental Design and Metaheuristic Algorithms

Author

Listed:
  • Milan Marković

    (Technical Faculty “Mihajlo Pupin”, University of Novi Sad, Djure Djakovića bb, 23000 Zrenjanin, Serbia)

  • Borivoj Novaković

    (Technical Faculty “Mihajlo Pupin”, University of Novi Sad, Djure Djakovića bb, 23000 Zrenjanin, Serbia)

  • Mića Đurđev

    (Technical Faculty “Mihajlo Pupin”, University of Novi Sad, Djure Djakovića bb, 23000 Zrenjanin, Serbia)

  • Saša Jovanović

    (Faculty of Technical Sciences, University of Priština in Kosovska Mitrovica, Kneza Miloša St. 7, 38220 Kosovska Mitrovica, Serbia)

  • Eleonora Desnica

    (Technical Faculty “Mihajlo Pupin”, University of Novi Sad, Djure Djakovića bb, 23000 Zrenjanin, Serbia)

  • Marko Blažić

    (Technical Faculty “Mihajlo Pupin”, University of Novi Sad, Djure Djakovića bb, 23000 Zrenjanin, Serbia)

  • Jasna Tolmač

    (Technical Faculty “Mihajlo Pupin”, University of Novi Sad, Djure Djakovića bb, 23000 Zrenjanin, Serbia)

Abstract

This paper presents the optimization of the power required to activate a jaw-type blowout preventer (BOP) in the oil industry using an axial piston pump. Experimental and numerical methods were combined to analyze the effects of pressure, flow rate, volumetric efficiency, and clearance leakage on energy consumption. Taguchi methodology with an orthogonal array and the “smaller-is-better” criterion was used in the experiments, while regression analysis provided a predictive model. Optimization was performed using the Grey Wolf Optimizer (GWO) in Python 3.13. The results show that pressure and flow rate significantly affect power consumption, while higher volumetric efficiency leads to notable energy savings. The optimal configuration reduced the power demand to 5.0001 kW. Based on this, reliability models were created to assess deviations from optimal conditions. The study demonstrates the effectiveness of combining statistical and optimization techniques for improving safety systems in the oil industry. The key contribution of this study lies in the integration of experimental Taguchi-based modeling with Grey Wolf Optimizer (GWO) metaheuristic techniques to optimize the energy-efficient activation of jaw-type blowout preventers, representing a novel methodological approach in the field of hydraulic safety systems in the oil industry.

Suggested Citation

  • Milan Marković & Borivoj Novaković & Mića Đurđev & Saša Jovanović & Eleonora Desnica & Marko Blažić & Jasna Tolmač, 2025. "Energy-Efficient Optimization of Jaw-Type Blowout Preventer Activation Using Combined Experimental Design and Metaheuristic Algorithms," Energies, MDPI, vol. 18(18), pages 1-23, September.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:18:p:4852-:d:1747858
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    References listed on IDEAS

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    1. Hemmat Esfe, Mohammad & Kamyab, Mohammad Hassan & Afrand, Masoud & Amiri, Mahmoud Kiannejad, 2018. "Using artificial neural network for investigating of concurrent effects of multi-walled carbon nanotubes and alumina nanoparticles on the viscosity of 10W-40 engine oil," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 510(C), pages 610-624.
    2. Evren Ozbayoglu & Murat Ozbayoglu & Baris Guney Ozdilli & Oney Erge, 2021. "Optimization of Flow Rate and Pipe Rotation Speed Considering Effective Cuttings Transport Using Data-Driven Models," Energies, MDPI, vol. 14(5), pages 1-32, March.
    3. Piotr Patrosz, 2021. "Influence of Properties of Hydraulic Fluid on Pressure Peaks in Axial Piston Pumps’ Chambers," Energies, MDPI, vol. 14(13), pages 1-23, June.
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