IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i20p7453-d938397.html
   My bibliography  Save this article

Optimization of Oil Pipeline Operations to Reduce Energy Consumption Using an Improved Squirrel Search Algorithm

Author

Listed:
  • Shanbi Peng

    (School of Civil Engineering and Geomatics, Southwest Petroleum University, Chengdu 610500, China)

  • Zhe Zhang

    (China Petroleum Pipeline Engineering Corporation, Langfang 065000, China)

  • Yongqiang Ji

    (China Petroleum & Chemical Co., Ltd. of North Branch, Zhengzhou 450006, China)

  • Laimin Shi

    (Zhejiang Zheneng Natural Gas Operation Co., Ltd., Hangzhou 310051, China)

Abstract

To achieve the goal of achieving carbon-neutral by 2060, the government of China has put forward higher requirements for energy conservation and consumption reduction in the energy industry. Therefore, it is necessary to reduce energy consumption in the process of transporting oil. In this paper, an optimization model that minimizes the total energy consumption of the entire pipeline system is proposed and the squirrel search algorithm (SSA) is used to solve the optimization model. Meanwhile, to improve the performance of the SSA, two strategies are proposed. One is the adaptive inertia weight strategy, and the other is the multi-group co-evolution strategy. The adaptive inertia weight can adjust the step size of the flying squirrels according to the difference of the objective function value and multi-group co-evolution is introduced to improve population diversity. The improved SSA is named multigroup coevolution-adaptive inertia weight SSA (MASSA). A total of 20 benchmark functions are used to test the performance of MASSA, including unimodal functions and multimodal functions. Compared with the other four algorithms, MASSA has better performance and convergence capabilities. In the case study experiment, an optimization model of the oil pipeline is built, which takes the minimum energy consumption of the whole pipeline as the objective function. Compared with the actual operating conditions, the electricity consumption optimized by MASSA decreases by 399,018.94 kgce, and the thermal energy dissipation decreases by 113,759.25 kgce. The total energy consumption is reduced by 512,778.19 kgce, which is 9.62%. These results indicate that the two improvement strategies are significant, and optimizing the operating parameters can reduce energy consumption.

Suggested Citation

  • Shanbi Peng & Zhe Zhang & Yongqiang Ji & Laimin Shi, 2022. "Optimization of Oil Pipeline Operations to Reduce Energy Consumption Using an Improved Squirrel Search Algorithm," Energies, MDPI, vol. 15(20), pages 1-19, October.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:20:p:7453-:d:938397
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/20/7453/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/20/7453/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Basu, M., 2019. "Squirrel search algorithm for multi-region combined heat and power economic dispatch incorporating renewable energy sources," Energy, Elsevier, vol. 182(C), pages 296-305.
    2. Kallis, Giorgos, 2007. "When is it coevolution?," Ecological Economics, Elsevier, vol. 62(1), pages 1-6, April.
    3. 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.
    4. Karasu, Seçkin & Altan, Aytaç & Bekiros, Stelios & Ahmad, Wasim, 2020. "A new forecasting model with wrapper-based feature selection approach using multi-objective optimization technique for chaotic crude oil time series," Energy, Elsevier, vol. 212(C).
    5. Zeng, Chunlei & Wu, Changchun & Zuo, Lili & Zhang, Bin & Hu, Xingqiao, 2014. "Predicting energy consumption of multiproduct pipeline using artificial neural networks," Energy, Elsevier, vol. 66(C), pages 791-798.
    6. Wang, Yanjiao & Du, Tianlin, 2020. "A Multi-objective Improved Squirrel Search Algorithm based on Decomposition with External Population and Adaptive Weight Vectors Adjustment," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 542(C).
    7. Tongyi Zheng & Weili Luo, 2019. "An Improved Squirrel Search Algorithm for Optimization," Complexity, Hindawi, vol. 2019, pages 1-31, July.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Lei Chen & Bingjie Zhao & Yunpeng Ma, 2023. "FSSSA: A Fuzzy Squirrel Search Algorithm Based on Wide-Area Search for Numerical and Engineering Optimization Problems," Mathematics, MDPI, vol. 11(17), pages 1-42, August.
    2. Hu, Gang & Xu, Zhaoqiang & Wang, Guorong & Zeng, Bin & Liu, Yubing & Lei, Ye, 2021. "Forecasting energy consumption of long-distance oil products pipeline based on improved fruit fly optimization algorithm and support vector regression," Energy, Elsevier, vol. 224(C).
    3. Qian, Jiaxin & Wu, Jiahui & Yao, Lei & Mahmut, Saniye & Zhang, Qiang, 2021. "Comprehensive performance evaluation of Wind-Solar-CCHP system based on emergy analysis and multi-objective decision method," Energy, Elsevier, vol. 230(C).
    4. Wen, Danyan & Liu, Li & Wang, Yudong & Zhang, Yaojie, 2022. "Forecasting crude oil market returns: Enhanced moving average technical indicators," Resources Policy, Elsevier, vol. 76(C).
    5. Rajpal, Sheetal & Lakhyani, Navin & Singh, Ayush Kumar & Kohli, Rishav & Kumar, Naveen, 2021. "Using handpicked features in conjunction with ResNet-50 for improved detection of COVID-19 from chest X-ray images," Chaos, Solitons & Fractals, Elsevier, vol. 145(C).
    6. Hasankhani, Arezoo & Hakimi, Seyed Mehdi, 2021. "Stochastic energy management of smart microgrid with intermittent renewable energy resources in electricity market," Energy, Elsevier, vol. 219(C).
    7. Jia, Wenlong & Yang, Fan & Li, Changjun & Huang, Ting & Song, Shuoshuo, 2021. "A unified thermodynamic framework to compute the hydrate formation conditions of acidic gas/water/alcohol/electrolyte mixtures up to 186.2 MPa," Energy, Elsevier, vol. 230(C).
    8. Karasu, Seçkin & Altan, Aytaç, 2022. "Crude oil time series prediction model based on LSTM network with chaotic Henry gas solubility optimization," Energy, Elsevier, vol. 242(C).
    9. Zhou, Yuzhou & Zhao, Jiexing & Zhai, Qiaozhu, 2021. "100% renewable energy: A multi-stage robust scheduling approach for cascade hydropower system with wind and photovoltaic power," Applied Energy, Elsevier, vol. 301(C).
    10. Hu, Rongchun & Zhang, Dongxu & Gu, Xudong, 2022. "Reliability analysis of a class of stochastically excited nonlinear Markovian jump systems," Chaos, Solitons & Fractals, Elsevier, vol. 155(C).
    11. Daniel Coq-Huelva & Angie Higuchi & Rafaela Alfalla-Luque & Ricardo Burgos-Morán & Ruth Arias-Gutiérrez, 2017. "Co-Evolution and Bio-Social Construction: The Kichwa Agroforestry Systems ( Chakras ) in the Ecuadorian Amazonia," Sustainability, MDPI, vol. 9(10), pages 1-19, October.
    12. Saifi, Basim & Drake, Lars, 2008. "A coevolutionary model for promoting agricultural sustainability," Ecological Economics, Elsevier, vol. 65(1), pages 24-34, March.
    13. Ríos-Núñez, Sandra M. & Coq-Huelva, Daniel & García-Trujillo, Roberto, 2013. "The Spanish livestock model: A coevolutionary analysis," Ecological Economics, Elsevier, vol. 93(C), pages 342-350.
    14. Mehrpooya, Mehdi & Ansarinasab, Hojat & Mousavi, Seyed Ali, 2021. "Life cycle assessment and exergoeconomic analysis of the multi-generation system based on fuel cell for methanol, power, and heat production," Renewable Energy, Elsevier, vol. 172(C), pages 1314-1332.
    15. Marquina, Jesús & Colinet, María José & Pablo-Romero, María del P., 2021. "The economic value of olive sector biomass for thermal and electrical uses in Andalusia (Spain)," Renewable and Sustainable Energy Reviews, Elsevier, vol. 148(C).
    16. Feng, Ping & Li, Xiaoyang & Wang, Jinyu & Li, Jie & Wang, Huan & He, Lu, 2021. "The mixtures of bio-oil derived from different biomass and coal/char as biofuels: Combustion characteristics," Energy, Elsevier, vol. 224(C).
    17. Liu, Xiufeng & Nielsen, Per Sieverts, 2016. "A hybrid ICT-solution for smart meter data analytics," Energy, Elsevier, vol. 115(P3), pages 1710-1722.
    18. Xu, Yuxin & Gao, Fei, 2024. "A novel higher-order Deffuant–Weisbuch networks model incorporating the Susceptible Infected Recovered framework," Chaos, Solitons & Fractals, Elsevier, vol. 182(C).
    19. Guo, Shu-Ling & Yang, Yong-Ge & Sun, Ya-Hui, 2021. "Stochastic response of an energy harvesting system with viscoelastic element under Gaussian white noise excitation," Chaos, Solitons & Fractals, Elsevier, vol. 151(C).
    20. Dimitrios Zikos, 2020. "Revisiting the Role of Institutions in Transformative Contexts: Institutional Change and Conflicts," Sustainability, MDPI, vol. 12(21), pages 1-20, October.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:15:y:2022:i:20:p:7453-:d:938397. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.