IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i15p5720-d881755.html
   My bibliography  Save this article

Optimization of Load Sharing in Compressor Station Based on Improved Salp Swarm Algorithm

Author

Listed:
  • Jiawei Zhang

    (School of Electronic Engineering, Xi’an Shiyou University, Xi’an 710065, China
    Shaanxi Key Laboratory of Measurement and Control Technology for Oil and Gas Wells, Xi’an 710065, China)

  • Lin Li

    (School of Electronic Engineering, Xi’an Shiyou University, Xi’an 710065, China
    Shaanxi Key Laboratory of Measurement and Control Technology for Oil and Gas Wells, Xi’an 710065, China
    These authors contributed equally to this work.)

  • Qizhi Zhang

    (School of Electronic Engineering, Xi’an Shiyou University, Xi’an 710065, China
    Shaanxi Key Laboratory of Measurement and Control Technology for Oil and Gas Wells, Xi’an 710065, China
    These authors contributed equally to this work.)

  • Yanbin Wu

    (No. 1 Oil Production Plant, Changqing Oilfield Company, Yan’an 716000, China)

Abstract

In long-distance gas transmission pipelines, there are many booster compressor stations consisting of parallel compressors that provide pressure for the delivery of natural gas. So, it is economically important to optimize the operation of the booster compressor station. The booster compressor station optimization problem is a typical mixed integer nonlinear programming (MINLP) problem, and solving it accurately and stably is a challenge. In this paper, we propose an improved salp swarm algorithm based on good point set, adaptive population division and adaptive inertia weight (GASSA) to solve this problem. In GASSA, three improvement strategies are utilized to enhance the global search capability of the algorithm and help the algorithm jump out of the local optimum. We also propose a constraint handling approach. By using semi-continuous variables, we directly describe the on or off state of the compressor instead of using auxiliary binary variables to reduce the number of variables and the difficulty of solving. The effectiveness of GASSA is firstly verified using eight standard benchmark functions, and the results show that GASSA has better performance than other selected algorithms. Then, GASSA is applied to optimize the booster compressor station load distribution model and compared with some well-known meta-heuristic algorithms. The results show that GASSA outperforms other algorithms in terms of accuracy and reliability.

Suggested Citation

  • Jiawei Zhang & Lin Li & Qizhi Zhang & Yanbin Wu, 2022. "Optimization of Load Sharing in Compressor Station Based on Improved Salp Swarm Algorithm," Energies, MDPI, vol. 15(15), pages 1-18, August.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:15:p:5720-:d:881755
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/15/5720/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/15/5720/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Lu, Hongfang & Ma, Xin & Azimi, Mohammadamin, 2020. "US natural gas consumption prediction using an improved kernel-based nonlinear extension of the Arps decline model," Energy, Elsevier, vol. 194(C).
    2. Milosavljevic, Predrag & Marchetti, Alejandro G. & Cortinovis, Andrea & Faulwasser, Timm & Mercangöz, Mehmet & Bonvin, Dominique, 2020. "Real-time optimization of load sharing for gas compressors in the presence of uncertainty," Applied Energy, Elsevier, vol. 272(C).
    3. Vasyl Zapukhliak & Lyubomyr Poberezhny & Pavlo Maruschak & Volodymyr Grudz Jr. & Roman Stasiuk & Janette Brezinová & Anna Guzanová, 2019. "Mathematical Modeling of Unsteady Gas Transmission System Operating Conditions under Insufficient Loading," Energies, MDPI, vol. 12(7), pages 1-14, April.
    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. Magazzino, Cosimo & Mele, Marco & Schneider, Nicolas, 2021. "A D2C algorithm on the natural gas consumption and economic growth: Challenges faced by Germany and Japan," Energy, Elsevier, vol. 219(C).
    2. Yin, Sihua & Yang, Haidong & Xu, Kangkang & Zhu, Chengjiu & Zhang, Shaqing & Liu, Guosheng, 2022. "Dynamic real–time abnormal energy consumption detection and energy efficiency optimization analysis considering uncertainty," Applied Energy, Elsevier, vol. 307(C).
    3. Lu, Hongfang & Ma, Xin & Huang, Kun & Azimi, Mohammadamin, 2020. "Prediction of offshore wind farm power using a novel two-stage model combining kernel-based nonlinear extension of the Arps decline model with a multi-objective grey wolf optimizer," Renewable and Sustainable Energy Reviews, Elsevier, vol. 127(C).
    4. Qin, Shuidan & Wang, Bing Xing & Tsai, Tzong-Ru & Wang, Xiaofei, 2023. "The prediction of remaining useful lifetime for the Weibull k-out-of-n load-sharing system," Reliability Engineering and System Safety, Elsevier, vol. 233(C).
    5. Singh, Sanjeet & Bansal, Pooja & Hosen, Mosharrof & Bansal, Sanjeev K., 2023. "Forecasting annual natural gas consumption in USA: Application of machine learning techniques- ANN and SVM," Resources Policy, Elsevier, vol. 80(C).
    6. Xin Ma & Yubin Cai & Hong Yuan & Yanqiao Deng, 2023. "Partially Linear Component Support Vector Machine for Primary Energy Consumption Forecasting of the Electric Power Sector in the United States," Sustainability, MDPI, vol. 15(9), pages 1-26, April.
    7. Pala, Zeydin, 2023. "Comparative study on monthly natural gas vehicle fuel consumption and industrial consumption using multi-hybrid forecast models," Energy, Elsevier, vol. 263(PC).
    8. Xu, Bin & Lin, Boqiang, 2020. "Investigating drivers of CO2 emission in China’s heavy industry: A quantile regression analysis," Energy, Elsevier, vol. 206(C).
    9. Palanisamy Manigandan & MD Shabbir Alam & Majed Alharthi & Uzma Khan & Kuppusamy Alagirisamy & Duraisamy Pachiyappan & Abdul Rehman, 2021. "Forecasting Natural Gas Production and Consumption in United States-Evidence from SARIMA and SARIMAX Models," Energies, MDPI, vol. 14(19), pages 1-17, September.
    10. Lu, Hongfang & Cheng, Feifei & Ma, Xin & Hu, Gang, 2020. "Short-term prediction of building energy consumption employing an improved extreme gradient boosting model: A case study of an intake tower," Energy, Elsevier, vol. 203(C).
    11. Hafezi, Reza & Akhavan, AmirNaser & Pakseresht, Saeed & A. Wood, David, 2021. "Global natural gas demand to 2025: A learning scenario development model," Energy, Elsevier, vol. 224(C).
    12. Bogusław Ptaszyński & Rafał Łuczak & Zbigniew Kuczera & Piotr Życzkowski & Klaudia Zwolińska-Glądys & Marek Borowski, 2024. "Pressure Change in a Duct with a Flow of a Homogeneous Gaseous Substance in the Presence of a Point Mass and Momentum Sink of Gas," Energies, MDPI, vol. 17(20), pages 1-28, October.
    13. Weichao Yu & Xianbin Zheng & Weihe Huang & Qingwen Cai & Jie Guo & Jili Xu & Yang Liu & Jing Gong & Hong Yang, 2022. "A Data-Driven Methodology for the Reliability Analysis of the Natural Gas Compressor Unit Considering Multiple Failure Modes," Energies, MDPI, vol. 15(10), pages 1-18, May.
    14. Mehrdad Massoudi, 2020. "Mathematical Modeling of Fluid Flow and Heat Transfer in Petroleum Industries and Geothermal Applications," Energies, MDPI, vol. 13(6), pages 1-4, March.
    15. Qiao Guo & Yuan Liu & Yunbo Yang & Tao Song & Shouxi Wang, 2022. "Improved Adaptive Time Step Method for Natural Gas Pipeline Transient Simulation," Energies, MDPI, vol. 15(14), pages 1-14, July.
    16. Deng, Yanqiao & Ma, Xin & Zhang, Peng & Cai, Yubin, 2022. "Multi-step ahead forecasting of daily urban gas load in Chengdu using a Tanimoto kernel-based NAR model and Whale optimization," Energy, Elsevier, vol. 260(C).
    17. Nourpour, Mohsen & Khoshgoftar Manesh, Mohammad Hasan, 2023. "Availability and 6E assessment and optimal design of a novel cogeneration system based on integrated turbo compressor station - SOFC-solar-geothermal-steam and organic Rankine cycles with machine lear," Renewable Energy, Elsevier, vol. 215(C).

    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:15:p:5720-:d:881755. 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.