IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i9p3632-d1130978.html
   My bibliography  Save this article

Research on Optimization of Profile Parameters in Screw Compressor Based on BP Neural Network and Genetic Algorithm

Author

Listed:
  • Tao Wang

    (School of Energy and Power Engineering, Zhengzhou University of Light Industry, Zhengzhou 450002, China)

  • Qiang Qi

    (School of Energy and Power Engineering, Zhengzhou University of Light Industry, Zhengzhou 450002, China)

  • Wei Zhang

    (School of Energy and Power Engineering, Zhengzhou University of Light Industry, Zhengzhou 450002, China
    School of Energy and Power Engineering, Xi’an Jiaotong University, Xi’an 710049, China)

  • Dengyi Zhan

    (School of Energy and Power Engineering, Zhengzhou University of Light Industry, Zhengzhou 450002, China)

Abstract

In order to accurately calculate the geometric characteristics of the twin-screw compressor and obtain the optimal profile parameters, a calculation method for the geometric characteristics of twin-screw compressors was proposed to simplify the profile parameter design in this paper. In this method, the database of geometric characteristics is established by back-propagation (BP) neural network, and the genetic algorithm is used to find the optimal profile design parameters. The effects of training methods and hidden layers on the calculation accuracy of neural network are discussed. The effects of profile parameters, including inner radius of the male rotor, protection angle, radius of the elliptic arc, outer radius of the female rotor on the comprehensive evaluation value composed of length of the contact line, blow hole area and area utilization rate, are analyzed. The results show that the time consumed for the database established by BP neural network is 92.8% shorter than that of the traditional method and the error is within 1.5% of the traditional method. Based on the genetic algorithm, compared with the original profile, the blow hole area of the screw compressor profile optimized by genetic algorithm is reduced by 54.8%, the length of contact line is increased by 1.57% and the area utilization rate is increased by 0.32%. The CFD numerical model is used to verify the optimization method, and it can be observed that the leakage through the blow hole of the optimized model is reduced, which makes the average mass flow rate increase by 5.2%, indicating the effectiveness of the rotor profile parameter optimization method.

Suggested Citation

  • Tao Wang & Qiang Qi & Wei Zhang & Dengyi Zhan, 2023. "Research on Optimization of Profile Parameters in Screw Compressor Based on BP Neural Network and Genetic Algorithm," Energies, MDPI, vol. 16(9), pages 1-17, April.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:9:p:3632-:d:1130978
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/9/3632/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/9/3632/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Huagen Wu & Jiankang Liu & Yuqi Shen & Mengtao Liang & Beiyu Zhang, 2021. "Research on Performance of Variable-Lead Rotor Twin Screw Compressor," Energies, MDPI, vol. 14(21), pages 1-16, October.
    2. Sun-Seok Byeon & Jae-Young Lee & Youn-Jea Kim, 2017. "Performance Characteristics of a 4 × 6 Oil-Free Twin-Screw Compressor," Energies, MDPI, vol. 10(7), pages 1-16, July.
    3. Zhilong He & Tao Wang & Xiaolin Wang & Xueyuan Peng & Ziwen Xing, 2018. "Experimental Investigation into the Effect of Oil Injection on the Performance of a Variable Speed Twin-Screw Compressor," Energies, MDPI, vol. 11(6), pages 1-14, May.
    4. Huagen Wu & Hao Huang & Beiyu Zhang & Baoshun Xiong & Kanlong Lin, 2019. "CFD Simulation and Experimental Study of Working Process of Screw Refrigeration Compressor with R134a," Energies, MDPI, vol. 12(11), pages 1-14, May.
    5. Ahmed, Fawad & Zhu, Shunmin & Yu, Guoyao & Luo, Ercang, 2022. "A potent numerical model coupled with multi-objective NSGA-II algorithm for the optimal design of Stirling engine," Energy, Elsevier, vol. 247(C).
    6. Francisco J. Rey Martínez & Julio F. San José Alonso & Eloy Velasco Gómez & Ana Tejero González & Paula M Esquivias & Javier M. Rey Hernández, 2020. "Energy Consumption Reduction of a Chiller Plant by Adding Evaporative Pads to Decrease Condensation Temperature," Energies, MDPI, vol. 13(9), pages 1-13, May.
    7. Concepcion Hernandez & Jorge Lara & Marco A. Arjona & Enrique Melgoza-Vazquez, 2023. "Multi-Objective Electromagnetic Design Optimization of a Power Transformer Using 3D Finite Element Analysis, Response Surface Methodology, and the Third Generation Non-Sorting Genetic Algorithm," Energies, MDPI, vol. 16(5), pages 1-21, February.
    8. Tian, Yafen & Geng, Yanting & Yuan, Hao & Zhao, Zhaorui, 2022. "Investigation on water injection characteristics and its influence on the performance of twin-screw steam compressor," Energy, Elsevier, vol. 259(C).
    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. Zhilong He & Tao Wang & Xiaolin Wang & Xueyuan Peng & Ziwen Xing, 2018. "Experimental Investigation into the Effect of Oil Injection on the Performance of a Variable Speed Twin-Screw Compressor," Energies, MDPI, vol. 11(6), pages 1-14, May.
    2. Shizhong Sun & Yiwei Feng & Ziwen Xing & Minglong Zhou & Wenqing Chen & Chuang Wang & Hanyang Cui, 2022. "Study on Influential Mechanism of Trailing Edge Sweep Angle on Aerodynamic Noise of a Centrifugal Air Compressor," Energies, MDPI, vol. 15(19), pages 1-14, October.
    3. Ana Tejero‐González & Antonio Franco‐Salas, 2022. "Direct evaporative cooling from wetted surfaces: Challenges for a clean air conditioning solution," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 11(3), May.
    4. Huagen Wu & Hao Huang & Beiyu Zhang & Baoshun Xiong & Kanlong Lin, 2019. "CFD Simulation and Experimental Study of Working Process of Screw Refrigeration Compressor with R134a," Energies, MDPI, vol. 12(11), pages 1-14, May.
    5. Wang, Huaiyu & Ji, Changwei & Yang, Jinxin & Wang, Shuofeng & Ge, Yunshan, 2022. "Towards a comprehensive optimization of the intake characteristics for side ported Wankel rotary engines by coupling machine learning with genetic algorithm," Energy, Elsevier, vol. 261(PB).
    6. Chia-Cheng Tsao & Wen-Kai Lin & Kai-Yuan Lai & Savas Yavuzkurt & Yao-Hsien Liu, 2023. "Numerical Investigation of Compression and Expansion Process of Twin-Screw Machine Using R-134a," Energies, MDPI, vol. 16(8), pages 1-14, April.
    7. Shang, Jingyi & Gao, Jinfeng & Jiang, Xin & Liu, Mingguang & Liu, Dunnan, 2023. "Optimal configuration of hybrid energy systems considering power to hydrogen and electricity-price prediction: A two-stage multi-objective bi-level framework," Energy, Elsevier, vol. 263(PF).
    8. Qing Guo & Kai Luo & Daijin Li & Chuang Huang & Kan Qin, 2021. "Effect of Operating Conditions on the Performance of Gas–Liquid Mixture Roots Pumps," Energies, MDPI, vol. 14(17), pages 1-23, August.

    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:16:y:2023:i:9:p:3632-:d:1130978. 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.