IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i10p3824-d821634.html
   My bibliography  Save this article

Study on a Discharge Circuit Prediction Model of High-Voltage Electro-Pulse Boring Based on Bayesian Fusion

Author

Listed:
  • Changping Li

    (School of Mechanical Engineering and Electronic Information, China University of Geosciences, Wuhan 430074, China
    Hubei Key Laboratory of Drilling and Production Engineering for Oil and Gas, Yangtze University, Wuhan 430199, China)

  • Xiaohui Wang

    (School of Mechanical Engineering and Electronic Information, China University of Geosciences, Wuhan 430074, China)

  • Longchen Duan

    (Faculty of Engineering, China University of Geosciences, Wuhan 430074, China)

  • Bo Lei

    (School of Mechanical Engineering and Electronic Information, China University of Geosciences, Wuhan 430074, China)

Abstract

It is necessary to develop new drilling and breaking technology for hard rock construction. However, the process of high-voltage electro-pulse (HVEP) rock-breaking is complex, and the selection of electro-pulse boring (EPB) process parameters lacks a theoretical basis. Firstly, the RLC model, TV-RLC model, and TV-CRLC model are established based on the characteristics of the HVEP circuit to predict the EPB dynamic discharge curve. Secondly, the parameters are identified by the Particle Swarm Optimization Genetic Algorithm (PSO-GA). Finally, due to the nonlinear and complex time-varying characteristics of the discharge circuit, the discharge circuit prediction models based on Bayesian fusion and current residual normalization fusion method are proposed, and the optimal weight of these three models is determined. Compared with the single models for EPB current prediction, the average relative error reduction rates based on Bayesian fusion and current residual normalization fusion methods are 25.5% and 9.5%, respectively. In this paper, the discharge circuit prediction model based on Bayesian fusion is established, which improves the prediction accuracy and reliability of the model, and it guides the selection of process parameters and the design of pulse power supply and electrode bits.

Suggested Citation

  • Changping Li & Xiaohui Wang & Longchen Duan & Bo Lei, 2022. "Study on a Discharge Circuit Prediction Model of High-Voltage Electro-Pulse Boring Based on Bayesian Fusion," Energies, MDPI, vol. 15(10), pages 1-12, May.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:10:p:3824-:d:821634
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/10/3824/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/10/3824/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Lin, Cheng & Mu, Hao & Xiong, Rui & Shen, Weixiang, 2016. "A novel multi-model probability battery state of charge estimation approach for electric vehicles using H-infinity algorithm," Applied Energy, Elsevier, vol. 166(C), pages 76-83.
    2. Jia, Ke & Li, Yanbin & Fang, Yu & Zheng, Liming & Bi, Tianshu & Yang, Qixun, 2018. "Transient current similarity based protection for wind farm transmission lines," Applied Energy, Elsevier, vol. 225(C), pages 42-51.
    3. Lin, Cheng & Mu, Hao & Xiong, Rui & Cao, Jiayi, 2017. "Multi-model probabilities based state fusion estimation method of lithium-ion battery for electric vehicles: State-of-energy," Applied Energy, Elsevier, vol. 194(C), pages 560-568.
    4. Changping Li & Longchen Duan & Songcheng Tan & Victor Chikhotkin, 2018. "Influences on High-Voltage Electro Pulse Boring in Granite," Energies, MDPI, vol. 11(9), pages 1-17, September.
    5. Changping Li & Longchen Duan & Songcheng Tan & Victor Chikhotkin & Wenpeng Fu, 2019. "Damage Model and Numerical Experiment of High-Voltage Electro Pulse Boring in Granite," Energies, MDPI, vol. 12(4), pages 1-19, February.
    6. Ghimire, Sujan & Deo, Ravinesh C. & Raj, Nawin & Mi, Jianchun, 2019. "Deep solar radiation forecasting with convolutional neural network and long short-term memory network algorithms," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    7. Salah Bouktif & Ali Fiaz & Ali Ouni & Mohamed Adel Serhani, 2018. "Optimal Deep Learning LSTM Model for Electric Load Forecasting using Feature Selection and Genetic Algorithm: Comparison with Machine Learning Approaches †," Energies, MDPI, vol. 11(7), pages 1-20, June.
    8. Wang, Xiaoling & Gao, Yuan & Zhang, Shuai & Sun, Hao & Li, Jie & Shao, Tao, 2019. "Nanosecond pulsed plasma assisted dry reforming of CH4: The effect of plasma operating parameters," Applied Energy, Elsevier, vol. 243(C), pages 132-144.
    9. Gao, Yuan & Zhang, Shuai & Sun, Hao & Wang, Ruixue & Tu, Xin & Shao, Tao, 2018. "Highly efficient conversion of methane using microsecond and nanosecond pulsed spark discharges," Applied Energy, Elsevier, vol. 226(C), pages 534-545.
    10. Gao, Feng & Chi, Hong & Shao, Xueyan, 2021. "Forecasting residential electricity consumption using a hybrid machine learning model with online search data," Applied Energy, Elsevier, vol. 300(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. George, Adwek & Shen, Boxiong & Craven, Michael & Wang, Yaolin & Kang, Dongrui & Wu, Chunfei & Tu, Xin, 2021. "A Review of Non-Thermal Plasma Technology: A novel solution for CO2 conversion and utilization," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    2. Zhixiang Cai & Hui Zhang & Kerou Liu & Yufei Chen & Qing Yu, 2020. "Experimental Investigation and Mechanism Analysis on Rock Damage by High Voltage Spark Discharge in Water: Effect of Electrical Conductivity," Energies, MDPI, vol. 13(20), pages 1-16, October.
    3. Zhang, Xu & Wang, Yujie & Wu, Ji & Chen, Zonghai, 2018. "A novel method for lithium-ion battery state of energy and state of power estimation based on multi-time-scale filter," Applied Energy, Elsevier, vol. 216(C), pages 442-451.
    4. Lim, Juin Yau & Safder, Usman & How, Bing Shen & Ifaei, Pouya & Yoo, Chang Kyoo, 2021. "Nationwide sustainable renewable energy and Power-to-X deployment planning in South Korea assisted with forecasting model," Applied Energy, Elsevier, vol. 283(C).
    5. Tian, Yong & Huang, Zhijia & Tian, Jindong & Li, Xiaoyu, 2022. "State of charge estimation of lithium-ion batteries based on cubature Kalman filters with different matrix decomposition strategies," Energy, Elsevier, vol. 238(PC).
    6. Bingjie Jin & Guihua Zeng & Zhilin Lu & Hongqiao Peng & Shuxin Luo & Xinhe Yang & Haojun Zhu & Mingbo Liu, 2022. "Hybrid LSTM–BPNN-to-BPNN Model Considering Multi-Source Information for Forecasting Medium- and Long-Term Electricity Peak Load," Energies, MDPI, vol. 15(20), pages 1-20, October.
    7. Gao, Yuan & Miyata, Shohei & Akashi, Yasunori, 2022. "Interpretable deep learning models for hourly solar radiation prediction based on graph neural network and attention," Applied Energy, Elsevier, vol. 321(C).
    8. Suriyan Jomthanachai & Wai Peng Wong & Khai Wah Khaw, 2024. "An Application of Machine Learning to Logistics Performance Prediction: An Economics Attribute-Based of Collective Instance," Computational Economics, Springer;Society for Computational Economics, vol. 63(2), pages 741-792, February.
    9. Xuezhe Wei & Xueyuan Wang & Haifeng Dai, 2018. "Practical On-Board Measurement of Lithium Ion Battery Impedance Based on Distributed Voltage and Current Sampling," Energies, MDPI, vol. 11(1), pages 1-15, January.
    10. Lu, Yunbo & Wang, Lunche & Zhu, Canming & Zou, Ling & Zhang, Ming & Feng, Lan & Cao, Qian, 2023. "Predicting surface solar radiation using a hybrid radiative Transfer–Machine learning model," Renewable and Sustainable Energy Reviews, Elsevier, vol. 173(C).
    11. Shree Krishna Acharya & Young-Min Wi & Jaehee Lee, 2019. "Short-Term Load Forecasting for a Single Household Based on Convolution Neural Networks Using Data Augmentation," Energies, MDPI, vol. 12(18), pages 1-19, September.
    12. Chen, Zheng & Zhao, Hongqian & Shu, Xing & Zhang, Yuanjian & Shen, Jiangwei & Liu, Yonggang, 2021. "Synthetic state of charge estimation for lithium-ion batteries based on long short-term memory network modeling and adaptive H-Infinity filter," Energy, Elsevier, vol. 228(C).
    13. Hu, Xiaosong & Feng, Fei & Liu, Kailong & Zhang, Lei & Xie, Jiale & Liu, Bo, 2019. "State estimation for advanced battery management: Key challenges and future trends," Renewable and Sustainable Energy Reviews, Elsevier, vol. 114(C), pages 1-1.
    14. Li, Xiaoyu & Zhang, Zuguang & Wang, Wenhui & Tian, Yong & Li, Dong & Tian, Jindong, 2020. "Multiphysical field measurement and fusion for battery electric-thermal-contour performance analysis," Applied Energy, Elsevier, vol. 262(C).
    15. Xiao, Zenan & Huang, Xiaoqiao & Liu, Jun & Li, Chengli & Tai, Yonghang, 2023. "A novel method based on time series ensemble model for hourly photovoltaic power prediction," Energy, Elsevier, vol. 276(C).
    16. Qiaohua Fang & Xuezhe Wei & Haifeng Dai, 2019. "A Remaining Discharge Energy Prediction Method for Lithium-Ion Battery Pack Considering SOC and Parameter Inconsistency," Energies, MDPI, vol. 12(6), pages 1-24, March.
    17. Qiao Zhu & Neng Xiong & Ming-Liang Yang & Rui-Sen Huang & Guang-Di Hu, 2017. "State of Charge Estimation for Lithium-Ion Battery Based on Nonlinear Observer: An H ∞ Method," Energies, MDPI, vol. 10(5), pages 1-19, May.
    18. Wei, Zhongbao & Zhao, Jiyun & Ji, Dongxu & Tseng, King Jet, 2017. "A multi-timescale estimator for battery state of charge and capacity dual estimation based on an online identified model," Applied Energy, Elsevier, vol. 204(C), pages 1264-1274.
    19. Asif Hussain Khoja & Abul Kalam Azad & Faisal Saleem & Bilal Alam Khan & Salman Raza Naqvi & Muhammad Taqi Mehran & Nor Aishah Saidina Amin, 2020. "Hydrogen Production from Methane Cracking in Dielectric Barrier Discharge Catalytic Plasma Reactor Using a Nanocatalyst," Energies, MDPI, vol. 13(22), pages 1-15, November.
    20. Ajith, Meenu & Martínez-Ramón, Manel, 2021. "Deep learning based solar radiation micro forecast by fusion of infrared cloud images and radiation data," Applied Energy, Elsevier, vol. 294(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:10:p:3824-:d:821634. 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.