IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i10p7818-d1143628.html

Experiment and Simulation on a Refrigeration Ventilation System for Deep Metal Mines

Author

Listed:
  • Wei Shao

    (Institute of Thermal Science and Technology, Shandong University, Jinan 250061, China
    Shandong Institute of Advanced Technology, Jinan 250100, China)

  • Shuo Wang

    (Institute of Thermal Science and Technology, Shandong University, Jinan 250061, China)

  • Wenpu Wang

    (Institute of Thermal Science and Technology, Shandong University, Jinan 250061, China)

  • Kun Shao

    (Shandong Institute of Advanced Technology, Jinan 250100, China)

  • Qi Xiao

    (Wuhan 2nd Ship Design and Research Institute, Wuhan 430205, China
    Science and Technology on Thermal Energy and Power Laboratory, Wuhan 430205, China)

  • Zheng Cui

    (Shandong Institute of Advanced Technology, Jinan 250100, China)

Abstract

Significant harm from heat has become a key restriction for deep metal mining with increasing mining depth. This paper proposes a refrigeration ventilation system for deep metal mines combined with an existing air cycling system and builds an experimental platform with six stope simulation boxes. Using the heat current method and the driving-resistance balance relationship, the heat transfer and flow constraints of the system were constructed. An artificial neural network was used to establish models of heat exchangers and refrigerators with historical experimental data. Combining the models of the system and stope simulation box, an algorithm that iterates the water outlet temperature of the evaporator and condenser of the refrigerator was proposed to design the coupled simulation model. The heat balance analysis and comparison of the air outlet temperatures of the stope, as well as the heat transfer rates of the heat exchangers with the experimental data, validated the coupled simulation model. Additionally, the effects of cooling fans and the air inlet temperature of the cooling tower were discussed, which provided a powerful modelling method for the coupled model of a refrigeration ventilation system, helps to reduce energy consumption, and improves the sustainability of mining production.

Suggested Citation

  • Wei Shao & Shuo Wang & Wenpu Wang & Kun Shao & Qi Xiao & Zheng Cui, 2023. "Experiment and Simulation on a Refrigeration Ventilation System for Deep Metal Mines," Sustainability, MDPI, vol. 15(10), pages 1-20, May.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:10:p:7818-:d:1143628
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/10/7818/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/10/7818/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Zhiyong Zhou & Yimeng Cui & Long Tian & Jianhong Chen & Wei Pan & Shan Yang & Pei Hu, 2019. "Study of the Influence of Ventilation Pipeline Setting on Cooling Effects in High-Temperature Mines," Energies, MDPI, vol. 12(21), pages 1-16, October.
    2. Singh, Priyanka & Dwivedi, Pragya, 2018. "Integration of new evolutionary approach with artificial neural network for solving short term load forecast problem," Applied Energy, Elsevier, vol. 217(C), pages 537-549.
    3. Wang, Yi-Fei & Chen, Qun, 2015. "A direct optimal control strategy of variable speed pumps in heat exchanger networks and experimental validations," Energy, Elsevier, vol. 85(C), pages 609-619.
    4. Rodríguez, Fermín & Fleetwood, Alice & Galarza, Ainhoa & Fontán, Luis, 2018. "Predicting solar energy generation through artificial neural networks using weather forecasts for microgrid control," Renewable Energy, Elsevier, vol. 126(C), pages 855-864.
    5. Chen, Qun & Fu, Rong-Huan & Xu, Yun-Chao, 2015. "Electrical circuit analogy for heat transfer analysis and optimization in heat exchanger networks," Applied Energy, Elsevier, vol. 139(C), pages 81-92.
    6. Amrouche, Badia & Le Pivert, Xavier, 2014. "Artificial neural network based daily local forecasting for global solar radiation," Applied Energy, Elsevier, vol. 130(C), pages 333-341.
    7. Ma, Huan & Chen, Qun & Hu, Bo & Sun, Qinhan & Li, Tie & Wang, Shunjiang, 2021. "A compact model to coordinate flexibility and efficiency for decomposed scheduling of integrated energy system," Applied Energy, Elsevier, vol. 285(C).
    8. Gou, Xing & Chen, Qun & Sun, Yong & Ma, Huan & Li, Bao-Ju, 2021. "Holistic analysis and optimization of distributed energy system considering different transport characteristics of multi-energy and component efficiency variation," Energy, Elsevier, vol. 228(C).
    9. Chen, Qun & Xu, Yun-Chao, 2012. "An entransy dissipation-based optimization principle for building central chilled water systems," Energy, Elsevier, vol. 37(1), pages 571-579.
    10. Wang, Lan & Lee, Eric W.M. & Yuen, Richard K.K., 2018. "Novel dynamic forecasting model for building cooling loads combining an artificial neural network and an ensemble approach," Applied Energy, Elsevier, vol. 228(C), pages 1740-1753.
    11. Ghorbanian, K. & Gholamrezaei, M., 2009. "An artificial neural network approach to compressor performance prediction," Applied Energy, Elsevier, vol. 86(7-8), pages 1210-1221, 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. Wang, Ran & Lu, Shilei & Feng, Wei, 2020. "A novel improved model for building energy consumption prediction based on model integration," Applied Energy, Elsevier, vol. 262(C).
    2. Benedetti, Miriam & Cesarotti, Vittorio & Introna, Vito & Serranti, Jacopo, 2016. "Energy consumption control automation using Artificial Neural Networks and adaptive algorithms: Proposal of a new methodology and case study," Applied Energy, Elsevier, vol. 165(C), pages 60-71.
    3. Xu, Rong-Hong & Zhao, Tian & Ma, Huan & He, Ke-Lun & Lv, Hong-Kun & Guo, Xu-Tao & Chen, Qun, 2023. "Operation optimization of distributed energy systems considering nonlinear characteristics of multi-energy transport and conversion processes," Energy, Elsevier, vol. 283(C).
    4. Chen, Xi & Chen, Qun & Chen, Hong & Xu, Ying-Gen & Zhao, Tian & Hu, Kang & He, Ke-Lun, 2019. "Heat current method for analysis and optimization of heat recovery-based power generation systems," Energy, Elsevier, vol. 189(C).
    5. Xu, Yun-Chao & Chen, Qun & Guo, Zeng-Yuan, 2015. "Entransy dissipation-based constraint for optimization of heat exchanger networks in thermal systems," Energy, Elsevier, vol. 86(C), pages 696-708.
    6. Lin, Qiliang & Chen, Yi-Chung & Chen, Fangliang & DeGanyar, Tejav & Yin, Huiming, 2022. "Design and experiments of a thermoelectric-powered wireless sensor network platform for smart building envelope," Applied Energy, Elsevier, vol. 305(C).
    7. Li, Xia & Chen, Qun & Chen, Xi & He, Ke-Lun & Hao, Jun-Hong, 2020. "Graph theory-based heat current analysis method for supercritical CO2 power generation system," Energy, Elsevier, vol. 194(C).
    8. Xu, Yun-Chao & Chen, Qun, 2013. "A theoretical global optimization method for vapor-compression refrigeration systems based on entransy theory," Energy, Elsevier, vol. 60(C), pages 464-473.
    9. 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).
    10. Mousavi, Navid & Kothapalli, Ganesh & Habibi, Daryoush & Das, Choton K. & Baniasadi, Ali, 2020. "A novel photovoltaic-pumped hydro storage microgrid applicable to rural areas," Applied Energy, Elsevier, vol. 262(C).
    11. Andreas Lenk & Marcus Vogt & Christoph Herrmann, 2024. "An Approach to Predicting Energy Demand Within Automobile Production Using the Temporal Fusion Transformer Model," Energies, MDPI, vol. 18(1), pages 1-34, December.
    12. Mohsen Beigi & Hossein Beigi Harchegani & Mehdi Torki & Mohammad Kaveh & Mariusz Szymanek & Esmail Khalife & Jacek Dziwulski, 2022. "Forecasting of Power Output of a PVPS Based on Meteorological Data Using RNN Approaches," Sustainability, MDPI, vol. 14(5), pages 1-12, March.
    13. Fernando Venâncio Mucomole & Carlos Augusto Santos Silva & Lourenço Lázaro Magaia, 2025. "Parametric Forecast of Solar Energy over Time by Applying Machine Learning Techniques: Systematic Review," Energies, MDPI, vol. 18(6), pages 1-51, March.
    14. Luo, X.J. & Oyedele, Lukumon O. & Ajayi, Anuoluwapo O. & Akinade, Olugbenga O. & Owolabi, Hakeem A. & Ahmed, Ashraf, 2020. "Feature extraction and genetic algorithm enhanced adaptive deep neural network for energy consumption prediction in buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
    15. Yadav, Amit Kumar & Malik, Hasmat & Chandel, S.S., 2015. "Application of rapid miner in ANN based prediction of solar radiation for assessment of solar energy resource potential of 76 sites in Northwestern India," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 1093-1106.
    16. Gharibpour, Hassan & Aminifar, Farrokh & Rahmati, Iman & Keshavarz, Arezou, 2021. "Dual variable decomposition to discriminate the cost imposed by inflexible units in electricity markets," Applied Energy, Elsevier, vol. 287(C).
    17. Da Liu & Kun Sun & Han Huang & Pingzhou Tang, 2018. "Monthly Load Forecasting Based on Economic Data by Decomposition Integration Theory," Sustainability, MDPI, vol. 10(9), pages 1-22, September.
    18. Kuk Yeol Bae & Han Seung Jang & Bang Chul Jung & Dan Keun Sung, 2019. "Effect of Prediction Error of Machine Learning Schemes on Photovoltaic Power Trading Based on Energy Storage Systems," Energies, MDPI, vol. 12(7), pages 1-20, April.
    19. Zhang, Suhan & Gu, Wei & Lu, Hai & Qiu, Haifeng & Lu, Shuai & Wang, Dada & Liang, Junyu & Li, Wenyun, 2021. "Superposition-principle based decoupling method for energy flow calculation in district heating networks," Applied Energy, Elsevier, vol. 295(C).
    20. Gautham Krishnadas & Aristides Kiprakis, 2020. "A Machine Learning Pipeline for Demand Response Capacity Scheduling," Energies, MDPI, vol. 13(7), pages 1-25, April.

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:jsusta:v:15:y:2023:i:10:p:7818-:d:1143628. 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.