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

Author

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  • 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
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    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.
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