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<p>Under the “Dual Carbon” strategy, efficient recovery of waste heat from diesel equipment in open-pit mines is required. Existing cooling systems cannot handle the dynamic load fluctuations in 5G-enabled energy supply systems, leading to delayed response and low energy efficiency. This paper builds a multi-objective optimization model based on an improved Honey Badger Algorithm. The model uses a Lithium Bromide Absorption cooling system and integrates differential evolution and balancing pool adjustment strategies to enhance the global search ability of the Honey Badger Algorithm. It is also embedded into a 5G scheduling platform to achieve real-time response and intelligent optimization of cooling loads. Experimental results show that the model achieves an average response time of only 1.13&#xa0;s. Comprehensive system performance indicators such as cooling output, unit cooling cost, and heat recovery rate all outperform traditional optimization methods. The average coefficient of performance reaches 1.78, and the unit cooling cost is as low as 0.40 yuan/kWh. These results demonstrate that the proposed multi-objective optimization model offers excellent performance and practicality in waste heat cooling systems in mining areas. It effectively addresses the problems of slow response and low energy efficiency found in traditional methods and provides a feasible technical path and theoretical support for building green and intelligent energy supply systems in open-pit mines.</p>

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Diesel waste heat cooling optimization in open-pit mines under 5G energy with an improved metaheuristic

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年11月12日

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  • Sufeng Hai,
  • Xingjun Ju,
  • Weifeng Miao,
  • Jingbin Yu,
  • Xinpeng Li,
  • Hui Wang

摘要

Under the “Dual Carbon” strategy, efficient recovery of waste heat from diesel equipment in open-pit mines is required. Existing cooling systems cannot handle the dynamic load fluctuations in 5G-enabled energy supply systems, leading to delayed response and low energy efficiency. This paper builds a multi-objective optimization model based on an improved Honey Badger Algorithm. The model uses a Lithium Bromide Absorption cooling system and integrates differential evolution and balancing pool adjustment strategies to enhance the global search ability of the Honey Badger Algorithm. It is also embedded into a 5G scheduling platform to achieve real-time response and intelligent optimization of cooling loads. Experimental results show that the model achieves an average response time of only 1.13 s. Comprehensive system performance indicators such as cooling output, unit cooling cost, and heat recovery rate all outperform traditional optimization methods. The average coefficient of performance reaches 1.78, and the unit cooling cost is as low as 0.40 yuan/kWh. These results demonstrate that the proposed multi-objective optimization model offers excellent performance and practicality in waste heat cooling systems in mining areas. It effectively addresses the problems of slow response and low energy efficiency found in traditional methods and provides a feasible technical path and theoretical support for building green and intelligent energy supply systems in open-pit mines.