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<p>To address the low efficiency caused by unstable operation of heating systems under variable operating conditions, this paper proposes an innovative dynamic optimization framework based on exergy analysis, filling a research gap in quantifying energy quality loss and enabling collaborative optimization across multiple operating conditions. A novel scientific partitioning method for variable operating conditions is developed using SCADA data combined with k-means clustering and silhouette coefficients, overcoming the limitations of traditional steady-state analyses. A refined flow model is constructed that integrates dynamic constraints of variable operating conditions and accurately quantifies loss distribution across different scenarios. Furthermore, a multi-parameter collaborative optimization strategy driven by a genetic algorithm is designed to target and reduce key loss mechanisms. Experimental verification shows that this method achieves an average increase of 6.6% in the exergy index. Under light-load and high-load conditions, the system’s exergy efficiency improves by 6.1% and 5.2%, respectively, providing both a theoretical breakthrough and a new engineering paradigm for efficient energy utilization under dynamic operating conditions.</p>

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Exergy-based optimization of heating system performance under variable operating conditions

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月11日

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  • Ruiqing Liang,
  • Shan Hua,
  • Xianhua Wen,
  • XingCheng Liu,
  • Changhao Fan,
  • Shuchong Wang,
  • Jiangjun Zhu,
  • Qichang Wang,
  • Dekui Shen

摘要

To address the low efficiency caused by unstable operation of heating systems under variable operating conditions, this paper proposes an innovative dynamic optimization framework based on exergy analysis, filling a research gap in quantifying energy quality loss and enabling collaborative optimization across multiple operating conditions. A novel scientific partitioning method for variable operating conditions is developed using SCADA data combined with k-means clustering and silhouette coefficients, overcoming the limitations of traditional steady-state analyses. A refined flow model is constructed that integrates dynamic constraints of variable operating conditions and accurately quantifies loss distribution across different scenarios. Furthermore, a multi-parameter collaborative optimization strategy driven by a genetic algorithm is designed to target and reduce key loss mechanisms. Experimental verification shows that this method achieves an average increase of 6.6% in the exergy index. Under light-load and high-load conditions, the system’s exergy efficiency improves by 6.1% and 5.2%, respectively, providing both a theoretical breakthrough and a new engineering paradigm for efficient energy utilization under dynamic operating conditions.