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Multi-objective optimization of a multiple heat sources small-scale organic Rankine cycle with quasi two stage single screw expander using machine learning techniques

Author

Listed:
  • Wang, Hai-Xiao
  • Lei, Biao
  • Cao, Jia-Yuan
  • Luo, Wei
  • Wu, Yu-Ting
  • Du, Yan-Jun
  • Zhang, Ye-Qiang
  • Liu, Hai-Jie

Abstract

Based on the characteristics of waste heat resources in the iron-making area of the Wuhan Steel of China Baowu Group, this study innovatively develops a multiple heat sources organic Rankine cycle (ORC) system that integrates slag flushing water and steam. A self-developed quasi two stage single screw expander (QTS-SSE) as the core power component, and utilizes machine learning techniques to construct a predictive model for the key parameters of QTS-SSE. An evaluation system for the multiple heat sources ORC system is established, focusing on energy, economic, environmental, and exergy dimensions. The results show that the QTS-SSE, through an innovative velocity expansion design, enhances system performance with a maximum recoverable power increase of 0.32 kW and an isentropic efficiency rise of 2.31 percentage points. Compared to conventional single screw expander ORC systems, this approach offers significant improvements in net power, thermal efficiency, electricity production cost, and exergy efficiency, with enhancements of 2.65 %, 2.65 %, 2.52 %, and 3.03 %, respectively. Furthermore, a multi-objective particle swarm optimization algorithm optimizes operational parameters, achieving a balance among energy efficiency, economic viability, environmental impact, and exergy efficiency. This research provides a valuable technical solution for the efficient recovery and utilization of waste heat resources in the steel industry.

Suggested Citation

  • Wang, Hai-Xiao & Lei, Biao & Cao, Jia-Yuan & Luo, Wei & Wu, Yu-Ting & Du, Yan-Jun & Zhang, Ye-Qiang & Liu, Hai-Jie, 2025. "Multi-objective optimization of a multiple heat sources small-scale organic Rankine cycle with quasi two stage single screw expander using machine learning techniques," Energy, Elsevier, vol. 339(C).
    • Handle: RePEc:eee:energy:v:339:y:2025:i:c:s0360544225047255
    DOI: 10.1016/j.energy.2025.139083
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