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Dynamic modeling and optimization of organic Rankine cycle in the waste heat recovery of the hydraulic system

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  • Gu, Zhengzhao
  • Feng, Kewen
  • Ge, Lei
  • Quan, Long

Abstract

The replacement of cooling system with the organic Rankine cycle could reduce cooling energy consumption and improve the overall energy efficiency in the hydraulic system. However, dynamic characteristics have a significant impact on stable operating and performance improvement of ORC systems in applications. Therefore, based on the physical prototype, the ORC dynamic model with whole process, including start-stop process, was established and calibration. Then the dynamic characteristics of the system were studied, and the response surface method was used to optimize the operating parameters with the dynamic model. The results show that the simulation model has sufficient accuracy under both steady and transient conditions. Compared with the oil flowrate, the connected-load affects the system performance more obvious. The evaporating pressure has increased by 3.4% as the connected-load increases from 60 W to 120 W. As to the response time of evaporating pressure and evaporator outlet temperature, they are 240 s and 290 s respectively in the starting stage, and are 210 s and 190 s respectively in the stopping stage. Through the optimization, the output power of the ORC system could reach the maximum value of 832 W, when the working medium pump and expander speed are 1500 rpm and 1400 rpm, respectively. The dynamic fluctuation of oil flowrate has relatively little influence on ORC system, it can work stably in the waste heat recovery of hydraulic system, and the operation and control should consider the long response time of ORC starting and stopping process.

Suggested Citation

  • Gu, Zhengzhao & Feng, Kewen & Ge, Lei & Quan, Long, 2023. "Dynamic modeling and optimization of organic Rankine cycle in the waste heat recovery of the hydraulic system," Energy, Elsevier, vol. 263(PB).
    • Handle: RePEc:eee:energy:v:263:y:2023:i:pb:s0360544222025592
    DOI: 10.1016/j.energy.2022.125673
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    1. Lu, Shengdong & Yang, Xinle & Bu, Shujuan & Li, Weikang & Yu, Ning & Wang, Xin & Dai, Wenzhi & Liu, Xunan, 2024. "Performance and parameter prediction of SCR–ORC system based on data–model fusion and twin data–driven," Energy, Elsevier, vol. 290(C).

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