CIESC Journal ›› 2018, Vol. 69 ›› Issue (6): 2603-2611.doi: 10.11949/j.issn.0438-1157.20171349

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Working fluid selection and multi-objective optimization of organic Rankine cycle with variable turbine efficiency

HAN Zhonghe, MEI Zhongkai, LI Peng   

  1. Key laboratory of Condition Monitoring and Control for Power Plant Equipment, Ministry of Education, North China Electric Power University, Baoding 071003, Hebei, China
  • Received:2017-10-11 Revised:2017-12-19 Online:2018-06-05 Published:2018-02-12
  • Supported by:

    supported by the National Natural Science Foundation of China (51306059) and the Fundamental Research Funds for the Central Universities(2017XS120).

Abstract:

To harness heat of 523.15 K high temperature flue gas, pentane, hexane, heptane, cyclohexane, MM (hexamethyldisiloxane), benzene and toluene were selected as working fluid candidates. With selection of one-dimensional radial-inflow turbine efficiency prediction model to replace constant turbine efficiency model, and net power output and exergy efficiency as target functions, organic Rankine cycle (ORC) system was simulated for multiple output variables by using non-dominated sorting genetic algorithm (NSGA-Ⅱ). Optimal solution of each working fluid was determined from Pareto frontiers by ideal point estimation. The results show a strong correlation between turbine efficiency and volumetric flow ratio (VFR) of working fluids in a way that turbine efficiency curve trends oppositely to VFR curve. At fixed heat source conditions, benzene is the optimal working fluid whereas toluene and cyclohexane are sub-optimal. Exergy efficiency accelerates in a downward trend at evaporation temperature above 400 K, but net power output slows down in a rise trend at evaporation temperature above 410 K. Optimization with constant turbine efficiency model somewhat affects screening results of optimal parameters and best working fluid, which are deviated from actual outcomes. However, optimization with variable turbine efficiency model can reduce such error and results are much closer to engineering practice.

Key words: organic Rankine cycle, multi-objective optimization, one-dimensional radial-inflow turbine efficiency prediction model, thermodynamic, economic, volumetric flow ratio

CLC Number: 

  • TK123

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