[Objective] This study aims to enrich the experimental study of the ORC using mixture working fluids and analyze its transient and steady-state operating characteristics. [Methods] A 4-kW organic Rankine cycle(ORC) prototype using R236 fa/R123 was tested to obtain the transient and steady-state operating data. [Results] The results of the ORC system at different mixture concentrations reveal that the operation characteristics of the ORC with mixed working fluids are consistent with those using a pure working fluid. The adjustment of the working fluid mass flow rate significantly affects the system output power
while it has a limited impact on the thermal efficiency. The ORC system operates relatively stable with the variation in the mixture concentration
as it has a minimal effect on both the output power and thermal efficiency. With the increase in the R236 fa mass fraction
the expander inlet pressure is stable while its outlet pressure gradually increases
resulting in high pressure ratios. This phenomenon indicates that adopting the expander with a larger internal volume ratio can optimize the ORC performance. The trade-off between the exergy destructions of the evaporator and condenser at different mixture concentrations is the main factor determining the system performance. The temperature glide caused by the condenser pressure drop limits the attribution of the mixture working fluid to optimize the system performance. [Conclusions] In the design and operation of mixed-working-fluid ORC systems
it is crucial to consider the matching of the condenser pressure drop and the working fluid inherent properties.
Thermodynamic performance analysis for dual fluid organic Rankine cycle system
Experimental investigation on a 4 kW organic Rankine cycle system subjected to different heat source flow rates and temperatures
Flexible operating characteristics of an organic Rankine cycle for distributed energy system
Design and dynamic simulation of a geothermal organic Rankine cycle system
Quantitative analysis and multi-objective optimization of Brayton cycle based on artificial neural network method
Related Author
LIAO Lei
YU Tingfang
LI Jifei
LI Ruixia
MIAO Zheng
XU Jinliang
ZHOU Yuhao
RUAN Jiongming
Related Institution
School of Mechanical and Electrical Engineering, Nanchang University
Sinopec Xinxing (Beijing) New Energy Research Institute Co., Ltd
Beijing Key Laboratory of Low Grade Energy Multiphase Flow and Heat Transfer (North China Electric Power University)
Key Laboratory of Space Engineering and Heat Conversion Technology, Institute of Physical and Chemical Technology, Chinese Academy of Sciences, Chinese Academy of Sciences