Experimental Study Of Full-condition Operation And Neural Network Performance Optimization Of Organic Rankine Cycle With Different Working Fluids

To deal with the challenges of energy contradiction and global climate change,countries around the world are gradually developing middle-low temperature recovery technology,so as to improve the utilization efficiency of energy and deal with the everworsening energy and environmental problem.Organic Rankine cycle(ORC)is a widely used low grade heat energy conversion technology with wide temperature range and moderate power.For the high efficiency and low cost of ORC,the experimental research of full-condition operation and neural network performance optimization of organic Rankine cycle with different working fluids were studied in this paper.The main research contents and innovative conclusions are as follows:1)Based on the ORC experimental platform,the basic operation characteristics and cycle performance of each component were researched,and the interaction law of components was obtained.The maximum isentropic efficiency and mechanical efficiency are 47.3% and 85.2%,respectively.The maximum isentropic efficiency of the scroll expander is 86%.The input heat varies from 22-44 k W,the maximum pressure difference of the system is 7.93 bar,and the maximum power generation is1.24 k W.The maximum thermal efficiency and net work output are 6.4% and 1.64 k W,respectively.2)Based on the neural network,the ORC prediction model was built,and the ORC performance prediction and optimization research was carried out.The BP-ORC prediction model was established according to the transient experimental data,and the accuracy of the model was analyzed.The influence of different operating parameters was discussed,and the optimal operating parameters were obtained with the maximum thermal efficiency and net output work as the optimization targets,and the constraint relationship of them was revealed.Found that increasing the pump outlet pressure and the expander inlet temperature or decreasing the expander outlet temperature can improve the thermal efficiency,and increasing the mass flow rate or matching the expander inlet and outlet temperatures can increase the net output work.The optimal thermal efficiency and net output work are 7.76% and 2.31 k W,respectively.3)The temperature slip characteristics of the mixture during phase transition can effectively decrease the irreversible loss due to the heat transfer temperature difference,and improve the matching between the cycle and the cold and heat sources.The experiment explores the influence of the distribution ratio of mixture on the operating characteristics,heat transfer performance and the overall performance of the cycle,and emphatically explores the relationship between heat exchanger pressure difference and the thermal efficiency.The results indicate that the thermal efficiency of the mixtures is slightly better,up to 7%.The heat exchanger pressure drop affects the system thermal efficiency seriously.The simulated thermal efficiency regardless of the pressure drop is up to 286.76% higher than the measured.Therefore,the influence of heat exchanger pressure drop should be paid attention to in the theoretical analysis.4)Based on a test platform,the flow and heat transfer characteristics of nanoorganic working fluid were studied.The phase,morphology and properties of ZnO nano-organic working fluid were studied.The change rules of light transmittance,viscosity and thermal conductivity were obtained.The heat transfer characteristics of nano-organic working fluid and pure working fluid were compared under different mass flow rates.The experimental results show that the prepared ZnO-R123 nanofluid has good stability,and the viscosity,thermal conductivity,heat transfer coefficient and pressure drop of the system are significantly improved with the addition of nanoparticles of ZnO.