Determining the Exergy and Energy Efficiency of an Organic Rankine Cycle Using Artificial Neural Network Method

Abstract

This paper discusses energy and exergy examination of the organic Rankine cycle (ORC). Artificial neural networks (ANN) were also used to analyze ORC's exergy. R123 and SES36 were used as organic working fluids. At the turbine inlet temperature ranging from 110 degrees C to 170 degrees C, the energy efficiencies ranged from 17% to 22%, whereas the exergy efficiencies ranged from 11% to 12%, respectively. Notably, the evaporator temperature, condenser temperature, efficiency ratio, condenser capacity, turbine capacity, evaporator capacity, feed pump, mass flow, and working fluid affect the exergy efficiency of ORC. Use was made of 352 data for training and 110 data for the test. The results of the ANN were compared with actual exergy efficiency values, in which the same data group was used. Also, new equations were derived from ANN for two fluids for the calculation of the exergy efficiency. The coefficient of determination (R2) of the network is 0.99991 for the exergy efficiency estimation. This value is very satisfactory.