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Alternative approach in performance analysis of organic rankine cycle (ORC)
Author(s) -
Kılıç Bayram,
Arabacı Emre
Publication year - 2018
Publication title -
environmental progress and sustainable energy
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.495
H-Index - 66
eISSN - 1944-7450
pISSN - 1944-7442
DOI - 10.1002/ep.12901
Subject(s) - organic rankine cycle , subcooling , refrigerant , adaptive neuro fuzzy inference system , condenser (optics) , artificial neural network , process engineering , rankine cycle , superheating , environmental science , thermodynamics , computer science , engineering , fuzzy logic , heat exchanger , fuzzy control system , artificial intelligence , waste heat , physics , heat transfer , light source , power (physics) , optics
In this study, artificial neural networks (ANNs) and adaptive neuro‐fuzzy (ANFIS) have been used for performance analysis of organic rankine cycle (ORC) using refrigerants R123, R125, R227, R365mfc, SES36. It is well known that the steam generator temperature, condenser temperature, subcooling temperature, and superheating temperature affect the efficiency ratio of ORC. Therefore, efficiency ratio is forecasted depending on variable system parameters values. The results of ANN are compared with ANFIS in which the same data sets are used. Furthermore, new formulations derived from ANN for five refrigerants are presented for the determination of the efficiency ratio. The R 2 values obtained from the networks were 0.99917, 0.99670, 0.99870, 0.99928, and 0.99911 for the R123, R125, R227, R365mfc, SES36 respectively which is very satisfactory. © 2018 American Institute of Chemical Engineers Environ Prog, 38: 254–259, 2019