Alternative approach in performance analysis of organic rankine cycle (ORC)

Abstract

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 R2 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

Energetic and exergetic efficiency comparison of recuperative transcritical organic Rankine cycles under different pure and mixture fluids

“…Zhi et al 36 presented a TRC model that optimized and predicted thermal and exergy efficiencies by using an artificial neural network (ANN) for low‐grade heat recovery. Kılıç and Arabacı 37 presented an approach to predict the performance analysis of an ORC by using refrigerants R123, R125, R227, R365mfc, and SES36 by using an ANN and adaptive neuro‐fuzzy inference system. Luo et al 38 developed an approach by using ANNs to predict the working fluid properties of ORC.…”

Deep neural network approach to estimation of power production for an organic Rankine cycle system

“…Therefore, the efficiency ratio was forecast depending on the steam generator temperature, condenser temperature, subcooling temperature, and superheating temperature. The results of ANN and ANFIS are very satisfactory according to the R 2 values, which ranged from between 0.99670 and 0.99928 [28]. Both studies suggested the use of ANN and ANFIS.…”

“…Kılıç and Arabacı carried out a performance analysis of an ORC using R123, R125, R227, R365mfc, and SES36 as refrigerants. In the study, ANN and ANFIS methods were used to evaluate a prediction expression [28]. Palagi et al compared the performance predictions for a 20 kW ORC system for feed-forward, recurrent (RNN) and long short-term memory (LSTM) networks.…”

Determination of sensitivity and contribution ratios of parameters affecting Organic Rankine Cycle efficiency with multivariant optimization

“…Sistemdeki bileşenlerin enerji analizinde pompa işi (5), buharlaştırıcı ısı girdisi (6), türbin işi (7), yoğuşturucudan atılan ısı miktarı (8), için kullanılan denklemler aşağıda verilmiştir.…”

“…Yapay sinir ağlarının ORÇ ısıl veriminin tahmin edilmesinde başarılı sonuçlara ulaştığı tespit edilmiştir. Gerçek değerler ile yapay sinir ağlarından elde edilen sonuçlar karşılaştırıldığında tüm akışkanlar için 𝑅 2 değerinin yaklaşık %99 çıktığını tespit etmişlerdir [8].…”