Performance Estimation of Organic Rankine Cycle by Using Soft Computing Technics

Year 2017, , 1 - 10, 21.08.2017

Tuğba Kovacı , Arzu Şencan Şahin , Erkan Dikmen Hasan Burak Şavklı

https://doi.org/10.24107/ijeas.297737

Abstract

In this study, the thermal efficiency
values of Organic Rankine cycle system were estimated depending on the
condenser temperature and the evaporator temperatures values by adaptive
network fuzzy interference system (ANFIS) and artificial neural networks system
(ANN). Organic Rankine cycle (ORC) fluids of R365-mfc and SES32 were chosen to
evaluate as the system fluid. The performance values of ANN and ANFIS models
are compared with actual values. The R² values are determined
between 0.97 and 0.99 for SES36 and R365-mfc, and this is satisfactory. Although
it was observed that both ANN and ANFIS models obtained a good statistical
prediction performance through coefficient of determination variance, the
accuracies of ANN predictions were usually imperceptible better than those of ANFIS predictions.

Keywords

Adaptive network fuzzy interference system, artificial neural networks system, Organic Rankine cycle, R365-mfc

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