Performance Estimation of Organic Rankine Cycle by Using Soft Computing Technics

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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