Artificial neural network modeling of geothermal district heating system thought exergy analysis

This paper deals with an artificial neural network (ANN) modeling to predict the exergy efficiency of geothermal district heating system under a broad range of operating conditions. As a case study, the Afyonkarahisar geothermal district heating system (AGDHS) in Turkey is considered. The average daily actual thermal data acquired from the AGDHS in the 2009–2010 heating season are collected and employed for exergy analysis. An ANN modeling is developed based on backpropagation learning algorithm for predicting the exergy efficiency of the system according to parameters of the system, namely the ambient temperature, flow rate and well head temperature. Then, the recorded and calculated data conducted in the AGDHS at different dates are used for training the network. The results showed that the network yields a maximum correlation coefficient with minimum coefficient of variance and root mean square values. The results confirmed that the ANN modeling can be applied successfully and can provide high accuracy and reliability for predicting the exergy performance of geothermal district heating systems.

► ANN has been modeled for predicting exergy efficiency a GDHS thought exergy analysis.
► The network yields a maximum correlation coefficient with minimum coefficient of variance and root mean square values.
► The ANN modeling can provide high accuracy and reliability for predicting the exergy efficiency of GDHSs.
► Thus, online monitoring system and the performance of GDHS can be implemented.