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Artificial neural network modelling of parabolic trough types solar thermal power plant

Damla Erikgenoğlu, Oğuz Arslan


This study aims to determine the most appropriate design point for solar energy aided power plant system using the R-134a as a working fluid, using the Artificial neural network (ANN) method. A total of 900 different data, with varying fractions of solution and working parameters, were analysed utilising energy-exergy, power, and Net Present Value (NPV) analysis. In the training phase, 630 data were used. The remaining 270 data were reserved for testing. Levenberge Marguardt ( ), Pola- Ribiere Conjugate Gradient ( ), and Scaled Conjugate Gradient ( ), algorithms are used to find the best approximation in the network. According to the ANN results, the error rate of 0.013% was determined in an acceptable range for engineering applications. The best results were obtained in the training and testing stages in the -10 algorithm. The values of absolute change percentage , Mean Percentage Error , Coefficient of Variation  and Root Mean Square Error ( ) were determined as 0.9999, 0.5100, 0.3807, and 0.0031, respectively, for the output of  in training steps. When the obtained results are examined, the analysis results of the most suitable system are as follows; the energy efficiency 18.4%, exergy efficiency 23.6%, and generated power of the system 281.9 . Besides, the profitability value of the system has been determined as 1.31 million .

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