Thermoeconomic modeling and artificial neural network optimization of Afyon geothermal power plant

The Afyon Geothermal Power Plant is modeled using the Multi-Layer Feed-Forward Artificial Neural Network. The 100 x 8 data set obtained from the real Binary Geothermal Power Plant is divided into two parts: 80 x 8 training data and 20 x 8 test data. Geothermal Power Plant system modeling has been performed numerically on Matlab with three inputs and five outputs. There are ten neurons in the hidden layer in the Artificial Neural Network-based system, and the logarithmic sigmoid transfer function is used as the transfer function in each neuron. The neurons in the output layer have the purelin transfer function. As a result of the training process, the 3.06 x 10E-2 mean square error value was obtained from the ANN-based Binary Geothermal Power Plant system. The main point of the study is the optimization of the binary geothermal power plant. The genetic algorithm method with Artificial Neural Network-based is used for this purpose. The results obtained from the outputs of the Artificial Neural Network-based Binary Geothermal Power Plant system are presented. The plant's geothermal water temperature and mass flow rates are 110 degrees C and 150 kg/s. Energy and exergy efficiencies of the plant are calculated as 10.4% and 29.7%. The optimized simple payback period and exergy cost of the electricity generated in the plant is calculated as 2.87 years and 0.0176 $/kWh, respectively. (C) 2020 Elsevier Ltd. All rights reserved.