Abstract The Afyon Geothermal Power Plant is modeled using the Multi-Layer Feed-Forward Artificial Neural Network. The 100 × 8 data set obtained from the real Binary Geothermal Power Plant is divided into two parts: 80 × 8 training data and 20 × 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 × 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 °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.

中文翻译：

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Afyon地热发电厂热经济建模与人工神经网络优化

摘要 Afyon 地热发电厂使用多层前馈人工神经网络建模。从真实二元地热发电厂得到的100×8数据集分为两部分：80×8训练数据和20×8测试数据。地热发电厂系统建模已在具有三个输入和五个输出的 Matlab 上进行了数值模拟。在基于人工神经网络的系统中，隐藏层有十个神经元，每个神经元中的传递函数采用对数sigmoid传递函数。输出层的神经元具有 purelin 传递函数。作为训练过程的结果，3.06 × 10E-2 均方误差值是从基于 ANN 的二元地热发电厂系统中获得的。研究的重点是二元地热发电厂的优化。基于人工神经网络的遗传算法方法用于此目的。介绍了从基于人工神经网络的二进制地热发电厂系统的输出中获得的结果。该工厂的地热水温度和质量流量为 110 °C 和 150 kg/s。工厂的能源和火用效率计算为 10.4% 和 29.7%。工厂发电的优化简单投资回收期和火用成本分别计算为 2.87 年和 0.0176 美元/千瓦时。工厂的能源和火用效率计算为 10.4% 和 29.7%。工厂发电的优化简单投资回收期和火用成本分别计算为 2.87 年和 0.0176 美元/千瓦时。工厂的能源和火用效率计算为 10.4% 和 29.7%。工厂发电的优化简单投资回收期和火用成本分别计算为 2.87 年和 0.0176 美元/千瓦时。