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Vol.2 , No. 4, Publication Date: May 25, 2015, Page: 178-185
 application which predicts of factor refrigerating capacity in a mechanical compression refrigeration system was developed. Mechanical compression refrigeration cycle, the most common cooling cycle. Element that provides heat by the evaporation of the refrigerant evaporator at low pressure environment. The Network, which has three layers as input, output, and hidden layer, has four input and one output cells. Six cells were used in hidden layers. Which back propagation algorithm was used for training. Desired error value was achieved in ANN and, ANN was tested with both data used for training ANN and data not used. Resultant low relative error value of the test indicates the usability of ANNs in this area. Capacity of change in a refrigeration system can be studied by different proceeding. Changing condenser temperature also changes the capacity of the refrigeration system. In this study, a series of experiments were performed in order to determine the effects of changing cooling water flow rate (changing condenser temperature) in a mechanical heat pump experimental setup on the refrigerating capacity of the system. Performance values obtained were used for training Artificial neural network (ANN) whose structure was designed for this operation.&picture=http://www.aascit.org/aascit/decorators/img/journal/896.jpg)
| [1] | Bekir Cirak, Siirt University, Engineering and Architecture Faculty, Department of Mechanical Engineering, Siirt, Turkey. |
In this study, an artificial neural network (ANN) application which predicts of factor refrigerating capacity in a mechanical compression refrigeration system was developed. Mechanical compression refrigeration cycle, the most common cooling cycle. Element that provides heat by the evaporation of the refrigerant evaporator at low pressure environment. The Network, which has three layers as input, output, and hidden layer, has four input and one output cells. Six cells were used in hidden layers. Which back propagation algorithm was used for training. Desired error value was achieved in ANN and, ANN was tested with both data used for training ANN and data not used. Resultant low relative error value of the test indicates the usability of ANNs in this area. Capacity of change in a refrigeration system can be studied by different proceeding. Changing condenser temperature also changes the capacity of the refrigeration system. In this study, a series of experiments were performed in order to determine the effects of changing cooling water flow rate (changing condenser temperature) in a mechanical heat pump experimental setup on the refrigerating capacity of the system. Performance values obtained were used for training Artificial neural network (ANN) whose structure was designed for this operation.
Keywords
Artificial Neural Networks, Refrigeration, Condenser, Evaporator, Expansion
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